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Q: Suppose that a bowl contains 100 chips: 30 are labelled 1, 20 are labelled 2, and 50 are labelled 3. The chips are thoroughly mixed, a chip is drawn, and let $X$ be the number on the chip.
1) Compute $P(X = x)$ for every real number $x$.
2) Suppose the first chip is replaced, a second chip is drawn, and let $Y$ be the number on the second chip. Compute $P(Y = y)$ for every real number $y$.
3) Let $W = X + Y$ and compute $P(W = w)$ for every real number $w$.

I have the solution for this:
I understand part 1 and 2, but not 3.
We define $W = X+Y$, and so this means that $W(w)=X(w)+Y(w), \forall w\in\mathbb{R^1}$
First my question is, why do we not consider $W(1)$? We have defined $X(1)$ and $Y(1)$, so why not $W(1)$?
Secondly, and probably the most important question is, how do they get $W(2)=0.12$? From the equation $W(2)=X(2)+Y(2)=0.2+0.2 = 0.4$. But they don't follow the equation. Why?
There's two questions I'm asking here.

A: There are five possibilities for the sum of the numbers on the two chips: $$ X+Y \in \{2,3,4,5,6\}$$

*

*If $X+Y=2$, then $(X,Y)$ must be $(1,1)$.

*If $X+Y=3$, then $(X,Y)$ can be $(1,2)$ or $(2,1)$.

*If $X+Y=4$, then $(X,Y)$ can be $(1,3)$, $(2,2)$, or $(3,1)$.

*If $X+Y=5$, then $(X,Y)$ can be $(2,3)$ or $(3,2)$.

*If $X+Y=6$, then $(X,Y)$ must be $(3,3)$.

The probability that $X+Y=2$ is the same as the probability that both $X=1$ and $Y=1$. Since the chip is replaced, $X=1$ and $Y=1$ are independent events, so we simply multiply their probabilities to find the probability that both occur:
$$P(X+Y=2) = P(X=1)P(Y=1) = 0.3^2 = 0.09$$
The probability that $X+Y=3$ is the same as the probability that both $X=1$ and $Y=2$, plus the probability that both $X=2$ and $Y=1$:
$$P(X+Y=3) = P(X=1)P(Y=2) + P(X=2)P(Y=1)= 0.3\cdot 0.2 \cdot 2= 0.12$$

The same reasoning applies for the other three cases, and we get the final answers of $0.09, 0.12, 0.34, 0.2$, and $0.25$.
As an extra check, when you finish a problem like this, make sure that all of the probabilities add up to $1$ (since there are no other possibilities for the value of $X+Y$, this should be the case:
$$0.09 + 0.12 + 0.34 + 0.2 + 0.25 = 1$$

A: $W = X + Y$ is a random variable that is computed as
$P(W=w) = \Sigma_{x=1}^3 P(X=x)\times P(Y=w-x) $
Hence, it should now be clear that $P(W=1) = P(X=1)\times P(Y=0)$. Since $P(Y=0)=0, P(W=1)=0$
Similarly other values can be calculated.
eg $$P(W=3) = P(X=1)\times P(Y=2) + P(X=2)\times P(X=1)$$ which gives the required answer.


text:
Fortune Recognizes The Arbor Company As One of the Best Workplaces in Aging Services
The Recognition Demonstrates The Arbor Company's Ability To Build a Positive Work Culture in an Industry Plagued by Attrition
ATLANTA, Oct. 26, 2021 /PRNewswire/ -- The Arbor Company, a premier senior living operator with 46 locations across the country, is one of the best places to work when it comes to aging services. Today, The Arbor Company is proud to announce its ranking on Fortune's "25 Best Large Workplaces in Aging Services: Senior Housing and Care" list. The nod demonstrates the senior living operator's ability to build a positive work environment in an industry that has been historically plagued with recruiting difficulties and high employee attrition.
The Arbor Company operates more than 40 senior living communities across 11 states, each with various senior living options. For more information, visit https://www.arborcompany.com/
The COVID-19 pandemic and the current labor crisis has made it even more challenging when it comes to staffing in the senior living industry. Despite these conditions, The Arbor Company has managed to maintain a great company culture through innovative programs and initiatives that allow the company to provide the best care for both its employees and its residents.
"It's important for residents to live in a place where people are happy to come to work, and where they're treated fairly and paid well," said Judd Harper, The Arbor Company President. "If the staff is taken care of, the residents will be taken care of."
One way The Arbor Company takes care of its staff is by rewarding them for their hard work and loyalty. The company plans to spend close to $500,000 on bonuses for those reaching five-, 10- and 15-year anniversaries as a way of saying thank you to loyal employees. Parties are planned to celebrate those employees receiving the bonuses. The company also has an online reward gateway called "The Grove" that allows employees to earn on-the-spot recognition and "Arbor Bucks" for monetary gifts.
The Arbor Company partnered with Activated Insights for its "Great Places to Work" surveys because the leadership team wanted to hear from employees and gain insight into how they were feeling about their jobs. Fortune tabulated that data in order to determine which companies made it onto the lists.
In the Fortune survey, 87% of employees said the company made them feel like they made a difference. Eighty-eight percent of staff stated that the company made them feel welcome when they joined the team. The majority also said their leadership team members and colleagues were team-oriented, helpful, supportive, caring, friendly and fair and that they respected each other. Great benefits and opportunities to advance made them feel valued.
"It's really about creating a sense of culture that has a strong foundation in empathy and recognizing the differences in each other, respecting those differences and building upon them," said Harper.
To maintain a healthy and diverse work culture, employees and department heads across the company are required to participate in unconscious bias training. The Arbor Company partnered with Eagle's Flight for diversity, equity and inclusion (DEI) training as a direct response to the Black Lives Matter movement. The company was also one of the first to require the COVID-19 vaccine in an effort to keep staff and residents healthy at its facilities during the pandemic.
"We're listed alongside some wonderful companies in senior living that have succeeded in creating great places to work, and we're honored to be one of them," said Harper.
ABOUT THE ARBOR COMPANY
When The Arbor Company started more than 30 years ago, senior living looked very different than it does today. But the founders wanted to provide the best service to seniors by giving them the options that best suited their needs. Today, The Arbor Company operates more than 40 senior living communities across 11 states, each with various senior living options. For more information, visit https://www.arborcompany.com/
View original content to download multimedia:https://www.prnewswire.com/news-releases/fortune-recognizes-the-arbor-company-as-one-of-the-best-workplaces-in-aging-services-301408518.html
SOURCE The Arbor Company

redpajama_set_name: RedPajamaCommonCrawl


Air Force Secretary Heather Wilson has a new role: chairman of the board.
Now that Congress has largely put the services in charge of buying weapons, and removed much of the purview of the Office of the Secretary of Defense, Wilson has put in place a new governing body to oversee hundreds of billions of dollars in purchases. The panel will meet for the first time next week.
That board is made up of Wilson; Gen. David Goldfein, the Air Force chief of staff; Undersecretary Matt Donovan; and Will Roper, the service's assistant secretary for acquisition, technology and logistics. The panel will meet three times a year and receive briefings from a dozen or so program executive officers. Each of those senior acquisition officials oversees numerous projects.
Right now, the meetings are dubbed "Air Force Acquisition Day," which Wilson acknowledges isn't the best name. "It's more like a corporate board meeting than it is a small business meeting," she said.
In all, Wilson is now responsible for 465 major programs. Her goal is simple: get capabilities to airmen faster and cheaper.
"I want them spending more time managing the programs than managing the Pentagon," Wilson said.
Buying new software is among the items Air Force leaders plan to discuss at next week's meeting.
Another possible agenda item: "how we innovate in sustainment, " acquisition head Roper said at a briefing last month.
Speaking more generally about her plans for changing the way the Air Force buys weapons, Wilson said to expect more prototyping and experimentation, akin to work already being done by Pratt & Whitney and General Electric to build new jet engines. The goal of the project is to increase fuel efficiency by 25 percent and thrust by 10 percent. These new engines could one day replace existing fighter jet engines.
"Instead of trying to do an engineering design and sit down with modeling and simulation and all of that for two or three years, which is what the normal acquisition process would do, we contracted with two of the best engine companies in America and said: 'See what you can build,'" Wilson said.
To put the potential savings in perspective, the Air Force plans to spend about $5 billion on fuel this year.


Are you Christopher Scott? If so, email [email protected] to claim this critic page.
Battleborn carves itself a deserving spot on people's lists. Its endearing roster, wild set of abilities, great variety of modes, progression systems, and the promise of free content in the form of new heroes, modes, and maps have left me addicted and turned me into a MOBA fan, a feat I didn't think was possible.
Despite my reservations, I can't wait to jump back into Black Desert Online. Its tight combat, numerous tradecrafts, and extensive setting have left me hooked. If you're craving something new over the well-traveled roads of many other MMOs, you owe it to yourself to give Black Desert a deeper look. But if dungeons, raids, and the treadmill of increasingly impressive loot are what you're looking for, then you'll have to look elsewhere.
The Nemesis System is an achievement, one that overshadows any faults Shadow of Mordor otherwise has through sheer fun factor, and I can't wait to see its influence spread.
If you loved Age of Wonders 3, the Golden Realms expansion is a no-brainer, especially for its low asking price. Some of the what's been added doesn't completely come together, but the Halfing race, Wild Magic specialization and new objectives make it a more exciting and purposeful package.
Titanfall is a shot of adrenaline. It constantly presents you with awesome scenarios thanks to its speed, freedom of movement and accessible content. I can't count the times I whooped and hollered in delight. Win or lose, I was having fun. That's not something I can easily say for its competition. But it also feels a little bare bones, missing some content we normally expect from such games. Regardless, if you're looking for your next competitive shooter, Titanfall deserves your attention.
South Park: The Stick of Truth may as well be an achievement. The soul of the show is captured perfectly with its visuals, consistently funny writing and ridiculous scenarios. It also happens to be damn fun to play. After the 13 hours it took me to complete it, all I've been able to think about since is going back to it to find every lost collectible and missed achievement. If you've ever enjoyed South Park, then you absolutely need to own this game.
It's a vibrant shooter with finely tuned characters that play very well off each other, providing for an almost party-like experience that should be fun for almost anyone. This new direction for the series is mostly a welcome success, but fans of traditional tower defense games or those from Dungeon Defenders and Orcs Must Die! may bemoan the lack of strong strategic placement and plotting.


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Although the wig scene has exploded in the last decade, wigs have been a part of popular culture for a VERY long time. This post is dedicated to celebrating the beautiful black-glam 90's girlies whose wig choices changed the game in one way or another!
Brandy Norwood
Miss Norwood absolutely HAD to be on our list of legendary wigs from the 90s. In 2019, it emerged that Brandy's iconic super micro braids were a wig style, a fact that blew the collective minds of women on Black Twitter for days. There is evidence that Brandy did wear actual braids but she switched them up with wigs from time to time as a protective hairstyle. Now, braided wigs are fairly common and done pretty well these days, but using this option almost 30 years ago, and pulling it off is queen behaviour.
Image | Brandy Rocking a Braid Wig
Janet Jackson has had huge success in both music and film. But one of the most amazing things about her? She could transform and change her look countless times, whilst maintaining her confident dedication to self-expression. From her poetic justice braids to golden curly ringlets or bone straight and windswept do's, Janet wore each style well; her hair extensions and wigs were always casual, powerful, and sexy!
Image | Janet at the MET Centre in 1990
We think Halle Berry's ghetto fab blonde up-do from B.A.P.S deserves an honourable mention. Berry went all out with her portrayal of Nisi in the film, with hair that looked fresh off of a Bronner Brothers Hair Show. Not only did she flex her comedic acting muscle (not many people pegged her for someone with humour), but Berry also gave urban hair culture a platform on the main stage (thank you Berry!).
People still can't get enough of her fabulous looks; you'll most definitely see a few people paying homage this Halloween!
Image | Halle Berry as Nisi in B.A.P.S
Vivica Fox
When we think of Vivica, we think of warm chestnut tones, gorgeous layers, and a silky smooth curl. Vivica is a total bombshell, then and now! Her hair was notorious for being big and loud. Her face was always perfectly framed with subtle highlights and balayage that were works of art. This queen is famous for her acting chops and is one of the leading ladies in Hollywood that truly showed us just how good our hair could look, despite the lack of great stylists for black women during the 90s. For that, we must always stan!
Image | Vivica in a Chestnut-Toned and Layered Wig
The topic of hair styling for the black Hollywood elite is interesting to say the least. Good hair styling was hard to come by for black women at the time, so we're amazed at how they found a way to add personality and creativity to their styles, gracing our television screens throughout the 90s! But today? It's not that hard to get your hands on an expert hairstylist - check out our previous blog for some remarkable wig stylists you can get in touch with today!


Stillwater Property Management, Stillwater Property Managers, Stillwater, MN Property Management Companies.
Many companies offering property management services in Stillwater are fairly new to the market. Signing a management contract with Real-Time Leasing is a way to leverage over a decade of experience in rental management. To ensure we keep owners' best interest in mind, our in-house property manager's work solely for Real-Time Leasing. Our unique check-and-balance structure ensures that we don't approve an applicant for the wrong reason and allows us to uphold our commitment to the property owner while adhering to fair housing standards.


Such attention to details, such great graphics. Good work, guys!
Thanks for another sneak-peak, Zantai. The hype is building!
Wow i am really excited! Those screenshot look sweet!
but circumstances are about to change[/B]. You will have to find out for yourself when the expansion is released later this year.
Ooh yeah put another hint in for use to speculate. Either it is the presence of Theodin Marcel in that area or something else, that circumstances has changed. Already my brain is in overdrive for the lore. If it going any faster it will exceed the speed of light and go into a different dimension.
To me it looks like a battleground after or before a fire swept through the city.
You have evidence of a tank in the third picture, some railroad type system in the first. Most likely the Candle and Steelcap District were hit hardest with fire and gunpowder usage. Then you can add the aetherials coming in with explosive creatures while both sides are using spells, guns and whatever else on top of that.
The level of destruction looks fine to me - if Malmouth is bigger then Port Valbury (which I'll assume it is) then it makes even more sense having a larger populace with the possibility of a greater military/policing presence.
Don't forget that old Arkovia is in ruins, due war among the noble houses.
the details of this wreckage look amazing, my mouse arm is itching!
is there some place that can offer me some cryogenic sleep so the next thing I see when I wake up is the expansion?
Afraid not. You're just going to have to freeze yourself the old fashioned way. Don't forget to tell a buddy to wake you up, though.
Malmouth looks great, @Crate! Really looking forward to this expac.


Applique, Hippie Boho Peace Sign Patch for Jeans, bags, shirts or anything that you feel like blinging up. These patches are washable and can be sewn on or glued on using fabric glue. We can print any name in any color. These are great to add your signature style to anything that you wear.
Perfect to accessorize tee shirts, backpacks, boots, jeans, hats. Each patch is hand embroidered, painted and glazed to protect the color over multiple washes.
If you have your own artwork that you would like to have made into a patch, just convo me and we can work out the details.


The Vineyard Hotel is one of my favourite hotels in the Southern Suburbs. Located in the leafy suburb of Newlands, the hotel is popular with the both locals, as well as international visitors. One of the major draw cards of the hotel, is the wonderful culinary offering and more especially the Myoga 7 Course Tasting Menu!
One of my favourite dining experiences to date, has to be the tasting menu at Myoga. The restaurant recently underwent a revamp and we were delighted to be invited back to enjoy the 7-course tasting. The sophisticated space is now lighter and brighter, making it an ideal lunchtime venue as well as a special place for dinner.
Myoga offers a tasting degustation menu, which is different from the tasting menus found in other restaurants because it offers so many choices.
Degustation ~ a culinary term meaning a careful, appreciative tasting of various foods and focusing on the gustatory system, the senses, high culinary art and good company. Dégustation is more likely to involve sampling small portions of all of a chef's signature dishes in one sitting.
The Myoga 7 course tasting menu, created by award winning chef Mike Bassett, offers no less than five (sometimes six!) options for each course.
In addition to the 7 course menu, you will find a unique dish of the chef's creation – a palate "tickler". To delight your palate even further, there is a clever and delicious surprise at the beginning and the end of the meal. I will spoil the surprise for you – you will simply have to experience it for yourself!
The innovation and creativity, which Mike Bassett is renowned for is evident in every one of the dishes served! One of the dishes, which stood out for both Grant and I was a savoury Oatmeal porridge. What an amazing taste journey, from such an unassuming dish!
The menu will truly delight all your sense and take you on a culinary adventure! The Black Salted Squid, with black aioli, anchovy lime condensed milk, white almond gazpacho sphere, Iberico ham and celery scented potato, was pure sophistication on a plate.
One of my favourite dishes was the All White Scallop! Served with macadamia buttered root vegetable puree, refried white beans, horseradish cream and a orange gastrique – it was one of the most amazing scallop dishes I have ever eaten!
Every dish is beautifully presented – an artwork on a plate. The "Granny Smith" desert above is no exception! Sour cream apple cake, served with caramel créme pâtissière, roasted apple ice cream, apple gel and almond crumble was a sublime ending to an extraordinary meal!
As well as experiencing amazing new taste sensations, we enjoyed a great selection of excellently paired wines. Some of which neither of us had tasted before.
I can go on and on about the Myoga 7 Course Tasting Menu but it is something you should definitely experience for yourself! Just when you thought the was nothing new under the sun, you taste something which truly surprises you!
The Myoga 7 Course Tasting Menu including wine pairing is R620 per person or R355 without wine. To book please contact Restaurant Reservations Tel: +27 21 657 4545, Fax: +27 21 657 4543, via email: eat@myoga.co.za or visit the Myoga Website .


text:
Ab initio simulations of the structure of thin water layers on defective anatase TiO2(101) surfaces
Ulrich Johannes Aschauer, Antonio Tilocca, Annabella Selloni
Titania-water interfaces are important in various fields of science, from geophysics to photocatalysis and biochemistry. Here, we use ab initio molecular dynamics simulations to investigate the structure of thin water overlayers on the (101) surface of TiO2 anatase in the presence of oxidizing defects. For comparison, results of our previous studies of water layers on defect-free and reduced anatase (101) are also reviewed. On the stoichiometric defect-free surface-ordered structures are formed at one and two monolayer coverage, and the order in the first bilayer is largely maintained when a third water layer is adsorbed. By contrast, the vertical and in-plane ordering of the water layers is strongly perturbed in the presence of both oxidizing and reducing defects. As a result, the structure of the water layer is much more diffuse under these conditions, and frequent exchanges of water molecules between different layers are observed.
International Journal of Quantum Chemistry
https://doi.org/10.1002/qua.24918
ab initio molecular dynamics
solid interface
10.1002/qua.24918
Dive into the research topics of 'Ab initio simulations of the structure of thin water layers on defective anatase TiO2(101) surfaces'. Together they form a unique fingerprint.
anatase Physics & Astronomy 100%
Titanium Dioxide Chemical Compounds 76%
Simulation Chemical Compounds 58%
water Physics & Astronomy 52%
Photocatalysis Chemical Compounds 43%
Biochemistry Chemical Compounds 42%
simulation Physics & Astronomy 35%
Monolayer Chemical Compounds 34%
Aschauer, U. J., Tilocca, A., & Selloni, A. (2015). Ab initio simulations of the structure of thin water layers on defective anatase TiO2(101) surfaces. International Journal of Quantum Chemistry, 115(18), 1250-1257. https://doi.org/10.1002/qua.24918
Aschauer, Ulrich Johannes ; Tilocca, Antonio ; Selloni, Annabella. / Ab initio simulations of the structure of thin water layers on defective anatase TiO2(101) surfaces. In: International Journal of Quantum Chemistry. 2015 ; Vol. 115, No. 18. pp. 1250-1257.
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title = "Ab initio simulations of the structure of thin water layers on defective anatase TiO2(101) surfaces",
abstract = "Titania-water interfaces are important in various fields of science, from geophysics to photocatalysis and biochemistry. Here, we use ab initio molecular dynamics simulations to investigate the structure of thin water overlayers on the (101) surface of TiO2 anatase in the presence of oxidizing defects. For comparison, results of our previous studies of water layers on defect-free and reduced anatase (101) are also reviewed. On the stoichiometric defect-free surface-ordered structures are formed at one and two monolayer coverage, and the order in the first bilayer is largely maintained when a third water layer is adsorbed. By contrast, the vertical and in-plane ordering of the water layers is strongly perturbed in the presence of both oxidizing and reducing defects. As a result, the structure of the water layer is much more diffuse under these conditions, and frequent exchanges of water molecules between different layers are observed.",
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Aschauer, UJ, Tilocca, A & Selloni, A 2015, 'Ab initio simulations of the structure of thin water layers on defective anatase TiO2(101) surfaces', International Journal of Quantum Chemistry, vol. 115, no. 18, pp. 1250-1257. https://doi.org/10.1002/qua.24918
Ab initio simulations of the structure of thin water layers on defective anatase TiO2(101) surfaces. / Aschauer, Ulrich Johannes; Tilocca, Antonio; Selloni, Annabella.
In: International Journal of Quantum Chemistry, Vol. 115, No. 18, 01.09.2015, p. 1250-1257.
T1 - Ab initio simulations of the structure of thin water layers on defective anatase TiO2(101) surfaces
AU - Aschauer, Ulrich Johannes
AU - Tilocca, Antonio
AU - Selloni, Annabella
N2 - Titania-water interfaces are important in various fields of science, from geophysics to photocatalysis and biochemistry. Here, we use ab initio molecular dynamics simulations to investigate the structure of thin water overlayers on the (101) surface of TiO2 anatase in the presence of oxidizing defects. For comparison, results of our previous studies of water layers on defect-free and reduced anatase (101) are also reviewed. On the stoichiometric defect-free surface-ordered structures are formed at one and two monolayer coverage, and the order in the first bilayer is largely maintained when a third water layer is adsorbed. By contrast, the vertical and in-plane ordering of the water layers is strongly perturbed in the presence of both oxidizing and reducing defects. As a result, the structure of the water layer is much more diffuse under these conditions, and frequent exchanges of water molecules between different layers are observed.
AB - Titania-water interfaces are important in various fields of science, from geophysics to photocatalysis and biochemistry. Here, we use ab initio molecular dynamics simulations to investigate the structure of thin water overlayers on the (101) surface of TiO2 anatase in the presence of oxidizing defects. For comparison, results of our previous studies of water layers on defect-free and reduced anatase (101) are also reviewed. On the stoichiometric defect-free surface-ordered structures are formed at one and two monolayer coverage, and the order in the first bilayer is largely maintained when a third water layer is adsorbed. By contrast, the vertical and in-plane ordering of the water layers is strongly perturbed in the presence of both oxidizing and reducing defects. As a result, the structure of the water layer is much more diffuse under these conditions, and frequent exchanges of water molecules between different layers are observed.
KW - ab initio molecular dynamics
KW - photocatalysis
KW - solid interface
KW - titania
KW - water
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DO - 10.1002/qua.24918
JO - International Journal of Quantum Chemistry
JF - International Journal of Quantum Chemistry
Aschauer UJ, Tilocca A, Selloni A. Ab initio simulations of the structure of thin water layers on defective anatase TiO2(101) surfaces. International Journal of Quantum Chemistry. 2015 Sep 1;115(18):1250-1257. doi: 10.1002/qua.24918


A three-floor art-deco style complex housing the Buriel Clay Theater, the Sargent Johnson Gallery, several workshops and classrooms, and visual arts studios. The center's programs reflect diversity and highlight African American art in its traditional and contemporary forms.
* Encourage the interaction of various components of African American Art and Culture (i.e. spiritual, educational, and secular).
We desire dialogue, support and interaction with the surrounding community as well as the broader San Francisco community with emphasis on positive intra-generational and inter-generational activities.


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Google has launched a new venture capital program focused on AI
Apple could be working on a chip dedicated to AI services
Published on 27 May 2017, 13:57 Asia/Kolkata
Mudit Mohilay
Google is a prolific investor. You may not hear about it, but Google keeps investing in all sorts of companies from time to time. Along the same, the company is being said to have launched a new venture capital program led by Alphabet's VP of engineering Anna Patterson.
Apparently, instead of your regular investors, this particular VC firm will have a bunch of Alphabet engineers, who will be part of the new fund on a rotational basis. Investments made through this fund will focus upon Artificial Intelligence.
A fund dedicated to AI and independent from Google Ventures sounds surprising, but isn't really. The company has time and again talked of a future that has AI at its center. Considering this, it only stands to reason that Google would want to keep a tab on the developments in AI. And what better way to get cutting edge companies come to you, than creating a fund for them. This way, Google can also acquire stakes in the most promising of these firms.
Google's Interest in AI goes way, way back of course, Of late, that interest has sen the company focus upon innovations like the Tensor units. Indeed, the company even announced that it was making a full stack of Tensor processing units free for researchers to use.
The new fund will invest anywhere between $1 million to $1- million into these startups, although what isn't known, is the amount that the VC fund is getting to start off with.
A bibliophile and a business enthusiast.
mudit.m@thetechportal.in


The Hippo : New Hampshire's Weekly : Algae triggering ALS?
William Gilmore, 60, of Rochester was a self-employed carpenter for most of his life. Gilmore built gazebos and decks and rebuilt kitchens. He was also an avid kayaker, scuba diver, surfer, fisher and bicyclist.
"I played just as hard as I worked hard," Gilmore said.
But, in 2011 he started to notice that his pneumatic tools and saws were getting too heavy for him — a 6'2", 210-lb., well-built man. His right arm had become weak.
At first, Gilmore says, doctors misdiagnosed the problem as something that would require time and physical therapy to overcome. But instead of getting better, his condition worsened. When it began to affect his left side as well in 2014, he went to Brigham and Women's Hospital in Boston for second opinion. There, he was diagnosed with a form of amyotrophic lateral sclerosis, or ALS, that he says is different from the fast-acting form that killed Lou Gehrig.
"It's a slow progression and, for the most part, it stays in the upper body," Gilmore said.
Today, his wife helps him do basic things.
"My arms don't work. I can't lift anything, I can't hold a pen, I can't feed myself, can't wash myself. It's a pretty nasty setup," Gilmore said.
He says by 2014, he wouldn't pick up a full cup of water.
"I couldn't trust my hands that I wasn't going to let it fall," Gilmore said.
And he's constantly reminded of his loss by the many relics of his bygone days as an active outdoorsman.
"All those things, my kayak, my dive gear, my surfboards, my bicycles, I can't use any of it anymore," Gilmore said.
Scientists are finding a mounting body of evidence that Gilmore's years living and working near coastline and shoreline may be at least one key factor that led to his disease. That's because several lakes and ponds Gilmore and others have interacted with are often host to cyanobacteria, also known as blue-green algae. The cyanobacteria, in turn, produce a toxin known as BMAA, which recent studies have shown correlates with high rates of ALS when ingested.
Dr. Elijah Stommel, a neurologist at Dartmouth-Hitchcock Medical Center who works with ALS patients, is one of the first scientists to discover a similar correlation in New Hampshire and the broader New England region.
What they saw was surprising — clusters of ALS patients around New Hampshire lakes.
At first, they had no idea why this clustering was taking place, but as they looked closer they noted that clustering was taking place around specific water bodies known to have significant cyanobacterial blooms.
"We've since found BMAA in some of these water bodies, so I think there's a pretty strong link. It doesn't prove anything," Stommel said.
He and his team identified patients living in those clusters who had never swum in nor eaten any fish from the lake they lived near. This led them to investigate the possibility that patients had been inhaling cyanobacteria from the air, something that's possible during its blooming season.
"We set up aerosol collectors, and we've actually been able to find cyanobacteria in the filters of these aerosol collectors over a relatively short time, just a few hours," Stommel said.
They also detected traces of cyanobacteria in patients' lungs.
Moving forward, Stommel wants to map out patients around Lake Erie, which has been known to suffer severe cyanobacterial blooms, in an effort to get closer to understanding the risk factors involved.
"We're trying to collaborate with the Cleveland Clinic. They have a big database of ALS patients there, and we're going to look retrospectively at the patients in that area hopefully and see if there are high rates of ALS there or clusterings of ALS and better define some of these risk factors," Stommel said.
Still, very little is known about the causes of neurodegenerative diseases like ALS and Alzheimer's. But research papers related to the environmental links to this disease and others have begun to snowball recently, thanks in part to Stommel's work.
Just this past January, a study was published showing vervet monkeys who ingested a lifetime's worth of BMAA grew large "tangles" in the brain consistent with those found in Alzheimer's patients in a shockingly brief amount of time.
And Stommel said a scientist in France has connected a cluster of ALS patients near Montpellier with oysters and mussels eaten by patients. The shellfish originated in Thau lagoon, a cyanobacterial hotbed.
Cyanobacteria has long been known for its toxins — it produces many, some of which are deadly to humans — but it wasn't fingered as the source of BMAA until the 1970s.
Prior to that, Army doctors in the 1940s and '50s had identified BMAA in cycad seeds and bat meat as the cause of the now-famous ALS-like outbreak on the island of Guam during that time.
But there's still plenty scientists don't yet understand. Stommel says there may be genetic precursors and it may require a combination of factors not yet understood for a person to contract the neurological disease.
Another thing came out of the monkey tests that may prove helpful: a possible treatment. A batch of vervets that ate the BMAA were also given an amino acid supplement called L-serine. Those monkeys weren't in nearly as bad shape as those that didn't receive the supplements. Human trials have already begun for Alzheimer's patients and the preliminary results are expected by the end of the year.
Which is good news, since cyanobacteria — the more than 2-billion-year-old algae scientists determined last fall lent our planet's atmosphere its first breeze of oxygen — cannot be easily avoided.
"These bacteria are really found everywhere. They're really ubiquitous," Stommel said.


The top specced electric cross bike in the SDURO Cross series, the Haibike SDURO Cross 9.0 Womens Electric Cross Bike is a machine that is just as happy off-road as it is on road, with the added advantage of its slimmer profile tyres being quicker on road than MTB tyres. Whether a cyclocross rider looking for a training machine or someone who just loves mixed road / XC riding this should be top of your list!
Do you enjoy riding a mix of country lanes and off-road sections? This is a fun weekend machine for those rides. You could be a commuter or someone who needs to do things in town regularly who encounters a few slippery / loose sections on your route so can't go for a pure road machine. Perhaps most importantly this is a great training machine for the cyclocross competition rider who is after a bit of variety in their training.
With its MTB-sized 27.5 inch wheels the Haibike SDURO Cross 9.0 Women's electric cross bike is great for bumpy routes yet with the lower profile Schwalbe Super Moto X it has a lower rolling resistance, allowing for higher average speeds on metalled surfaces.
Handling the rougher sections of your riding routes with ease is the SR Suntour Mobie E45 LOR front shocks with lockouts for different terrains - be they rough, hard, uphill or downhill.
The machine has a Bosch Performance CX drive system that supports an 11-speed Shimano Deore XT M8000 drivetrain. This runs over a huge 46 tooth granny gear so fear of even the nastiest of hills will become just a memory. Stopping power is amply provided with the Magura MT4 hydraulic disc brakes that use 180mm rotors.


text:
Toshiba's Eco-Friendly Office Copier Prints Erasable Documents
Finding a greener future
Jaymi Heimbuch JaymiHeimbuch
Image via CrunchGear
A new copy machine by Toshiba hopes to be the next big thing for paper-conscious offices. It prints documents that can be erased instantly by heating the pages, and the paper can be used up to five times before needing to be recycled. The toner is heat-sensitive, and loses color when warmed up. But what happens if you leave your important documents in the hot car?According to CrunchGear, the printer with its erasable paper can result in a "60% cut in carbon dioxide emissions, when compared with using four sheets of paper. The copier can make 20 to 30 copies a minute and will cost about 20-30% more than conventional copiers with a similar performance, according to a report in The Nikkei (Japan's biggest business daily)." The company hopes to commercialize the copy machines sometime during the next fiscal year.
It reminds us a little of a giant version of the PrePeat.
The idea is very intriguing -- at least at first. After all, isn't it perfect that an office could reuse paper up to five times? While it sounds like a great way to curb the consumption of paper by offices, there are a few issues that shake the "green" label for the machine. For instance, at what rate will paper get heated and reused, rather than saved (since the purpose of a copy machine is to have hardcopies of a document on hand), and does the energy that goes into heating the paper eat into any carbon savings by minimizing paper use? And what chemicals are used in heat-sensitive ink -- it might be far less green than using soy-based inks.
We aren't convinced that this is necessarily a green solution all around, and it underscores the fact that the greenest thing to do with copiers is just not use them. Avoiding printing in the first place is more practical than investing in this new machine.
Follow Jaymi on Twitter for more stories like this
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Pushing commercial truck efficiency to new levels on both sides of the pond.
Over 95 percent of all commercial trucks are powered by diesel engines, and these new trucks are more efficient than ever. Since 2014, the U.S. has been phasing in Phase 1 of its first ever rules establishing efficiency requirements and greenhouse gas emissions (GHG) limits for new trucks. According to 2016 National Emissions Inventory information, transportation sources account for 28 percent of all GHG emissions by sector. Of the transportation sector, medium and heavy-duty trucks account for 23 percent of all GHG emissions.
The U.S. standards require CO2 and fuel consumption reductions based on vehicle type, ranging from 6–23 percent compared to a model year 2010 baseline. Today in 2018, Phase 2 requirements are in place - extending through 2027, further lowering levels of allowable CO2 emissions and fuel consumption by 16-19 percent for vocational vehicles and up to 30 percent for tractor trailers. Manufacturers having solidly achieve the Phase 1 requirements are bringing new innovation in fuel efficiency to commercial vehicles as part of the compliance strategy.
In Europe, according to 2014 data, road transport is responsible for over 20 percent of the EU's total CO2 emissions: cars and vans account for about 15 percent and heavy-duty vehicles (lorries, buses and coaches) for about 5 percent. CO2 emissions from heavy-duty vehicles have grown constantly between 1990 and 2010, mainly as a result of an increase in road freight traffic.
The European Commission (EC) climate goal of reducing greenhouse gas emission from transports by 30 percent by 2030 (compared to 2005 levels) is currently under discussion. The Commission's two-step approach announced in May 2018 in Brussels proposed CO2 reduction targets for 2025 and 2030 for heavy-duty commercial trucks. Truck manufacturing representatives at the European Automobile Manufacturers Association ACEA expressed concern due to the close proximity of the first milestone (2025) to the current day business cycle and produce planning, which is well underway. The truck manufacturing industry in the EU also supports the proposal to validate the indicative 2030 target at a later point, as this would allow the latest fuel efficiency technologies available at that point in time to be taken into account. Trucks move more than 14 billion tonnes of goods per year. They deliver more than 70 percent of all land-based freight in Europe, or some 90 percent of the total value of goods.
Whether in the U.S. or EU, reducing fuel consumption is a strategy that everyone can benefit from. Manufacturer innovations in fuel savings are such now that fleets that have not upgraded their vehicle fleets run the risk of reduced competitiveness with those operating a newer generation fleet. To illustrate the difference, Daimler Trucks organized a controlled fleet fuel economy test and demonstrated an 8 percent improvement in fuel efficiency over a 2016 model in a carefully controlled side by side verified test. Over the course of driving 120,000 miles a year, an 8 percent reduction in fuel consumption could translate into savings of 1200 gallons of diesel or $4200 at $USD 3.50/gallon. Whether in euros, pounds or dollars, the savings from the newest generation diesel commercial vehicles are real!


DNA microarrays, which determine the expression levels of tens of thousands of genes from a sample, are an important research tool. However, the volume of data they produce can be an obstacle to interpretation of the results. Clustering the genes on the basis of similarity of their expression profiles can simplify the data, and potentially provides an important source of biological inference, but these methods have not been tested systematically on datasets from complex human tissues. In this paper, four clustering methods, CRC, k-means, ISA and memISA, are used upon three brain expression datasets. The results are compared on speed, gene coverage and GO enrichment. The effects of combining the clusters produced by each method are also assessed.
k-means outperforms the other methods, with 100% gene coverage and GO enrichments only slightly exceeded by memISA and ISA. Those two methods produce greater GO enrichments on the datasets used, but at the cost of much lower gene coverage, fewer clusters produced, and speed. The clusters they find are largely different to those produced by k-means. Combining clusters produced by k-means and memISA or ISA leads to increased GO enrichment and number of clusters produced (compared to k-means alone), without negatively impacting gene coverage. memISA can also find potentially disease-related clusters. In two independent dorsolateral prefrontal cortex datasets, it finds three overlapping clusters that are either enriched for genes associated with schizophrenia, genes differentially expressed in schizophrenia, or both. Two of these clusters are enriched for genes of the MAP kinase pathway, suggesting a possible role for this pathway in the aetiology of schizophrenia.
Considered alone, k-means clustering is the most effective of the four methods on typical microarray brain expression datasets. However, memISA and ISA can add extra high-quality clusters to the set produced by k-means, so combining these three methods is the method of choice.
Clustering genes according to their expression profiles is an important step in interpreting data from microarray studies. Clustering can help summarise datasets, reducing tens of thousands of genes to a much smaller number of clusters. It can aid understanding of systemic effects; looking for a small change in expression between disease states across many genes in a cluster could be a better strategy for finding the causes of complex, polygenic disorders than looking for large changes in single genes. Clustering can also help predict gene function, as coexpressed genes are more likely to have similar functions than non-coexpressed genes.
There are many clustering methods for microarray expression data currently available. However, there are few comparisons of these methods, making it hard for researchers to make a rational choice between them. The majority of papers comparing multiple clustering methods use simulated data or data from simple organisms such as bacteria and yeast [4–6], which may limit the applicability of their findings to data from more complex sources such as human tissues which express more genes. Thus, to investigate human disease, it would be useful to test the methods upon expression data derived from complex human tissues, among which brain tissue is particularly complex since it expresses a higher proportion of the genome transcribed than other tissues[7, 8]. Thalamuthu et al  have previously looked at a wide range of datasets, including some human expression datasets. However, since they restricted their analysis to functionally defined subsets of genes, that analysis did not fully reflect the complexity of human expression, particularly for disorders where there is insufficient knowledge of their aetiology to focus on specific subsets of genes.
We have examined four methods, k-means clustering, Chinese Restaurant Clustering (CRC), the Iterative Signature Algorithm (ISA)[12, 13] and a new, progressive variant of ISA called memISA. memISA was loosely based upon another method called PISA, for which there was no suitable implementation. These were chosen after a literature survey of the available methods (see table in Additional Files 1). All four are unsupervised methods that derive the clusters from the input data, rather than supervised methods which classify genes into user-specified clusters.
Many of the available comparative clustering studies focus exclusively on older methods [5, 15], or restrict their analysis to a single class of clustering methods [4, 6]. In our study, the methods were chosen on the basis of variety. ISA and memISA are examples of biclustering methods, CRC is a mixture model based method, while k-means clustering is a simple, well-understood algorithm. They were reported as performing well by their authors and/or other studies [4, 5].
The methods were also chosen partly on the basis of novelty. Apart from k-means clustering, they are too recent to have been included in many previous surveys of clustering methods, and so are particularly in need of testing.
We compared the performance of these three methods by examining the results for biologically meaningful clustering by looking for gene ontology (GO) enrichments within the resulting clusters. We also generated and compared a modified variation of ISA, memISA, which weighted against genes that were already members of a cluster to prevent bias of clusters detected from the strongest genes within them.
Three datasets were used for testing, the Dobrin , McLean 66  (MC66) and Perrone-Bizzozero (PB – GEO dataset GSE4036)  datasets (Table 1). They were downloaded in CEL format from the Stanley Medical Research Online Genomics database, the Harvard National Brain Databank database and GEO, respectively. They were then processed using R, with custom CDF files to map the probes to genes. Box plots were used to examine the quality of the data, and several outlier samples were removed. Three versions of each dataset were produced. One was normalised by the RMA median polish method, for use in CRC and k-means. The other two were normalised to produce a gene-normalised and sample-normalised dataset for running ISA.
Quality control consisted of box plotting the samples and removing outliers.
Gene coverage, the percentage of genes on the chip that are put into at least one cluster, was assessed for the cluster set produced by each method.
The methods were also assessed by speed. As ISA and memISA are dependent on parallelisation to run at a reasonable speed, this is taken as real-world time taken to run, rather than computer run-time used. For k-means and penalised k-means, this includes the time taken to estimate k.
GO enrichment is a method that assesses the percentage of clusters that are significantly enriched (compared to all annotated genes on the microarray) with genes from one or more Gene Ontology categories (from the goa_human database) at different significance levels, using Fisher's exact test and the Benjamini false discovery rate multiple testing correction. Clusters were tested for enrichment (using Fisher's exact test) for all GO biological process terms 3 or more levels deep into the hierarchical tree of GO terms, at several different levels of significance. At least 3 genes from the input cluster had to match a GO category for the cluster to be counted as enriched for that category, to ensure that chance appearance of 1 or 2 genes from a GO category with few members could not affect the results. The percentage of clusters matching this criterion gives a measure of the biological, functional relevance of the clusters.
GO enrichment was determined with the web-based service, GOstat. This accepts multiple kinds of gene name or ID as input, allowing approximately 85% of genes within the input clusters to be included. This was automated using WWW-Mechanize, a Perl module.
To compare the results of GO enrichments for the various clustering algorithms, we also examined several random cluster sets using GO enrichment. Four sets of clusters with the same distribution of cluster sizes as those made by k-means (at the value of k recommended by cascadeKM), CRC, ISA and memISA (both after removal of overlapping clusters) were produced. The cluster sets made from the Dobrin, MC66 and PB datasets were combined when determining the distribution of sizes. The new cluster sets had genes chosen at random from all those available on the Affymetrix 133P chip.
k-means clustering is a standard clustering method that has been in use for several decades . It requires that the user specify the number of clusters to sort the genes into (k). k-means clustering is a single cluster membership method – each gene can belong to only one cluster and it also assigns every gene to a cluster. Essentially, it distributes k centroids (quasi-data points representing cluster centres) throughout the data. Data points are then assigned to their nearest cluster, and the centroids are moved to minimise the distance between them and their assigned data points. This is repeated until the centroids stop moving. A number of distance measures can be used to define distance between data point and centroid, with Euclidean distance being one of the most commonly used and simplest. The procedure is summarised in Fig. 1.
Flowchart summarising the method used by k-means clustering. k is a user-defined input parameter which sets the number of clusters k-means clustering will find.
There are numerous variants of k-means clustering [27, 28]. Here, two are tried – standard k-means clustering, as above, and penalised k-means clustering. Penalised k-means clustering uses a threshold parameter (λ) to allow some of the genes to be treated as noise, and not clustered.
Initially, an estimate for the value of k was found for all three datasets using the cascadeKM function in R. Values of k between 2 and 35 were assessed, with 25 iterations per value, and the k values that minimised the Calinski criterion were chosen . The recommended values of k were 6 for Dobrin, 7 for MC66 and 8 for PB cerebellum. k-means and penalised k-means were then performed on all four datasets at 200 iterations and these values of k. The recommended value of 0.1 was used for λ in penalised k-means.
These small values of k will only partition the data into several large clusters, which may be too general a grouping to provide biologically relevant inferences. To examine the performance of k-means when producing smaller, more specific clusters, and also for a more direct comparison to CRC, k-means and penalised k-means clustering were also performed with values of k equal to the numbers of clusters produced by CRC on that dataset (23 in all cases).
k-means was performed using Cluster 3.0 . Penalised k-means clustering was performed using PWKmeans . Both were performed on a Windows desktop PC with 2 GB RAM, using a 2.66 Ghz processor.
CRC is a model-based clustering method. The name arises from a metaphor where genes are regarded as customers in a Chinese restaurant with unlimited tables of unlimited size, each representing a cluster, and their food orders represent the expression profile of each gene. The customers are then seated at tables according to the similarities of their food orders. CRC has several advantages over other methods. It can handle missing data and cluster genes based on negative correlation and time-shifted correlation. Like k-means it is a single cluster membership method. Its methodology is complex, and is based upon treating the expression profiles of the genes as the sum of multiple normal distributions.
The procedure is outlined in Fig. 2. Each iteration of the flowchart in Fig. 2 can be considered a Markov chain process. CRC runs a number of these chains in parallel (set by the user – 10 is the recommended amount), and reports the highest likelihood cluster set as the final output. The chains are also limited to a certain number of iterations through the flowchart before reporting their clusters. This is another parameter decided by the user, and is recommended to be set at 20. Finally, a probability cut-off can be input, which determines how high the likelihood of a gene being a member of a cluster needs to be in order for it to be included in the final output. In practice, most genes are members of their cluster with probability 1, so this removes few genes.
Flowchart summarising the method used by CRC. One run through this flowchart equates to a single chain in CRC, with several chains being run in parallel. The number of parallel chains and the maximum number of iterations are user-defined parameters.
CRC was performed on all three datasets. It was performed at two parameter sets for each dataset – 10 chains/20 cycles per chain/probability cut-off of 0.7, and 20 chains/40 cycles per chain/probability cut-off of 0.9.
CRC was performed using a standalone program . It was performed on a Unix server running Redhat OS with 32 GB RAM, using one 2.2 Ghz processor.
ISA is a biclustering method – it clusters both rows and columns of the dataset, here the genes and the specific samples they come from [12, 13]. This allows ISA to focus on subsets of samples with good signal for the genes of the cluster, reducing the amount of noise (see Fig. 3). Unlike k-means and CRC, it is not a single-cluster membership method: it allows genes and samples to belong to multiple clusters, and does not have to put every gene into a cluster. A high proportion of its clusters were found to be significantly enriched for one or more GO terms in yeast data.
Flowchart summarising the method used by ISA. tG and tC are user-defined threshold parameters. They determine how great the level of expression for a gene or sample (defined in standard deviations from the weighted mean of all genes over those samples, or all samples over those genes) needs to be for selection in the cluster. Higher values lead to more, smaller clusters, lower values to fewer, larger clusters. A preliminary run at a low number of iterations, with a wide range of values for tG and tC, is used to determine a sensible range of tG and tC values for use in the main run.
ISA produces tens of thousands of clusters. In postprocessing, to reduce this set to a manageable size, duplicate clusters are removed, similar clusters are merged, and clusters can be reiterated through ISA. The nature of postprocessing affects the final clusters.
ISA also assigns 'scores' to genes and samples it has clustered, as part of its method. A gene or sample with a high score will have more influence on the samples or genes selected at the next stage of the process. The final values of these scores are reported in ISA's output. A high score here indicates that the gene or sample has had greater influence over the clusters' contents than a gene or sample with a low score.
ISA was used on the Dobrin datasets with 8 different postprocessing regimes (see Additional Files 2, ISAPostprocessing.doc, for details). The regime that produced the highest GO enrichments included filtering the clusters by size and number of occurrences, and using less stringent similarity criteria when combing similar clusters.
To compare with memISA, CRC and k-means, runs were performed on all three datasets, at 20000 iterations. These runs used tG values of 1.0 to 4.2 (inclusive, increasing in 0.1 intervals). Different tC values were used for different datasets, as each contained different numbers of samples – Dobrin was run at tC 0.2, 0.5 and 1.0, MC66 at 0.25 and 1.25, and PB cerebellum at 0.1, 0.4 and 0.7. Filtering was used – a cluster had to have appeared 3 times, and contain at least 40 genes, to be included in the final output. Clusters that shared 70% or more of their genes with a larger cluster were removed from the final results (see below).
ISA was written in Perl (see Additional Files 3, ISAScripts.zip for a zip file containing all ISA and memISA scripts), based upon the previous Matlab implementation. This implementation has all of the properties of the Matlab version. The postprocessing scripts were written in Perl. The normalisation script was written in R. ISA was parallelised using Cardiff University's CONDOR network, which distributes individual ISA runs to unused Windows desktop computers across campus.
The underlying method of memISA is closely based on ISA and similar to PISA (Fig. 3). It biases against both genes and samples that have already been put into a cluster, according to two user input parameters, f and n. The bias is calculated relative to the highest scoring gene and sample in a cluster – this has its gene/sample score multiplied by the factor (1 - f) in future iterations. All other genes/samples found in a cluster have their future scores reduced by a smaller amount. This is determined by the proportion of their score and the highest gene/sample score – a gene with a quarter of the score of the highest gene will have its future scores multiplied by 1 - (f * 0.25). The intent of this is to bias against the highest scoring genes of a cluster while allowing lower scoring genes to be relatively unaffected and still be included in subsequent clusters (the highest scoring genes typically have scores 10 times greater than the majority of genes in a cluster). If a gene/sample is included in a subsequent cluster, the biases are multiplied together – a gene which is the strongest gene in two successive clusters would have its score multiplied by (1 - f)2 in following iterations.
These biases are only remembered for a certain number of iterations (n). Every n iterations, the slate is wiped clean. This is to ensure that memISA does not begin returning noise once it has found all the available clusters in the data, and to limit the effect that an early misclustering can have on the results.
memISA was run on the Dobrin dataset at 20000 iterations with a number of different values for f and n (Table 2). It was found the results were generally robust to the values of f and n, and that f = 0.7 and n = 5 produced clusters with the highest GO enrichment, so these values were used in all further analysis. A filtering step was also attempted on one dataset to see if it would improve GO enrichment. For this, those genes whose gene scores were in the bottom 10% for their cluster were removed from the cluster. This step reduced both gene coverage and GO enrichment and so was not used further.
memISA was run on the Dobrin, MC66 and PB cerebellum datasets at tG 1.0 to 4.2 (inclusive, increasing in 0.1 intervals) and tC 0.2, 0.5 and 1.0. Filtering was carried out as with ISA, using an occurrence threshold of 3 and a size threshold of 40.
memISA was implemented in Perl, and was based upon the new Perl implementation of ISA. Like ISA, it was parallelised using CONDOR.
We examined inter-method overlap in gene membership of clusters for the four methods and intra-method overlap of ISA and memISA. CRC and k-means, as single-cluster membership methods, had no intra-method overlap between their clusters. ISA and memISA cluster sets, however, both contained a large amount of intra-method overlap, making them impossible to compare fairly with clusters produced by k-means or CRC. To try to facilitate fair comparison, clusters with gene overlap above a certain level (values of 60, 70 and 80% gene overlap were tried) were merged but since this resulted in datasets with fewer than 3 clusters, this approach was abandoned. As an alternative, where over 70% of the genes in the smaller of a pair of clusters was shared with a larger cluster, the smaller cluster was removed. This process was performed on a subset of ISA and memISA output – those raw clusters produced at tG = 2.1 or greater were used, and the rest discarded. This was in order to prevent a few very large clusters causing the removal of nearly all smaller clusters. This overlap removal step was applied after all other postprocessing.
As there was not a large amount of overlap in clusters obtained between the ISA methods and either CRC or k-means, the possibility of combining their cluster sets to improve GO enrichment was investigated. The cluster sets were simply combined and clusters that had over 70% gene overlap with a larger cluster were removed as above. One set contained k-means, memISA and ISA clusters, one set contained CRC, memISA and ISA clusters. The memISA and ISA clusters had already had overlaps removed before combining. The CRC set used was the 10 chains/20 cycles per chain/0.7 cut-off. The k-means sets used were the k = 23 and k = 22 sets.
The clusters produced from the combined k-means/ISA/memISA method on the Dobrin dataset were tested for enrichment with 607 genes associated with schizophrenia below a nominal threshold of p < 0.005 according to a recent genome-wide association study using the program EASE , which implements a version of Fisher's Exact Test. Enriched clusters were also tested for enrichment for 352 genes differentially expressed between schizophrenics and controls in the analysis of the Stanley Medical Research Institute Online Genomics Database at an uncorrected p-value of 0.02 or lower.
Clusters from combined k-means/ISA/memISA in the independent MC66 dataset that shared over 45% of their genes with any enriched cluster from the Dobrin dataset were then identified. Their enrichment for schizophrenia-associated genes and genes differentially expressed in schizophrenia was then determined with EASE. A permutation-based method of enrichment determination was also used. This allows the enrichment p-value for the MC66 clusters to be determined independently of the Dobrin cluster. 4000 pairs of clusters were constructed at random from the genes present on the Affymetrix 133A chip.
The random clusters were constructed in pairs, as follows. Firstly, the number of genes shared between the three clusters was calculated (see Fig. 4). These figures were then used to create randomised MC66 clusters with the same level of overlap with the Dobrin cluster and each other.
Overlap between putative schizophrenia-related clusters produced from Dobrin and MC66 datasets. Venn diagram showing the amount of overlap between the clusters enriched for schizophrenia-related genes, in order to construct randomised clusters for permutation.
165 genes from the Dobrin 3093-gene cluster were selected at random, and placed in both the 2546-gene and 436-gene MC66 randomised clusters. From the remaining Dobrin cluster genes, 1068 and 24 genes were selected at random, the former placed in the 2546-gene randomised cluster, the latter placed in the 436-gene randomised cluster. Then, 102 genes from the genes on the chip not present in the Dobrin 3093-gene cluster were selected at random, and placed in both the 2546-gene and 436-gene randomised clusters. From the remaining genes on the chip not present in the Dobrin 3093-gene cluster, 983 and 90 genes were selected at random, the former placed in the 2546-gene randomised cluster, the latter in the 436-gene randomised cluster. This was repeated 4000 times to produce a population of 8000 random clusters. These clusters were then processed with EASE in the same way as the original cluster, allowing the original results to be compared to them.
These clusters were also examined for enrichment in KEGG and BioCarta pathways, using the Composite Regulatory Signature Database  (http://140.120.213.10:8080/crsd/main/home.jsp), and for enrichment in GO biological process categories using GOstat.
EASE was also used to test these clusters for enrichment with genes found to be ten-fold or more upregulated in specific cell types within brain tissue according to Cahoy et al -specifically, neurons, oligodendrocytes and astrocytes.
All four methods performed better than the random cluster sets when examined using GO enrichment to represent known biological relationships (Figs. 5, 6, 7). This implies that all the clustering methods result in groupings of biological significance. Of the three random cluster sets, those with the same size distribution as ISA had slightly lower GO enrichment than those with the same size distribution as memISA or CRC. This may suggest that GO enrichment has a small bias against ISA due to the sizes of clusters it produces. However, at p < 0.05 the difference dropped to under 1% GO enrichment, suggesting that any such bias is extremely slight and may well be due to chance.
GO enrichment and gene coverage of clusters for all methods – Dobrin dataset. Orange, green, yellow and light blue bars are the percentage of clusters that are significantly enriched for one or more GO categories at p < 0.05, 0.01, 0.001 and 0.0001 respectively. Dark blue bar is gene coverage, the percentage of genes available on the chip that are assigned to at least one cluster. Numbers in square brackets are the number of clusters produced by that method. 'Dist.' = distribution of sizes. Parameter set A for CRC is 10 chains and 20 iterations per chain. Parameter set B for CRC is 20 chains and 40 iterations per chain.
GO enrichment and gene coverage of clusters for all methods – MC66 dataset. Orange, green, yellow and light blue bars are the percentage of clusters that are significantly enriched for one or more GO categories at p < 0.05, 0.01, 0.001 and 0.0001 respectively. Dark blue bar is gene coverage, the percentage of genes available on the chip that are assigned to at least one cluster. Numbers in square brackets are the number of clusters produced by that method. 'Dist.' = distribution of sizes. Parameter set A for CRC is 10 chains and 20 iterations per chain. Parameter set B for CRC is 20 chains and 40 iterations per chain.
GO enrichment and gene coverage of clusters for all methods – PB dataset. Orange, green, yellow and light blue bars are the percentage of clusters that are significantly enriched for one or more GO categories at p < 0.05, 0.01, 0.001 and 0.0001 respectively. Dark blue bar is gene coverage, the percentage of genes available on the chip that are assigned to at least one cluster. Numbers in square brackets are the number of clusters produced by that method. 'Dist.' = distribution of sizes. Parameter set A for CRC is 10 chains and 20 iterations per chain. Parameter set B for CRC is 20 chains and 40 iterations per chain.
k-means and penalised k-means produced clusters with high GO enrichments, especially at the lower k values recommended by cascadeKM. In these low-k cluster sets, k-means obtained higher GO enrichments than penalised k-means. In the k = 22 and k = 23 cluster sets, they produced cluster sets with similar GO enrichment (Figs. 5, 6, 7). As k-means gave similar GO enrichment to penalised k-means and by definition clustered more genes it was used in comparisons with the other methods.
The different parameter sets used for CRC made little difference to the GO enrichments of its clusters. (Figs. 5, 6, 7). Increasing the numbers of iterations or cycles or increasing the probability cut off had little effect which suggests that altering these parameters is unnecessary, and that the default values of 10 cycles and 20 iterations per cycle should be used for most datasets, with parameters only being increased on very large datasets. One problem noted with CRC was that analysing more than 202 samples caused the program to crash. This occurred on both Windows and Linux versions of the program, so was presumed to be an inherent problem with the program.
In contrast to CRC, changing the parameters of ISA can have unpredictable effects on the GO enrichment of its clusters, particularly after overlaps have been removed (see Figs. 5, 6, 7). The different values of tC used in memISA and ISA for the PB cerebellum and MC66 datasets may help explain some unexpected results – in particular, the very large number of clusters produced by memISA prior to removing the overlaps in PB cerebellum, and the unexpectedly poor performance of memISA on the MC66 dataset. However, these may also be due to chance differences in the selection of random starting clusters, or to inherent qualities of the methods.
memISA is robust to the choice of f and n, as all of the combinations tried produced reasonable GO enrichments (see Table 2). f = 0.7 and n = 5 were chosen because they produced clusters with slightly better GO enrichments than other parameter sets.
There was a large amount of overlap between the clusters produced using penalised k-means and k-means at k = 23, with the majority of clusters (from all three datasets) having over 70% overlap with a cluster from the other method, and all others having over 40% overlap (see Table 3 and Additional Files 4 – AllOverlaps.xls for more detail). Since these methods found similar clusters, further analysis was focused on standard k-means clustering, as it had 100% gene coverage.
Values in table indicate the percentage of clusters produced by the method in the left margin that have over 70% gene overlap with one or more clusters produced by the method in the top margin.
There was considerable overlap in the results obtained between k-means and CRC across all three datasets. This suggests that k-means and CRC find similar patterns within the datasets. Conversely, there was little overlap between either k-means or CRC and either memISA or ISA clusters. In the case of ISA, there were a few overlaps at 70% or above for each dataset. In the case of memISA, there was a large cluster that overlapped with several of the smaller clusters produced by CRC at 70% or more, plus one other 70% plus overlap between more similarly sized clusters, in all three datasets.
Removing clusters with over 70% intra-method gene overlap from the ISA and memISA cluster sets reduced the number of clusters considerably. These sets contained only 4–10 clusters and were much smaller than the original ones. However, their GO enrichments were generally considerably higher (see Figs. 5, 6, 7) but at the cost of a considerable drop in gene coverage.
Nearly all ISA clusters had over 70% overlap with a memISA cluster across all three datasets. However, less than half of the memISA clusters had over 70% overlap with a cluster from ISA, as many of the ISA clusters overlap with the same memISA cluster. This level of overlap is surprisingly high, considering that their post-processing regimes already include a step to merge similar clusters. However, this step requires high sample overlap and correlation of shared gene/sample scores in addition to simple gene overlap. It also uses the size of the larger cluster to calculate overlap – i.e. 50% overlap in this step indicates that 50% of the genes in the larger cluster are found in the smaller cluster. As a result, it tends to only combine clusters of a similar size. The ability of memISA to bias against already-found clusters may help it find clusters that would previously have been hidden by a stronger cluster, a useful feature when looking for novel clusters.
The tendency of the cluster merging step in ISA and memISA to only combine clusters of a similar size may help to explain the improvement in GO enrichment the removal of overlapping clusters produces. Requiring a similar size and similar samples and gene/sample scores may help to ensure that only those clusters which come from the same signal are actually merged, excluding noise clusters with a coincidentally high gene overlap. The overlap removal process would then remove these clusters from the dataset altogether, improving GO enrichment.
The reasons for the poorer performance of memISA on the MC66 dataset are not known. It is possible that the difference in the tC and tG parameters between memISA and ISA for this dataset was critical. The smaller number of genes in this dataset might also be important, and so reducing the values of tG used may help. Alternatively, it might be that chance played a role. memISA may be inherently more prone to chance variation than ISA or CRC.
The cluster sets produced by combining the methods had similar gene coverage to those produced by CRC/k-means alone (see Figs. 5, 6, 7). They generally had a higher number of clusters. For the CRC/ISA/memISA combined set, the GO enrichment of these clusters was higher in the Dobrin and PB cerebellum datasets,. In the k-means/ISA/memISA combined sets, the gains in GO enrichment relative to k-means alone were generally smaller: under 5% at most levels of p. There were a few small losses in GO enrichment in some datasets and at some levels of p, but generally the impact on GO enrichment was still positive.
Before highly overlapping clusters were removed from the clusters produced by ISA, k-means had the highest gene coverage (100% by definition), followed by CRC, and then by memISA and lastly ISA. However, these cluster sets are not directly comparable on number of clusters or on GO enrichment, as the cluster sets produced by ISA and memISA contain a large amount of redundancy.
As memISA and ISA had much lower gene coverage than k-means or CRC, the relationship between mean gene expression levels and cluster membership was examined for these methods in the Dobrin dataset. For both ISA and memISA, no significant correlation was found (r = -0.132 for ISA, r = -0.081 for memISA).
The number of genes per cluster for each method and dataset was also examined, and the mean cluster size and standard deviation computed (see Additional Files 5, SizeDistribution.xls). Generally, CRC, k-means and penalised k-means were consistent in their cluster sizes, which appear to vary only with the number of genes in the dataset. The average cluster size was between 800 and 900 for these three methods in both 133P datasets (Dobrin and PB), and between 500 and 600 in the MC66 dataset. ISA generally produces clusters that are smaller than this, between 400 and 600 on average (with no obvious relationship to number of genes or samples in the dataset). memISA, conversely, is particularly prone to producing datasets with one or two particularly large clusters, giving it a higher average cluster size and standard deviation. This is because the larger number of unique clusters it produces makes it more likely for clusters to overlap and be merged, leading to these extremely large clusters.
To examine whether cluster size affected enrichment, cluster size was checked for correlation with log10 of the p-values of the best GO hit for each cluster (unenriched clusters were treated as having a p-value of 1). No significant correlation was found for any of the methods.
The three datasets were used to evaluate approximate runtimes for the four methods (see Table 4). CRC and k-means are very fast methods, with a runtime of a few hours on current computer technology. ISA and memISA, meanwhile, are much slower, taking up to a month without parallelisation. Even with parallelisation using CONDOR, ISA and memISA can take over 24 hours for a full parameter set when post-processing is included. Restricting the parameters to tG 2.1 and above, as in the non-overlapping cluster set before, reduces these times by up to half.
Table showing the real-world time taken for the methods to run on each dataset.
The clusters produced from the combined k-means/ISA/memISA method on the Dobrin dataset were tested for enrichment with 607 genes associated with schizophrenia according to a recent genome-wide association study, using the program EASE . These 607 genes each contained at least one SNP associated with schizophrenia at an Armitage p-value of 0.005 or under. One cluster, containing 3093 genes and originally found by memISA, was enriched (p = 0.0104 after Bonferroni correction for 26 clusters).
This cluster was also tested for enrichment with 352 genes found to be differentially expressed between schizophrenics and controls in the analysis of the Stanley Medical Research Institute Online Genomics Database at an uncorrected p-value of 0.02 or lower. The cluster was slightly enriched, at a p-value of 0.09.
Clusters from combined k-means/ISA/memISA in the independent MC66 dataset that shared over 45% of their genes with this enriched cluster were then identified. Two clusters were found (containing 2546 and 436 genes respectively), both of which were nominally enriched for both schizophrenia-associated genes (2546-gene cluster at p = 0.0127, 436-gene cluster at p = 0.0117) and genes differentially expressed in schizophrenia (2546-gene cluster at p = 0.0064, 436-gene cluster at p = 0.00047 – see Additional Files 6, Clusters.xls, for the gene symbols of the genes in these clusters). However, since these clusters have some overlap with the 3093-gene Dobrin cluster, this cannot be considered independent replication of the original cluster.
To avoid this confounding effect, their enrichment for schizophrenia-associated genes and genes differentially expressed in schizophrenia was determined using a permutation-based method. The 436-gene cluster remained significantly enriched for the schizophrenia associated genes, while the 2546-gene cluster showed some enrichment, but this was insufficient to be significant (permutation p = 0.169 for the 2546-gene cluster, permutation p = 0.0255 for the 436-gene cluster). However, both clusters were significantly enriched for genes differentially expressed in schizophrenia (permutation p = 0.0053 for the 2546-gene cluster, permutation p = 0.0005 for the 436-gene cluster).
These clusters were also examined for enrichment in KEGG and BioCarta pathways, using the Composite Regulatory Signature Database  (http://140.120.213.10:8080/crsd/main/home.jsp). The top hit for the Dobrin cluster and the 2546-gene MC66 cluster was the KEGG entry for the MAPK signalling pathway (p = 1.12e-7, FDR q = 0.00024 in Dobrin, p = 6.95e-10, FDR q = 1.46e-6 in MC66). The only significant hit for the MC66 436-gene cluster was from the BioCarta Synaptic Junction pathway (p = 3.88e-5, FDR q = 2.71e-2).
The MC66 436-gene cluster was also examined using GOstat, where the best hit was for GO:0007399 (nervous system development) GO category (p = 0.044 after FDR correction).
The three clusters were also tested for enrichment with genes found to be ten-fold or more upregulated in specific cell types within brain tissue according to Cahoy et al -specifically, neurons, oligodendrocytes and astrocytes. All three clusters were found to be highly significantly enriched with genes upregulated in neurons (p = 2.5e-21 in Dobrin, p = 1.55e-16 in MC66, Bonferroni corrected). There was also enrichment for genes upregulated in oligodendrocytes (Dobrin p = 0.06, MC66 p = 2.4e-4, Bonferroni corrected) and astrocytes (Dobrin p = 5.13e-22, MC66 p = 2.26e-10, Bonferroni corrected).
Three overlapping clusters, enriched to varying degrees for either schizophrenia-associated genes or genes differentially expressed in schizophrenia, were found from the two independent dorsolateral prefrontal cortex datasets. The apparent excess of schizophrenia-associated genes in the 2546-gene MC66 cluster could be explained by its overlap with the Dobrin cluster. Thus, this cluster does not constitute independent evidence for schizophrenia-associated genes clustering together with respect to their expression levels. However, the 436-gene MC66 cluster remained significantly enriched when assessed by the permutation method. Both MC66 clusters did show significant over-representation for genes differentially expressed in schizophrenia, even after correction for the overlap with the Dobrin cluster. This demonstrates the ability of the methods to find potentially disease-related gene clusters that are replicable in multiple datasets.
The large size of two of the clusters makes inferences about individual genes difficult. However, both the larger clusters are enriched for genes present in the KEGG MAP kinase pathway, suggesting that this pathway may relate to the aetiology of schizophrenia. Members of this pathway have also been found to be differentially expressed between controls and schizophrenics in other brain regions . In addition, when structural variants such as microdeletions occur in the genomes of schizophrenics, they are particularly likely to occur in the genes of the MAP kinase pathway .
The smaller cluster was also found to be near-significantly enriched for serine/threonine kinase genes (the class of kinases which MAP kinases belong to), and also for synaptic junction and neurological development genes. As this cluster is enriched for both schizophrenia associated genes and genes differentially expressed in schizophrenia, further investigation of the role of these pathways in schizophrenia aetiology may be useful.
However, the MAP kinase-related genes present in the two large clusters do not overlap with the schizophrenia associated gene set or the differentially expressed in schizophrenia gene set (they share no genes at all in either the MC66 or Dobrin cluster). This might suggest the MAP kinase function of the clusters may be incidental to their roles in schizophrenia aetiology. Further investigation with other functional analysis tools may allow more biological inferences from these clusters.
Our findings broadly agree with several other surveys of clustering methods (Figs. 5, 6, 7). Like Prelić et al, we find that ISA is an effective method that produces clusters with high GO enrichment , but our cluster sets generally do not have as high a proportion of GO enriched clusters as theirs. This is likely to be a consequence of the greater complexity of the input data.
Garge et al found k-means clustering effective  on a wide range of input datasets. This is echoed by the k-means cluster sets reported here, which have high GO enrichment and gene coverage scores. These scores were generally higher than CRC, the mixture modelling method examined here. This contrasts with the findings of Thalamuthu et al, who found that modelling methods were superior to k-means clustering . This difference is again likely to be due to the datasets used; in particular the datasets used here were much larger in size.
k-means clustering, CRC, ISA and memISA are all potentially useful methods. Considered alone, k-means clustering is probably the most useful of the four, as it is fast, does not require parallelisation, and produces clusters with slightly higher levels of GO enrichment than CRC when producing similar numbers of clusters. When used to find smaller numbers of clusters more in line with the estimation of k, the GO enrichments are higher still, reaching 100% at some levels of p. It also assigns a cluster to every gene (100% gene coverage), unlike overlap-removed ISA and memISA (under 30% gene coverage). Although this must lead to some false positives, this does not seem to have affected the GO enrichment scores unduly, and is an advantage in exploratory studies where as wide a view as possible is desired. Furthermore, k-means is a relatively simple and very well understood method. This simplicity may be the reason for its good performance here, as it may allow it to cope with a wide variety of input data. CRC, conversely, has many more parameters and so may have had scope to become optimised for the smaller yeast and bacterial datasets it was built for and tested upon.
However, for the fullest picture of clusters available in a dataset, combining memISA, ISA and k-means is the best option, as it offers higher GO enrichment than k-means alone in two out of the three test datasets while retaining 100% gene coverage (see Figs. 5, 6, 7). Even in the MC66 dataset, it added additional clusters not found by k-means without reducing GO enrichment. One of these memISA clusters (found in both dorsolateral prefrontal cortex datasets) was found to be significantly enriched for schizophrenia-associated genes and genes differentially expressed in schizophrenia, further emphasising the utility of combining methods. If time allows, this combined method should be the method of choice for clustering microarray brain expression data.
ALR is supported by the MRC through a bursary. We would also like to thank Seth Dobrin for access to his data, and the two anonymous reviewers for their suggestions and constructive criticism.
ALR wrote all programs, performed analyses and wrote the paper. PH designed the permutation method used to assess cluster enrichment. LJ, PH and MCO constructively evaluated and edited the paper, and advised additional analyses. MJO provided access to the schizophrenia association data.


WHAT'S YOUR FAVOURITE FALL LIP?
Do you stick with nudes, break out the bold or blackened berries, or go for vivid reds?
This week the Lipstick League wants to know whether you have a go-to Fall lip shade, and if yes, what is it, please?
I don't have a specific Fall go-to lipstick, but this is the time of year that I gravitate toward richer vivid shades. Blackened berry, deep fuchsia, crimson, scarlet.... Aside from blackened berry, I tend toward more vibrant shades – dark lip colours with a lot of brown or brick in them can make my lips look thinner, almost pinched; intense vibrant shades, like the reds and fuchsias in the above snapshot, do not.
My Beauty Bunny is giving away a Too Faced Just Peachy Mattes eyeshadow palette, plus the chance to win $350 cash. PLUS stay tuned for a week of giveaways next week!
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What's your favourite Fall lipstick shade? Do you have just one?


text:
WW2 bomb blown up in Germany after evacuation
A 250kg (550lb) World War Two bomb caused damage in a German city when it exploded on Wednesday under controlled circumstances, BBC reported.
It was discovered in the southern city of Regensburg during construction work.
The bomb could not be transported or defused, experts found. So they instead evacuated some 4,500 people in a 1.5km (one mile) radius.
Despite the planned explosion, nearby buildings were damaged in the blast, their windows shattering.
Unexploded bombs from the war are not uncommon in Germany, which was heavily bombed by Britain and, later, the US.
Regional newspaper Mittelbayerische Zeitung reported that the Regensburg bomb was equipped with a particularly complicated detonator.
It also presented a genuine threat, investigators decided - rather than being considered a dud - and had to be detonated on the spot as soon as possible.
A bomb disposal robot was used to place an explosive charge next to the bomb, it reported. The resulting explosion, shortly before 05:00 local time (03:00 GMT) , could be heard kilometres away.
Police then began a safety check of the area before allowing residents to return to their homes. A full inspection of the damage caused will be carried out on Wednesday.
The bomb has not been identified - but British and US forces bombed the city multiple times during World War Two, dropping hundreds of bombs, many of which did not explode.
A US-made bomb was discovered in the city in January, which was defused after a lengthy operation that resulted in the evacuation of more than 2,000 people.
In June 2017, a similar discovery made headlines when more than 100 prisoners had to be evacuated when a bomb was found near a jail - along with 1,500 residents.
Across Germany, evacuations for old bombs sometimes involve entire populations. About 18,500 people were evacuated from Ludwigshafen in August last year,while Frankfurt evacuated some 70,000 over the threat from a 1.4 tonne British "blockbuster" bomb in 2017.
Tags: WWII Germany
Turkey to start mass production of HÜRJET aircraft


text:
Tayfun Pirselimoglu
100' • Turkey • 2021
Turkish •
Can is the only witness of a murder in a train station in a bizarre town. He finds himself in an ambiguous plot, then. All of his attempts for getting that hell out are in vain.
© 2023 Budapest Independent Film Festival
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text:
Улапа де Мелчор Окампо () насеље је у Мексику у савезној држави Идалго у општини Тетепанго. Насеље се налази на надморској висини од 2061 м.

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