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As automakers look into developing a new generation of tech-savvy cars that can drive themselves, others are tackling a seemingly more impossible feat: giving the concrete roads they drive on brains of their own.
Continue Reading Below
Smart Highways – the brainchild of Dutch designer Daan Roosegaarde – would be able to charge electric cars passing over them, paint temporary digital snowflakes over ice patches, illuminate priority lanes and reduce energy waste by using streetlights donned with sensors.
It may sound fantastical, but this is not just some artist's dream. It's reality.
Bringing his idea to fruition, Roosegaarde, who has called himself in jest a "hippie with a business plan," has teamed up with an unlikely partner: publicly-traded multinational Heijman, one of Europe's largest engineers and road manufactures.
The earliest versions of their jointly-developed Smart Highway will appear later this year on a 150-meter stretch of road in the Netherlands province of Brabant. Roosegaarde predicts highways across the world can be transitioned within the next three to five years.
The idea last month won the INDEX: Award 2013 – a highly prestigious prize for "Designs to Improve Life" administered only once every two years. The prize comes with a 100,000-euro reward, which Roosegaarde will inject into the project's continued development.
"These are all ideas which are not super high-tech, they are basic principles which we can implement to existing roads," Roosegaarde said in a video posted to CNN, which highlights Index nominations.
Beautiful and Smart
The Index jurors said they were impressed with Smart Highway because it provides an entirely new approach to traditional roads that is "not only beautiful and alluring but also sustainable and cost-effective."
Using a series of solar technologies, dynamic paint and smart sensors, the road would be designed to come alive at night. Lanes would illuminate, electric cars would be charged, glow-in-the-dark paint would highlight dangerous conditions or traffic patterns and street lights would light up only as a car approached.
"Why can we not connect it to a small, simple sensor so the lights are only on when you drive so you get this beautiful sort-of ghost of light?" Roosegaarde asks.
The Super Highway is designed to be simultaneously beautiful and intelligent, armed with capabilities to spark future innovation around the way people drive and live.
While vehicles themselves continue to be a major piece of this puzzle as manufacturers like Elon Musk's Tesla (NASDAQ:TSLA) build electric cars and others focus on autonomous ones, roads have barely budged.
"When we look at highways, why is so much money and time and energy spent on cars but the actual roads themselves are still stuck in the Middle Ages?" Roosegaarde asks.
The project has a five-step plan for modernizing European roadways, including embedding highways with technology that can visually communicate with the driver using "smart" paint that would be charged by the sun throughout the day.
Paving existing roads with new technologies could help make electric cars -- even autonomous cars -- more feasible and desirable, with roads evolving as fast as the technologies that power them.
Wising Up
The basic idea is nothing new – in fact, as early as 1991 a professor at Virginia Tech proposed embedding electronic sensors into the pavement that would be able to relay information about road's conditions via "radio waves to a central computer."
In 2010, the research group ACCESS Linnaeus Centre announced a focus on "wireless sensor networks" for roads, saying it would help reduce the environmental impact of the transportation system and improve efficiency.
However, past projects have been hit with high costs and technological roadblocks.
Dr. Rajit Gadh, director of the UCLA Smart Grid Energy Research Center, which is studying how to better plug electric vehicles (EV) into the smart grid, says mass-scale projects cannot be widely successful without acquiring several minor successes first.
While the technology exists to charge EV cars wirelessly while in motion, he warns of the heavy costs of putting inductive coils underground every few feet on the highway and suggests instead using this still infant technology at high-traffic intersections, bus stops or stop signs where energy can be more efficiently transferred.
If developers prove through smaller feats the feasibility of their technology, they may be able to garner more investment, which could ultimately help trigger fresh commercial investment and adoption.
"There's still a lot of steps needed to be done in research labs before you can have a pretty scalable solution whereby you can charge in an economical manner while you're driving," Gadh said. Though he does see it becoming as ubiquitous as today's EV charging stations -- perhaps even gas stations -- much farther down the road.
Developers of the Smart Highway plan to use magnetic fields stationed under the road to charge moving cars. They are tweaking their technology in partnership with the Dutch government.
Ever the designer, Roosegaarde is already brainstorming new ideas, including applying some of these same technologies to illuminate bicycle lanes or airport landing tracks.
One day, he might even try to use the bioluminescence of jellyfish and fireflies to illuminate roadside shrubbery, an effort to once-and-for-all get rid of light poles.


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SYDNEY (Reuters Life!) - Former INXS and Noiseworks singer Jon Stevens is being held in hospital after suffering complications following emergency open-heart surgery, his management company said on Thursday.
The Australian rocker's manager Simon Lucas said the operation was carried out in a Sydney hospital about three weeks ago and was deemed a success.
But 46-year-old Stevens' condition has deteriorated and his recovery has been hampered due to some post-operative complications, primarily fluid around the heart and infection.
"Jon is in good spirits and following doctors' orders, but will not be released from hospital until he gets the go-ahead from his doctors and surgeon early next week," Lucas said in a statement.
Stevens was taken to hospital after doctors reportedly found a blockage in one of the main arteries to the heart during a routine check-up. Stevens had two stents inserted into the arteries in his heart in December.
Sydney band Noiseworks became successful in the late 1980s and New Zealand-born Stevens went on to tour and record with INXS from 2000, after the 1997 death of frontman Michael Hutchence.
He quit the band in 2003 and has released more solo albums including "Ain't No Life for the Faint Hearted" in 2004.
Reporting by Belinda Goldsmith, Editing by Miral Fahmy

subset: commoncrawl


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Telling the Truth in IT March 17, 2011Posted by mwidlake in Meeting notes.
Tags: Humour, Meeting, user group
I've been doing presentations for many years, mostly on Oracle Technology, occasionally on management topics. However, my favorite presentation to give is one about when things go wrong.
The title is usually something like "Surviving Survivable Disasters" or "5 ways to Seriously Screw up a Project" and, though the specific examples and flow may vary, the general content is the same. I talk about IT situations that have gone wrong or things that strike me as daft/silly/mindless in IT. My aim is to be entertaining and have a laugh at the situations but I also want to explore what causes disasters and how we might go about avoiding at least some of them.
When doing the presentation I have a couple of ground rules:
- I must have witnessed the situation myself or know personally, and trust, the individual who is my source.
- I do not name organisations or individuals unless I am specifically given permission {by individuals that is, organisations never get named. Except one}.
- I try to resist the temptation to embellish. It's not hard to resists, a good disaster usually stands on it's own merits.
It's a great talk for introducing some light relief into a series of very technical presentations or for opening up a day of talks, to get people relaxed. It's also the only talk I get seriously nervous about doing – if you are aiming to be entertaining and you miss, you stand to die on stage. The first time I did the talk I was physically sweating. However, it went down a storm. I did it 4 or 5 more times over as many years and it always went down well.
However, about 4 years ago I did the presentation just as I was about to go back to being self employed. After the talk a very good friend came over and said something like "Really entertaining talk but…maybe you should tone it down? A lot. Potential employers are going to take a dim view of you doing this, they will worry they will appear in the next talk". I protested that I never mention companies or people and, surely, all organisations are able to admit that things go wrong and it is to everyone's benefit if we all learn from them? My friend was adamant that though companies want to benefit from other disasters, they never, ever want to in any way be the source of that benefit. He was sure it would be very damaging to my potential career. Hmmmm…. I could see his point.
I was already scheduled to do the talk again in a couple of months and I took heed of his advice for it. I toned down the material, I removed some of the best stories and I added several disclaimers. I also died on stage. It went from an amusing 45 minutes to a preachy and stodgy affair.
I have not done it since.
The question is, should I have pulled back from doing that talk? Is it really going to harm my potential employability? (After all, no work has ever come my way from presenting). Why can't we be honest that issues occur and that learning from them is far more valuable than covering them up? After all, do we believe a person who claims never to have made mistakes?
What prompted this thread is that I have been asked to do the talk again – and I have agreed to do so. I'll be doing it next week, with the title "5 ways to advance your career through IT Disasters" for the UK Oracle user group Back to Basics event. This is a day of introductory talks for people who are fairly new to Oracle, the brain-child of Lisa Dobson. Lisa realised a few years ago that there were not enough intro-type presentations, most technical talks are by experts for fellow experts {or, at least, people wanting to become experts}.
I'm very happy to support helping those who are new to Oracle and I think it is important that people who are new to IT are exposed to what can go wrong – and any advice that might help them avoid it. After all, it's better they learn from our mistakes than just repeat them again. OK, they'll just repeat them again anyway, but they might spot that they are doing so just in time 🙂
Is this a good idea? What the hell, I want more free time to do things like this blog – and get on top of the garden.
- 1. Tony Sleight - March 17, 2011
I think you should go for it. You say yourself you've never had any work as a result of presenting. The potential work from your technical expertise and knowledge would far outweigh employment damage from a quirky and amusing 45 minute presentation where no names are mentioned. Would you be comfortable working in an environment where the thought that a company has only taken you on because you didn't do a presentation on the wrong way of doing things?
- 2. Bernard Polarski - March 17, 2011
"my favourite presentation to give is one about when things go wrong"
Sound like humankind history since stone age. By the way, if you ever come to meet a 'Sapiens Sapiens', shoot on sight : these are nasty beasts that roam Earth surface.
- 3. Graham - March 17, 2011
Nice post Martin, we've all been on the wrong end of some kind of avoidable disaster. If I could give one tip to an oracle (or anything else) newbie… if you make a mistake – do not try to cover it up!
- 4. PdV - March 18, 2011
Oh Yes! Do it.
Most IT disasters are human made: gung-ho-sales-on-targetbonus, optimistic techies, ambitious management, outsourcing, funny written SLA's, CYA-culture, FUD-vendors, service-managers, large-organisations, and humankind as a whole are a dangerous but interesting combination.
We, the techies, have to live up to all those promises made by the glamorous sales force.
And only by stateing simple truths and reigning in all the complex stuff, do I have any hope of keeping this operation going.
As a DBA and Oracle-techie, I end up between rocks and harder places.
Greetings from a far-away place,
(apologies for the moan, I'm helping to keep an operation going – pumpt out widgets to sell – despite all the corporate-HQ stuff that demands a reply).


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Hello dear Maddies. I'm back from a skiing trip in France. The mountains were beautiful and it was so peaceful and still. But I will make a post about it later.
Now, I want to talk about my bucket list. Do you guys have a bucket list?
There are a few thing in life I want to do like:
- Walk the GR 10 Trail through the Pyrenees
- Printing all my digital pictures, before my computer dies. (Plus a photo album is awesome)
- Spending the night on the beach.
- Seeing the Northern Light
- Enjoying the little things in Life
- ...
What's on your bucket list?


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We're only about three weeks out from adding a new little leaf to the family tree, by way of my first nephew. It's getting exciting around here and, of course, I'm integrating genealogy into the anticipation. I've been working on my brother-in-laws tree some more, but wanted to make something specifically for the little guy.
My sister had decided to make a home-made baby book: a 12x12 scrapbook filled with pages made by friends and family. I've made a few pages for her, but it occurred to me yesterday that I'd missed something: a family tree page! I used a stamp kit my mom gave me at christmas to create this page (I'll do one for my brother-in-law's family too):
I'm not sure if the page is finished yet. Should I add some grass or little animals? Either way I think it turned out nicely.
20 June 2013
Scrapping for the New Little Leaf
5 comments:
- Stephanie said...
That is completely awesome! So adorable!
- 21 June, 2013
- Grant Davis said...
Happy Blogiversary!!
Regards, Grant
- 25 June, 2013
- Grant Davis said...
Happy Blogiversary!!
Regards, Grant
- 25 June, 2013
- Tracy Meyers said...
Simply beautiful. Love the page and love the blog.
Happy Blogiversary!
- 25 June, 2013
- *GeorgiaTim said...
YES! I like the idea of animals. Search for "scrapbooking jolee animals" on Amazon. There are farm animals and zoo animals and other sorts. It's really cute.
- 29 June, 2013
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Kim Kardashian's Debut Song: An Anthem for the Art of Fameballing
[There was a video here]
Kim Kardashian debuted her first foray into music—a feat of autotuning called "Jam"—on Ryan Seacrest's radio show today. "Turn me up, Turn me up, Turn me, Turn me, Turn me up," she bleats thinly. Kim is either trapped at a club with very quiet DJ (The same club where Ke$ha and J.Lo are always begging, "Turn it up." Maybe the deejays are quiet because they only have access to shitty songs?) or she is asking us to turn up the faucet from which all things Kim Kardashian pour, because this song is a celebration of the art of earning your keep with half-hearted product endorsements and paid appearances:
DJ, here I am!
Feeling good, feeling great, just got paid!
Kim Kardashian has arrived. Pay up, suckers. [Ryan Seacrest]


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by Tyler Peck, sports information graduate assistant
MILLEDGEVILLE, Ga. - The Georgia College & State University basketball teams are both having very successful seasons thus far and are ahead of the Peach Belt Conference (PBC) preseason expectations.
Mike Hoffman, sports information director at Indiana University at Pennsylvania (IUP), wanted to see how the basketball teams at IUP fared against Division-II schools across the country after seeing how successful his teams had started. Hoffman took the combined wins of the men's and women's basketball teams along with the losses to see the winning percentage.
Georgia College ranked No.7 on the list with a combined winning percentage of .816, including all games played up to Feb. 9. The men were 17-2, the women 14-5 for a combined 31-7.
Minnesota State University and Gannon University are tied in first place with a .872 winning mark. Northern Kentucky University, University of Central Oklahoma, Fort Lewis College and IUP are all above Georgia College.
The Bobcats travel to face PBC opponent, Lander University on Saturday, Feb. 14 with games starting at 2 p.m.
Visit www2.GCSU.edu/Bobcats to nominate your favorite former Bobcats and Colonials to the GCSU Athletics Hall of Fame. The GCSU Department of Athletics sponsors 10 varsity athletic programs at the NCAA Division II level. As a Division II program, GCSU prides itself on balancing the life of the student-athlete, evidenced by the Bobcats' multiple appearances in post-season competition as well as documented academic success and community-service involvement.


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Cheechoo's power-play goal gives Sharks 3-2 victory over DucksNHL.com @NHLdotcom
ANAHEIM, Calif. - Joe Pavelski and Jonathan Cheechoo scored the tying and go-ahead goals during power plays, and the San Jose Sharks set a franchise record with their 52nd victory, beating the Anaheim Ducks 3-2 on Sunday night.
Jeremy Roenick had his first goal in four months, and backup goalie Brian Boucher stopped 19 shots for the Sharks, who lead Boston by three points in the race for the NHL's best record (115-112).
San Jose has never won the Presidents' Trophy, despite recording no fewer than 104 points in four of the last five seasons. The Sharks' final three games are against the bottom three teams in the Western Conference - Colorado, Phoenix and Los Angeles.
Petteri Nokelainen scored a short-handed for Anaheim and Cory Perry connected on the power play. Jonas Hiller made 23 saves, after winning 5-2 Saturday night at the Shark Tank and handing San Jose only its fourth regulation loss at home all season.
The Ducks, who had won nine of their previous 10 games, still need four points in their final two against Dallas (2-2-1) and Phoenix (4-1-0) to clinch their fourth straight playoff berth outright. Hiller has started four of five against Phoenix, going 4-0 with a shutout. Jean-Sebastien Giguere has started four of five against Dallas, going 1-2-1.
San Jose received a 5-on-3 power play for a full minute in the third period after Chris Pronger earned a delay-of-game penalty for clearing the puck over the glass halfway through Drew Miller's holding penalty. The Sharks didn't get a shot on net during the two-man advantage, but Cheechoo got his fourth winner with 23 seconds left on Pronger's penalty.
Sharks left-winger Jody Shelley, whose 1,215 penalty minutes since the start of the 2001-02 season are the second-highest total behind Ottawa's Chris Neil, was goaded into a slashing penalty by Anaheim enforcer George Parros at 11:13 of the second period a few seconds after Parros checked Shelley into his own bench.
The Ducks capitalized on the ensuing power play just 18 seconds later, as defenceman Marc-Edouard Vlasic had trouble clearing a bouncing puck deep in the Sharks' zone and Perry whipped his team-high 31st goal past Boucher for a 2-1 lead.
Pavelski tied it at 15:16 of the second with his 24th goal, extending the San Jose's franchise record to 11 straight games with a power-play goal. Rob Niedermayer was serving a charging penalty at the time.
The Sharks, who came in with the best power-play percentage on the road and third-best in the league overall, surrendered their 11th short-handed goal of the season at 5:38 of the first period while Anaheim rookie Andrew Ebbett was serving a holding-the-stick penalty.
Vlasic blocked a clearing pass by Scott Niedermayer while standing on the Anaheim blue line, but Nokelainen got control of the puck and took off on a breakaway. He went for the five hole, and Boucher slid backward into the net with Nokelainen's first career short-handed goal.
Roenick tied it at 2:45 of the second, beating Hiller between the pads with a one-timer from 25 feet in the slot after Joe Thornton set him up from the left corner.
Roenick, who has played on only 12 of San Jose's last 51 games because of shoulder problems, scored his first goal since Dec. 6. It was the 513th of his career, overtaking Gilbert Perreault for 36th place overall.
Notes: Sharks captain and leading goal scorer Patrick Marleau missed his fourth straight game because of an undisclosed lower body injury. ... Boucher faced Anaheim for the first time since was March 28, 2008, when he made 23 saves in a 3-1 victory that snapped a 10-game home winning streak by the Ducks. Evgeni Nabokov was in net for the first five meetings this season and had two shutouts. ... Ryan Getzlaf's assist on Perry's goal made him the third player in Ducks history to record 90 or more points in a season. Teemu Selanne did it four times, and Paul Kariya twice. ... D Kent Huskins, obtained by San Jose from the Ducks along with Travis Moen on March 4, still hasn't played for the Sharks because of the same foot injury that sidelined him for 27 games prior to the trade and eventually required surgery. ... Anaheim is 8-9-0 in the second game of back-to-back sets.


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HANG TIME HEADQUARTERS — The countdown to crazy season in the NBA is on.
Anything goes during crazy season, no rumor is too out there and no anonymous source is out off-limits. That's what adds all of the spice to the process.
The very first and perhaps most intriguing report of the countdown season comes from The Sporting News, which has multiple sources claiming that the Los Angeles Clippers are interested in making a deal for Boston Celtics All-Star Kevin Garnett. The deal would reportedly involve the Clippers giving up promising young point guard Eric Bledsoe and veteran swingman Caron Butler.
The obligatory refuting of these claims are already in full effect, with ESPN's Chris Broussard reporting that there have been no "trade talks" between the Clippers and Celtics, which of course would force someone to come up with a clear-cut definition of "trade talks."
But there is no doubt that both the Clippers, losers of five of their last seven games with injured All-Star point guard Chris Paul on the shelf, and the Celtics, who will finish this season without their own All-Star point guard, Rajon Rondo, are searching for solutions. Sean Deveney of The Sporting News made a compelling case for these two franchises doing business in his initial report:
Celtics officials have made no decisions about the future, even with rookie power forward Jared Sullinger (back surgery) joining point guard Rajon Rondo (knee) last week in seeing their seasons end.
The Celtics came into Sunday's game on a four-game winning streak and will wait until closer to the trade deadline to decide whether to keep this team together. The deadline is 3 p.m. ET Feb. 21.
For now, the Clippers wait on their injured guards. [Chauncey] Billups is working his way back from a foot injury. There is no timetable for the return of star point guard Paul from a knee injury. [Jamal] Crawford, the team's sixth man, is wearing an industrial-strength facemask to protect his broken nose.
The Clippers are a battered bunch and losers of five of their previous seven games.
Coach Vinny Del Negro hopes to get Paul and Billups back during the team's eight-game trip, which runs through Feb. 11.
"Right now, it is all about winning games," Del Negro said. "We need to get guys back to win at a higher rate than we are right now, then we were used to at the beginning of the season. It's a long season; you have to manage it the right way. When we think we're getting everyone back, it seems like, so far this year, someone's been injured. You have to manage that. You have to take the highs and the lows."
But still, the Clippers are going for it. And why not? When healthy, they count themselves among NBA title contenders. That's saying something for a franchise that had long been considered not just among the NBA's worst, but in all of sports.
Considering, too, the struggles of the Los Angeles Lakers, the Clippers see this season as a chance to reach at least the Western Conference finals and in doing so to reorder the hoops hierarchy in LA.
Garnett, who makes his offseason home in Malibu, has a no-trade clause in his contract and two years left on his contract. So there is that one, gigantic hurdle to deal with. He's not going anywhere he doesn't want to go.
But he could have worse options than joining a Clippers team that could be one or two healthy stars away from making a championship run. The championship window in Boston is closed, no matter how hard Celtics coach Doc Rivers tries to fight it.
Garnett has a limited amount of time left to chase a second title to pair with the one he won with the Celtics in 2008. Rolling with the Clippers could be his best and last chance to add to his already Hall of Fame worthy resume. For a player as consumed by winning as Garnett has been his entire career, it would be hard to dismiss an opportunity like this were it actually on the table.
And that brings us back to the core of crazy season in the NBA. No matter how far-fetched an idea seems in theory, the possibilities will get floated to the basketball-loving public between now and the Feb. 21 trade deadline.
The countdown to crazy season in the NBA is on!


text: Michael Huntingdon wrote: > I just sat in on a presentation where both streaming and meta-data were > discussed. Meta-data is the silver bullet in a Luster environment. The > conversation turned to setting up NFS for home/user, with the nearly all > other data on SFS with enough OSS and MDS servers to provide both the > throughput and failover needed. > I think this is how I would design most systems, whether Lustre is the high-performance filesystem or not. Craig > regards > michael > > > At 07:57 PM 12/11/2006, Brian D. Ropers-Huilman wrote: >> On 12/11/06, Craig Tierney <ctierney at hypermall.net> wrote: >>> Lustre supports redundant meta-data servers (MDS) and failover for the >>> object-storage servers (OSS). However, "high-performing" is relative. >>> Great at streaming data, not at meta-data. >> >> Which is really the bane of all cluster file systems, isn't it? Meta >> data accesses kill performance. >> >> -- >> Brian D. Ropers-Huilman >> _______________________________________________ >> Beowulf mailing list, Beowulf at beowulf.org >> To change your subscription (digest mode or unsubscribe) visit >> http://www.beowulf.org/mailman/listinfo/beowulf > > >


text:
- Research article
- Open Access
Most of the extant mtDNA boundaries in South and Southwest Asia were likely shaped during the initial settlement of Eurasia by anatomically modern humans
© Metspalu et al; licensee BioMed Central Ltd. 2004
- Received: 07 May 2004
- Accepted: 31 August 2004
- Published: 31 August 2004
Recent advances in the understanding of the maternal and paternal heritage of south and southwest Asian populations have highlighted their role in the colonization of Eurasia by anatomically modern humans. Further understanding requires a deeper insight into the topology of the branches of the Indian mtDNA phylogenetic tree, which should be contextualized within the phylogeography of the neighboring regional mtDNA variation. Accordingly, we have analyzed mtDNA control and coding region variation in 796 Indian (including both tribal and caste populations from different parts of India) and 436 Iranian mtDNAs. The results were integrated and analyzed together with published data from South, Southeast Asia and West Eurasia.
Four new Indian-specific haplogroup M sub-clades were defined. These, in combination with two previously described haplogroups, encompass approximately one third of the haplogroup M mtDNAs in India. Their phylogeography and spread among different linguistic phyla and social strata was investigated in detail. Furthermore, the analysis of the Iranian mtDNA pool revealed patterns of limited reciprocal gene flow between Iran and the Indian sub-continent and allowed the identification of different assemblies of shared mtDNA sub-clades.
Since the initial peopling of South and West Asia by anatomically modern humans, when this region may well have provided the initial settlers who colonized much of the rest of Eurasia, the gene flow in and out of India of the maternally transmitted mtDNA has been surprisingly limited. Specifically, our analysis of the mtDNA haplogroups, which are shared between Indian and Iranian populations and exhibit coalescence ages corresponding to around the early Upper Paleolithic, indicates that they are present in India largely as Indian-specific sub-lineages. In contrast, other ancient Indian-specific variants of M and R are very rare outside the sub-continent.
- Credible Region
- Tribal Population
- Tribal Group
- Spatial Autocorrelation Analysis
- Coalescence Time
Two mtDNA macro-haplogroups (M and N) that arose from the African haplogroup L3 encompass virtually all mtDNAs outside Africa [1–4]. The phylogenetic node N (including R) has spread its branches all over Eurasia, in contrast to haplogroup M, which is found in Eastern Eurasia but is virtually absent in Europe. The numerous branches of N are, however, generally segregated to either the eastern (e.g. A, B [5], Y [6], R9 [7] or western (e.g. N1 [8, 9], N2 (comprising of W and its sister-clade identified by [10]), TJ, HV, U [11]) Eurasian-specific pools.
The majority of Indian mtDNAs belong to macro-haplogroup M [8, 12–21]. While the topology of the M sub-haplogroups that are common in mainland East Asia (M7, M8 (including C, Z), M9 (including E), D, G [7, 22, 23]) and in Africa (M1 [24]) is established in detail, the internal haplogroup structure of M in India has remained largely undefined. We have previously demonstrated that transitions at nps. 477G, 1780, 8502 and 16319 designate Indian-specific haplogroup M2, the most frequent M clade in India [15]. Another Indian-specific M clade supported by HVS-I variation as well as coding region markers, is M6 [15]. Haplogroups M3, M4 and M5 have been discriminated preliminarily by their characteristic HVS-I mutations [19], but since their defining positions, 16126, 16311, and 16129, respectively, are phylogenetically unstable [25, 26], it is unlikely that the proposed haplogroups are monophyletic. Most numerous sub-groups of macro-haplogroup N in India are the Indian-specific variants of the phylogenetic node R including haplogroups R5, R6, U2(a, b, c) [8, 13, 27].
The overwhelming majority of the Iranian mtDNAs have been shown to lie in the West Eurasian domain of the global human mtDNA pool [27, 28]. Here we focus on the analysis of mtDNA lineages that are shared between Indians and Iranians and bear signals of pre-Holocene expansion in the region.
India congregates four linguistic domains (Indo-European, Dravidic, Austro-Asiatic and Tibeto-Burman) that occupy non-random spheres of the geographic distribution of its populations. The majority of the recent studies based on mtDNA variation have, in contrast to some [21], provided evidence that linguistic groups of India do not represent genetically homogeneous units and are not, therefore, traceable to different immigration waves from distinct sources [8, 13, 19]. The complexity that arises in defining populations and groups of populations in India based on genetic and cultural criteria has been recently demonstrated in South Indian tribal and caste populations. The combined data from mtDNA, Y-chromosome and autosomal genes indicated that the tribes and castes derive largely from the same genetic heritage of Late Pleistocene southern and southwestern Asians, and have received limited gene flow from external sources since the Holocene [15]. Similar results were obtained by Cordaux et al. [29], who demonstrated that caste and tribal groups exhibit similar levels of molecular variance. However, genetic distances indicated that the Tibeto-Burman speaking tribal populations (from eastern India) were more closely related to East Asians than to other Indians [29]. This is consistent with an earlier suggestion placing the origin of these tribal groups east of India – in Tibet and Myanmar [30].
Characteristics of the Indian and Iranian population samples whose mtDNA variation has been determined in the course of this study.
State / Region
Socio-cultural affiliation
Linguistic affiliation
West Bengal
Mixed caste people
80 Million
Uttar Pradesh
Mixed caste people from Cochin
Cochin Jews
(in Israel)
Mixed caste people
50 Million
Konkanastha Brahmin
West Bengal
West Bengal
Sri Lanka
3.4 Million
Mixed caste people
24 Million
5 Million
(in Rajasthan)
Sri Lanka
14.6 Million
Uttar Pradesh
(in India)
Uttar Pradesh
Uttar Pradesh Brahmin
2.5 Million
ca. 68 Million
Results and Discussion
Geographic distribution of macro-clades M and N in India
Geographic, linguistic and socio-cultural distribution of major Indian-specific mtDNA haplogroups
65 (63.1–66.8)
6.3 (5.4–7.3)
2.2 (1.7–2.9)
0.6 (.4–1)
2.9 (2.3–3.6)
2.1 (1.6–2.8)
0.9 (.6–1.4)
11.4 (10.2–12.7)
2.2 (1.7–2.8)
1.3 (.9–1.8)
5.3 (4.5–6.2)
3.4 (2.7–4.2)
10.5 (9.4–11.8)
12.1 (10.9–13.4)
5.3 (3.6–7.8)
0 (0–.8)
0.5 (.1–1.6)
0 (0–.8)
0 (0–.8)
0.2 (.1–1.3)
0 (0–.8)
3.2 (1.9–5.3)
0.9 (.4–2.3)
0 (0–.8)
0.2 (.1–1.3)
13.3 (10.4–16.8)
3.9 (2.5–6.2)
90.8 (87.7–93.2)
Socio-cultural affiliation (Indian data only)
Caste population
57.8 (54.9–60.5)
5.1 (4–6.5)
4.2 (3.2–5.5)
1.1 (.6–1.8)
3 (2.2–4.1)
1.4 (.9–2.2)
1.4 (.9–2.2)
14.1 (12.3–16.2)
3.3 (2.5–4.5)
1.7 (1.1–2.5)
7.8 (6.4–9.5)
4.8 (3.7–6.2)
4 (3–5.2)
17.1 (15.1–19.3)
Tribal population
71.5 (69–73.8)
7.5 (6.2–9)
0.5 (.2–1)
0.2 (0–.5)
2.7 (2–3.7)
2.7 (2–3.7)
0.5 (.2–1)
8.9 (7.5–10.6)
1.2 (.7–1.9)
0.9 (.5–1.6)
3 (2.2–4.1)
2 (1.4–2.9)
16.5 (14.6–18.6)
7.5 (6.2–9)
Brahmins & Kshatriyas
60.1 (54.5–65.3)
1.9 (.9–4.1)
5.1 (3.2–8.1)
1 (.3–2.8)
0.6 (.2–2.3)
1 (.3–2.8)
2.2 (1.1–4.5)
14.7 (11.2–19.1)
4.5 (2.7–7.4)
1.3 (.5–3.2)
6.4 (4.2–9.7)
6.1 (3.9–9.3)
3.5 (2–6.2)
17.6 (13.8–22.2)
Other Castesh
61.5 (57.2–65.6)
6.2 (4.4–8.6)
4.6 (3.1–6.8)
1.2 (.5–2.5)
2.9 (1.8–4.7)
2.1 (1.2–3.8)
0.8 (.3–2)
12.6 (10–15.7)
2.3 (1.3–4)
1.5 (.8–3)
7.4 (5.4–9.9)
3.9 (2.5–5.9)
1.7 (.1–1.4)
16.6 (1.2–3.8)
Language groups of India
57.5 (54.4–60.6)
3.8 (2.8–5.3)
4.5 (3.3–6)
1.0 (.6–1.9)
2.7 (1.9–3.9)
1.1 (.6–2)
1.6 (1–2.6)
7.8 (6.3–9.7)
2.5 (1.7–3.7)
1.6 (1–2.6)
7.1 (5.6–8.9)
6.9 (5.4–8.6)
4.0 (3–5.5)
19.9 (17.5–22.6)
70.0 (67.2–72.7)
10.8 (9.1–12.8)
1.2 (.7–2.1)
0.5 (.2–1.1)
4.0 (2.9–5.3)
1.4 (.9–2.3)
0.5 (.2–1.1)
8.9 (7.4–10.8)
2.7 (1.9–3.9)
1.4 (.9–2.3)
5.9 (4.7–7.5)
1.2 (.7–2.1)
3.0 (2.1–4.2)
8.8 (7.3–10.7)
59.8 (53.6–65.7)
0 (0–1.5)
0.4 (.1–2.2)
0 (0–1.5)
1.6 (.7–4)
1.2 (.4–3.5)
1.2 (.4–3.5)
0.8 (.2–2.9)
1.2 (.4–3.5)
0 (0–1.5)
0.8 (.2–2.9)
2.0 (.9–4.6)
61.4 (55.3–67.3)
3.2 (1.7–6.2)
Austro-Asiatic (Mundari)
86.7 (78.1–92.2)
2.2 (.7–7.7)
0 (0–4)
0 (0–4)
0 (0–4)
26.7 (18.6–36.7)
0 (0–4)
4.4 (1.8–10.9)
0 (0–4)
1.1 (.3–6)
1.1 (.3–6)
1.1 (.3–6)
0 (0–4)
1.1 (.3–6)
Indo-European i
66.4 (60.3–72)
8.0 (5.3–12)
0.4 (.1–2.2)
0.8 (.2–2.8)
4.8 (2.8–8.2)
1.6 (.6–4)
0.4 (.1–2.2)
8.8 (5.9–13)
2.0 (.9–4.6)
1.6 (.6–4)
6.4 (4–10.1)
4.0 (2.2–7.2)
1.2 (.4–3.5)
13.6 (9.9–18.4)
Indo-European j
54.4 (50.7–58)
2.4 (1.5–3.8)
5.9 (4.4–7.9)
1.1 (.6–2.2)
2.0 (1.2–3.3)
1.0 (.5–2)
2.0 (1.2–3.3)
7.4 (5.7–9.6)
2.7 (1.7–4.1)
1.5 (.9–2.7)
7.3 (5.6–9.4)
7.9 (6.1–10.1)
5.0 (3.7–6.9)
22.2 (19.3–25.4)
Indian States and Bangladesh (excluding tribal populations) k
Andhra Pradesh
65.3 (59.1–71.0)
7.7 (5–11.8)
2.8 (1.4–5.7)
1.6 (.6–4.1)
3.3 (1.7–6.4)
4.1 (2.3–7.4)
0 (0–1.5)
18 (13.7–23.2)
4.1 (2.3–7.4)
1.2 (.4–3.5)
5.3 (3.2–8.9)
2.4 (1.1–5.2)
1.2 (.4–3.5)
8.9 (6–13.2)
66.67 (48.6–80.8)
9.96 (3.61–25.7)
0 (.08–11.2)
0 (.08–11.2)
3.29 (.78–16.6)
3.29 (.78–16.6)
0 (.08–11.2)
6.67 (2.04–21.4)
0 (.08–11.2)
0 (.08–11.2)
6.67 (2.04–21.4)
3.29 (.78–16.6)
9.96 (3.61–25.7)
6.67 (2.04–21.4)
40.4 (28.6–53.4)
5.3 (1.9–14.4)
7 (2.9–16.7)
1.8 (.4–9.2)
1.8 (.4–9.2)
1.8 (.4–9.2)
0 (0–6.2)
17.5 (9.9–29.4)
5.3 (1.9–14.4)
1.8 (.4–9.2)
1.8 (.4–9.2)
17.5 (9.9–29.4)
5.3 (1.9–14.4)
33.3 (22.5–46.3)
55.3 (41.2–68.6)
2.1 (.5–11.1)
2.1 (.5–11.1)
0 (.1–7.4)
0 (.1–7.4)
0 (.1–7.4)
0 (.1–7.4)
19.1 (10.5–32.6)
6.4 (2.3–17.2)
0 (.1–7.4)
10.6 (4.7–22.7)
6.4 (2.3–17.2)
0 (.1–7.4)
14.9 (7.5–27.8)
26.3 (11.9–49.1)
0 (.1–16.8)
0 (.1–16.8)
5.3 (1.2–24.9)
10.5 (3.2–31.7)
0 (.1–16.8)
0 (.1–16.8)
10.5 (3.2–31.7)
0 (.1–16.8)
5.3 (1.2–24.9)
10.5 (3.2–31.7)
5.3 (1.2–24.9)
21.1 (8.7–43.7)
31.6 (15.4–54.3)
54 (44.2–63.5)
4 (1.6–9.8)
0 (0–3.6)
0 (0–3.6)
0 (0–3.6)
0 (0–3.6)
5 (2.2–11.2)
25 (17.6–34.3)
9 (4.9–16.2)
1 (.2–5.4)
7 (3.5–13.8)
1 (.2–5.4)
4 (1.6–9.8)
11 (6.3–18.7)
65 (55.9–73)
3.4 (1.4–8.5)
11.1 (6.6–18.1)
0.9 (.2–4.6)
0 (0–3.1)
0 (0–3.1)
5.1 (2.4–10.7)
6.8 (3.5–12.9)
0.9 (.2–4.6)
0.9 (.2–4.6)
4.3 (1.9–9.6)
4.3 (1.9–9.6)
5.1 (2.4–10.7)
23.1 (16.4–31.5)
36 (28.8–44)
0.7 (.2–3.6)
4.7 (2.3–9.3)
1.3 (.4–4.7)
0.7 (.2–3.6)
0 (0–2.4)
2.7 (1.1–6.6)
11.3 (7.2–17.4)
0.7 (.2–3.6)
0.7 (.2–3.6)
7.3 (4.2–12.7)
14 (9.4–20.5)
5.3 (2.8–10.2)
42.7 (35–50.7)
69.4 (53–82)
0 (.1–9.5)
13.9 (6.2–28.8)
0 (.1–9.5)
0 (.1–9.5)
5.6 (1.7–18.2)
0 (.1–9.5)
22.2 (11.8–38.2)
5.6 (1.7–18.2)
2.8 (.7–14.2)
2.8 (.7–14.2)
5.6 (1.7–18.2)
0 (.1–9.5)
2.8 (.7–14.2)
Sri Lanka
57.6 (49–65.7)
6.8 (3.7–12.5)
1.5 (.5–5.3)
1.5 (.5–5.3)
1.5 (.5–5.3)
0.8 (.2–4.1)
0 (0–2.7)
12.9 (8.2–19.7)
0.8 (.2–4.1)
3 (1.2–7.5)
12.1 (7.6–18.8)
2.3 (.8–6.5)
2.3 (.8–6.5)
15.2 (10–22.3)
Tamil Nadu
64.7 (50.9–76.4)
5.9 (2.1–15.9)
3.9 (1.2–13.2)
2 (.5–10.3)
2 (.5–10.3)
2 (.5–10.3)
0 (0–6.8)
11.8 (5.6–23.4)
0 (0–6.8)
3.9 (1.2–13.2)
2 (.5–10.3)
3.9 (1.2–13.2)
23.5 (14–36.8)
7.8 (3.2–18.5)
Uttar Pradesh
52 (42.2–61.7)
1 (.2–5.5)
8.2 (4.2–15.3)
1 (.2–5.5)
0 (0–3.7)
2 (.6–7.1)
1 (.2–5.5)
11.2 (6.4–19)
3.1 (1.1–8.6)
2 (.6–7.1)
15.3 (9.5–23.8)
3.1 (1.1–8.6)
4.1 (1.7–10)
13.3 (8–21.4)
West Bengal
71.7 (62.5–79.4)
3.8 (1.5–9.3)
1.9 (.6–6.6)
0 (0–3.4)
5.7 (2.7–11.8)
0 (0–3.4)
0.9 (.2–5.1)
12.3 (7.3–19.9)
6.6 (3.3–13)
2.8 (1–8)
6.6 (3.3–13)
0.9 (.2–5.1)
0.9 (.2–5.1)
7.5 (3.9–14.2)
With the exception of the diverse set of largely Indian-specific R lineages, the most frequent mtDNA haplogroup in India that derives from the phylogenetic node N is haplogroup W [13]. The frequency peak of haplogroup W is 5% in the northwestern states – Gujarat, Punjab and Kashmir. Elsewhere in India its frequency is very low (from 0 to 0.9%) (Table 2) forming a significant spatial cline (Figure 4).
MtDNA haplogroups in Iran
Over 90% of the mtDNAs found in Iran belong to haplogroups HV, TJ, U, N1, N2 and X, commonly found in West Eurasia (Table 2). In contrast to Europe, where H is predominant among the mtDNA haplogroups, in Iran the frequency of haplogroup U (29%) is higher than that of haplogroup H (17%) (Table 9, see Additional file 4). This difference accounts, at least partly for the presence in Iran of U sub-groups, such as U7 (9.4%), that are virtually absent in Europe.
Compared to India, haplogroup M frequency in Iran is marginally low (5.3%) and there are no distinguished Iranian-specific sub-clades of haplogroup M. All Iranian haplogroup M lineages can be seen as derived from other regional variants of the haplogroup: eleven show affiliation to haplogroup M lineages found in India, twelve in East and Central Asia (D, G, and M8) and one in northeast Africa (M1).
Indian-specific (R5 and Indian-specific M and U2 variants) and East Asian-specific (A, B and East Asian-specific M subgroups) mtDNAs, both, make up less than 4% of the Iranian mtDNA pool. We used Turkey (88.8 ± 4.0%) as the third parental population for evaluating the relative proportions of admixture from India (2.2 ± 1.7%) and China (9.1 ± 4.1%) into Iran. Therefore we can conclude that historic gene flow from India to Iran has been very limited.
The package of the most ancient mtDNA haplogroups in India
Indian-specific sub-clades of mtDNA haplogroups M and R.
COALESCENCE (years)
ρ/nδ2 a
COALESCENCE b (years)
ρ/nδ2 a
Proportion (95% CR)
70,600 ± 21,000
70,100 ± 20,700
6.1 (5.3–7.1)
48,300 ± 20,100
46,700 ± 22,800
2.9 (2.3–3.6)
54,800 ± 25,000
57,600 ± 22,300
3.2 (2.6–3.9)
17,300 ± 7,400
17,300 ± 7,600
3.0f (2.4 – 3.8)
19,200 ± 9,000
19,100 ± 9,000
0.8 (0.5–1.2)
33,000 ± 13,900
30,000 ± 13,600
2.9 (2.3–3.6)
19,100 ± 7,600
15,700 ± 8,100 d
1.4 (1.0–1.9)
6,000 ± 2,100
12,100 ± 4,500
1.4 (1.0–1.9)
9,400 ± 3,200
17,100 ± 4,700
2.1 (1.7–2.7)
19,400 ± 7,200
22,300 ± 8,600
1.0g (0.7 – 1.5)
40,400 ± 14,300
52,500 ± 21,700
0.3 (0.2–0.6)
66,100 ± 22,000
69,800 ± 24,800
2.1 (1.7–2.7)
-12282 AluI
30,000 ± 11,000
30,300 ± 11,600
1.3 (0.9–1.8)
Together with the Indian-specific sub-clades of haplogroup U2 [13, 27], haplogroups M2 and R5 can be discriminated as a package of Indian-specific mtDNA clades harboring extremely deep coalescence times (around 50,000 – 70,000 ybp). Together they constitute nearly 15% of the Indian mtDNAs. Importantly, these haplogroups are virtually absent elsewhere in Eurasia [13, 15], this study]. Because most of Indian varieties of haplogroup M are still unclassified (M*), this package is likely to be extended when more mtDNA coding region information will become available for the M* lineages in India.
The geographic distribution of the M2, R5 and U2i package seems to be rather uniform in the context of the Indian-specific maternal lineages (SAA p > 0.05). When excluding the mtDNAs that are likely to have arrived more recently from West or East Eurasia, the share of the package among the caste populations in northern and southern India is roughly similar (CR: 16.1–24.5% and 19.5–26.0 %, respectively). However, in accordance with expectations from the individual haplogroup distributions, the tribal groups speaking Austro-Asiatic and Tibeto-Burman languages are characterized by considerably lower values (CR: 3.9–14.1% and 3.8–15.1%, respectively).
The quest for finding the origin of haplogroup M and a plausible scenario for the peopling of Eurasia
Based on the high frequency and diversity of haplogroup M in India and elsewhere in Asia, some authors have suggested (versus [3]) that M may have arisen in Southwest Asia [16, 17, 31]. Finding M1 or a lineage ancestral to M1 in India, could help to explain the presence of M1 in Africa as a result of a back migration from India. Yet, to date this has not been achieved [15], this study). Therefore, one cannot rule out the still most parsimonious scenario that haplogroup M arose in East Africa [3]. Furthermore, the lack of L3 lineages other than M and N (indeed, L3M and L3N) in India is more consistent with the African launch of haplogroup M. On the other hand, one also observes that: i) M1 is the only variant of haplogroup M found in Africa; ii) M1 has a fairly restricted phylogeography in Africa, barely penetrating into sub-Saharan populations, being found predominantly in association with the Afro-Asiatic linguistic phylum – a finding that appears to be inconsistent with the distribution of sub-clades of haplogroups L3 and L2 that have similar time depths. That, plus the presence of M1 without accompanying L lineages in the Caucasus [32] and [our unpublished data], leaves the question about the origin of haplogroup M still open.
The improved structure of autochthonous Indian mtDNA clades
Nearly a third of Indian mtDNAs belonging to haplogroup M could be assigned to its existing boughs and limbs with the current knowledge of the mtDNA coding region polymorphisms (Table 9, see Additional file 4). It is likely that the unclassified Indian M* and R* mtDNAs are also to a large extent autochthonous because neither the East nor West Eurasian mtDNA pools include such lineages at notable frequencies.
Coalescence estimates for these Indian-specific mtDNA haplogroups (M3a, M4a, M6, M25 and R6) fall largely between 20,000 and 30,000 ybp. These estimates overlap with those of many West Eurasian-specific (e.g. H, HV, preHV, U3, U4, K, X [9, 34]) and East Eurasian-specific (A, F2, D4, M7c1, M7a1, M8a [7, 22]) mtDNA clades, suggesting a rather synchronic worldwide demographic expansion event in the late Pleistocene, during an interglacial period preceding the LGM.
Several Indian-specific mtDNA clades demonstrate a similar spread-pattern in southern India. We found haplogroups M4a, M6a and M18 in southeastern Tamil Nadu and Andhra Pradesh while they were absent from neighboring Karnataka and Kerala (Figure 1 panel M6a and Figure 2 panels M4a and M18). One possible explanation is that admixture has been facilitated along the coastlines of the Arabian Sea and the Bay of Bengal. On the other hand, because the absolute frequencies of these haplogroups are rather low, it cannot be ruled out that an increase of sample sizes would disrupt the observed spread-pattern.
Were the Austro-Asiatic speaking tribal people the earliest inhabitants of India?
By calculating nucleotide diversities and expansion times (using the method from [37]) for different linguistic groups of India, some previous studies on mtDNA variation have distinguished the Austro-Asiatic speaking tribal groups as the carriers of the genetic legacy of the earliest settlers of the subcontinent [17, 38]. However, because the linguistic groups of India do not cluster into distinct branches of the Indian mtDNA tree [13, 15, 19], this study], calculating the beginning of expansion for those groupings is problematic and likely controversial as well.
Recently, Basu et al. (2003) supported the conclusions of [17, 38] by reporting that the frequency of the ancient haplogroup M2 among the Austro-Asiatic tribal populations is as high as 19%, and that they lack the slightly younger haplogroup M4 (the likely paraphyletic mother-clade of M4a). The authors have regarded the HVS-I transition at np 16319 as sufficient in defining haplogroup M2. This assumption, however, might lead to an overestimation of M2 frequency and age. Indeed, the 16319 transition has arisen several times on the background of other Indian haplogroup M lineages (Table 7, see Additional file 2), more specifically, in mtDNAs lacking the coding region markers that define M2 [15].
Although two out of the four M2 sequences reported by Basu et al. (2003) among the Lodha, Mundas and Santals (HVS-I sequences originally published by [17]) do harbor the characteristic M2a HVS-I motif (16223-16270-16274-16319-16352), without information from the coding region it is not clear whether the other two sequences (HVS-I motifs: 16092-16179-16223-16289-16294-16319 and 16147G-16172-16223-16319) represent novel M2 sub-clades (because these sequences cannot be affiliated with M2a or M2b) or derive from two independent branches of haplogroup M where 16319 transition has arisen recurrently. HVS-I motif 16147G-16172-16223, for example, is commonly associated with haplogroup N1a. Since sequence data on the five M2s among the Austro-Asiatic speaking tribe Ho, reported by Basu et al. (2003), have not been made available in the publication, we cannot rely on their haplogroup classification. Thus, we are left with one Munda and one Santal mtDNA belonging to haplogroup M2. They make up just 5% of the Austro-Asiatic tribal sample of 37 subjects (excluding the ten Ho). Interestingly, we found no instances of haplogroup M2 among the 56 Lodhas analyzed in this study. Consequently, when excluding the recurrences of the 16319 transition on the background of other sequence motifs, the frequency of M2 among the Austro-Asiatic speaking tribal groups from West Bengal in the combined dataset (Table 7, see Additional file 2) is significantly reduced to about 2%. The corrected value is comparable to the M2 frequency (>3%) in tribal populations speaking Indo-European languages of Punjab and Uttar Pradesh, but is significantly lower than its frequency (>14%) among the Dravidic-speaking tribal groups of Andhra Pradesh (Table 8, see Additional file 3).
Language families present today in India, such as Indo-European, Dravidic and Austro-Asiatic, are all much younger than the majority of indigenous mtDNA lineages found among their present-day speakers at high frequencies (see Additional file 9). It would make it highly speculative to infer, from the extant mtDNA pools of their speakers, whether one of the listed above linguistically defined group in India should be considered more "autochthonous" than any other in respect of its presence in the subcontinent.
Additionally, we note that some recent linguistic and archaeological evidence place the spread of the Austro-Asiatic languages in the Neolithic, in conjunction with the dispersal of rice cultivation from the Yangtze River basin [39]. If this were the case, it would imply that the arrival of this linguistic phylum in India was not associated with female gene flow.
Gene flow from West Eurasia
In comparison to an overall frequency of 17% (CR 15.1–19.3%) among the caste populations, only 7% (CR 6.2 – 9.0%) of the mtDNAs from the tribal groups show affiliation to the West Eurasian haplogroups. The observed difference could be caused by differences in the extent of gene flow from the west to different social layers of the Indian society [19], and/or a more pronounced genetic drift among the tribal groups.
Coalescence estimates and diversity values for mtDNA haplogroups U7, W and R2 in India, Central Asia, Near and Middle East.
COALESCENCE (years)
COALESCENCE (years)
COALESCENCE (years)
41,400 ± 15,800
41,200 ± 14,800
34,400 ± 15,500
37,900 ± 11,100
32,000 ± 8,700
27,400 ± 8,300
40,400 ± 14,300
36,100 ± 11,100
25,200 ± 13,300
Similarly to HV, TJ, N1, the spread of U7, W and R2 in India is geographically uneven – the three haplogroups are much more frequent in the northwestern states (SAA p < 0.05, Figure 4). Together, they constitute nearly 14% of the maternal gene pool in Indian Punjab, Gujarat and Rajasthan. Their frequency is also high in Iran (13%) and Pakistan (10%) but declines in Central Asia (5%).
Although haplogroup W is not highly frequent in European populations, it is nevertheless quite common [9], reaching the highest frequencies among the central-northern Finns (9% [40]). Yet, it is virtually absent from the Finno-Ugric speaking populations of the Volga basin [41]. In Central Asia the frequency of haplogroup W stays below 2%.
Many European populations are lacking haplogroup U7 [9], but its frequency climbs over 4% in the Near East and up to 5% in Pakistan, reaching nearly 10% level in Iranians (Table 9, see Additional file 4). In India, haplogroup U7 frequency peaks at over 12% in Gujarat, in the westernmost state of India, while for the whole of India its frequency stays around 2%. Expansion times and haplotype diversities for the Indian and Near and Middle Eastern U7 mtDNAs are strikingly similar (Table 4). The possible homeland of this haplogroup spans likely in Gujarat and Iran because from there its frequency declines steeply both to the east and to the west. If the origin were in Iran rather than in India, then its equally high frequency as well as diversity in Gujarat favors a scenario whereby U7 has been introduced to the coastal western India either very early, or by multiple founders. Notably, the overlap of the Indian and Iranian lineages is largely restricted to the ancestral nodes while the coalescence age estimates for the nearly exclusively Indian (16207-16309-16318T) and West Eurasian (16126-16309-16318T) founder HVS-I motifs both yield time depths of about 20,000 to 30,000 ybp.
Haplogroup R2 appears at low frequencies in Near and Middle East and India and is virtually absent elsewhere. The spread of haplogroup R2 in Europe is restricted to a few populations in the Volga basin where it is represented by nodal haplotypes and by a region-specific subclade characterized by the HVS-I motif 16037–16172 [41]. The coalescence estimate of this sub-clade is 11,400 ± 9,000 ybp. However, its wide error range prevents us from drawing any firm conclusions.
To summarize, the West and South Asian phylogeography of haplogroups W, U7 and R2 can be viewed as a genetic continuum that spans from the Near East into India, extending north into Central Asia. The coalescence times of these haplogroups suggest that this continuum took shape somewhere between 30,000 to 50,000 ybp (Table 4), thus falling within the climatically favorable interglacial period. We notice that the extant U7 and W frequencies along the proposed continuum are not uniform. U7 is more predominant in Iran, Pakistan, northwestern India and the Arabian peninsula, while W is more frequent in the western Near-East, Anatolia and the Caucasus. The coalescence ages of the Indian- and Iranian-specific U7 clades suggest that the time-window of this continuum was closed by ca. 20,000 ybp. The inferred extreme aridity of eastern Iran and western India during the last glacial maximum, which is well documented in paleovegetation reconstructions [42] may explain the observed segregation.
It has been suggested that the Jews settled in southwest India on the coast of the Arabian Sea sometime during the early Middle Ages. However, the mtDNA pool of the extant Cochin Jews is overwhelmingly Indian-specific (Table 10, see Additional file 5). We found exact or close matches to the fourteen HVS-I haplotypes observed among the Cochin Jews in other Indian populations. It is not clear whether the Near Eastern mtDNA lineages have been lost or the initial Jewish settlers did not include women.
Gene flow from East Eurasia
The East Eurasian-specific mtDNA haplogroups are less common in India and more sharply geographically segregated than the haplogroups of western Eurasian ancestry (Table 2; Figure 11, panel C). Indian caste populations harbor only about 4% of such mtDNAs, compared to 17% of the West Eurasian ones (Table 2). Elevated frequencies of haplogroups common in eastern Eurasia are observed in Bangladesh (17%) and Indian Kashmir (21%) and may be explained by admixture with the adjacent populations of Tibet and Myanmar (and possibly further east: from China and perhaps Thailand). On the other hand, the high frequencies of East Eurasian-specific mtDNAs found in the southern Indian state of Tamil Nadu (21%) are unexpected when considering their relatively low frequencies (~1%) in West Bengal and Andhra Pradesh. We notice, however, that the haplogroup assignments used here for the Tamil Nadu sample (A4, B4, F1a and M7) (Table 7, see Additional file 2) are based on HVS-I sequences alone [29]. As shown and discussed elsewhere [7], such type of assignment is prone to mistakes.
Two varieties of haplogroup M, D4c and G2a, were recently identified as largely specific to Central Asia [43]. In spite of geographical proximity we did not find these haplogroups in northern or northwestern India. Haplogroup G2a did, though in marginally low frequency, turn up in Iran (CR: 0.1 – 1.6%) and in southern Indian states Andhra Pradesh (CR: 0.3 – 2.9%) and Sri Lanka (CR: 0.2 – 4.1%).
Tibeto-Burman speaking tribal populations of eastern and northern India exhibit the highest frequencies of East Eurasian-specific mtDNA haplogroups. As inferred from the published HVS-I sequences [29], their share sums up to approximately two thirds of mtDNAs among the tribal groups in Assam, Nagaland, Arunachal Pradesh and Tripura, (Table 8, see Additional file 3; Figure 11, panel D). MtDNA haplogroups native to East Eurasia are also highly frequent in the northern states of India, reaching a peak of nearly 50% among the Kanet of Himachal Pradesh. Papiha and colleagues have previously demonstrated through the typing of immunoglobin allotypes that the Tibetan admixture among the regional Kanet groups decreases as the distance from the Tibetan border increases [44]. Thus, mtDNA data are consistent with an ancestral origin of the Tibeto-Burman speaking tribal populations outside (east of) India in the neighboring Tibet and Myanmar [30, 45].
Haplotype sharing between populations
The majority (70%) of the 1136 mtDNA haplotypes found among continental Indians (including Pakistan and Bangladesh) are singletons and 41% of those that occur more than once are restricted to a single population. Only a few haplotypes are shared among five or more populations.
Proportion of mtDNA haplotype sharing between population groups of South Asia
Group 1
Group 2
pop. group
pop. group
Random 1g
1256 ± 215
617 ± 109
.399 – .453
Random 2g
1426 ± 215
643 ± 114
.396 – .447
132 (112 – 154)
.411 – .467
.324 – .372
107 (90 – 128)
Northern states
.277 – .329
Southern states
.345 – .394
96 (80 – 116)
Western states
.453 – .511
Eastern states
.331 – .379
107 (90 – 128)
.428 – 490
.273 – .317
90 (74 – 109)
.328 – .379
.393 – .446
106 (89 – 127)
AA tribals
.552 – .746
.014 – .025
9 (5 – 17)
TB tribals
.174 – .277
.091 – .115
24 (16 – 35)
.295 – .346
.425 – .487
87 (71 – 106)
340 f
An alternative method that assesses the degree of haplotype sharing between populations is to investigate the combined frequency of the shared haplotypes in two population groups. Thus, amongst the northern and the southern population groups the combined frequency of the haplotypes present also in the other group is significantly lower than that which we observed in the case of random groups. This is not surprising because West Eurasian-specific mtDNA haplogroups are rather frequent in northwest India. Because the Indo-European and the Dravidic speakers of India are largely concentrated to the northern and southern parts of the subcontinent, respectively, the differences arising from geographic division of the Indian populations also correspond to these linguistic groupings (Table 5).
Three Indian-specific haplogroups, M2, U2i and R5, which encompass about 15% of the Indian mtDNA pool, exhibit equally deep coalescence ages of about 50,000 – 70,000 years. Thus, their spread can be associated with the initial peopling of South Asia.
Haplogroups U7, W and R2 harbor a number of similar traits. Their overlapping geographic distributions and coalescence times suggest some degree of genetic continuum in the area spanning from the Near and Middle East through northwest India and reaching north into Central Asia. The coalescence estimates for these haplogroups are equally deep (around 30,000 – 50,000 years) in these different regions. That may be a result of either relatively more recent albeit large in scale migrations that brought along most of the diversity or may indeed reflect the region-specific expansions of these haplogroups. The former explanation could be ruled out since it is impossible to envisage a substantial movement of mtDNAs from South Asia that would not include haplogroup M. The same is true for the opposite – the share of U7, W and R2 within the West Eurasian-specific mtDNA haplogroups is two-fold higher in India than it is in Iran. Moreover, the South- and West Asian-specific sub-branches of haplogroup U7 predate the last glacial maximum. Therefore, deep autochthonous history of these haplogroups in the region remains to be the most parsimonious explanation.
Through the use of mtDNA coding region markers, we were able to classify altogether a quarter of the Indian M and R mtDNAs into a number of Indian-specific mtDNA haplogroups, four of which we newly identified. Several of these are characterized by clear patterns in their geographic distribution and/or different frequencies among different socio-cultural groups of India. Additional efforts should be undertaken to identify new coding region markers in order to further improve the classification of Indian mtDNAs. Indeed, much of the information is still hidden by the poor resolution of the Indian mtDNA tree and further piling of HVS-I datasets would add little to deepen our understanding of the demographic history of South and Southwest Asia.
We found that haplogroup M frequency drops abruptly from about 60% in India to about 5% in Iran, marking the western border of the haplogroup M distribution. A similarly sharp border cuts the distribution of Indian-specific mtDNA haplogroups to the east and to the north of the subcontinent. We therefore propose that the initial mtDNA pool established upon the peopling of South Asia has not been replaced but has rather been reshaped in situ by major demographic episodes in the past and garnished by relatively minor events of gene flow both from the West and the East during more recent chapters of the demographic history in the region.
MtDNA sequence variation in a total of 796 Indian samples most of which are held in a collection at Newcastle University [46] was analyzed. The samples cover a wide geographical range that spans from Himachal Pradesh in the north, Sri Lanka in the south, West Bengal in the east and Gujarat in the west (Table 1).
Tribal populations constitute 15% of the total sample size. The Lodha (n = 56) live mostly in the western part of Midnapore district of West Bengal where they are also known as Kheria or Kharia. Their total population size was ~59000 according to the 1981 census. Their language belongs to the Mundari branch of the Austro-Asiatic language family [47]. The Kanet (n = 37) make up two thirds of the ~50000 inhabitants of the Kinnaur district in Himachal Pradesh [48]. Their language belongs to the Himalayan group of the Tibeto-Burman language family [47]. Five Bhoksa and twenty-six Tharu individuals were included in the present study in addition to those from the same populations that we have previously reported [13]. Most of the Tharu live in the Terai areas (a belt of marshy land at the foot of the Himalayas) of Nepal (n = 720000). Approximately 96000 reside in Uttar Pradesh and Uttaranchal, Indian states adjacent to Nepal. Around 32000 Bhoksas live in the lowland areas of Uttaranchal and the neighboring Bijnor district of Uttar Pradesh. Both these tribal groups speak languages belonging to the Indo-European phylum [47].
The non-tribal Indian samples analyzed contained 105 West Bengalis of different caste rank, 58 Konkanastha Brahmins from Bombay, 53 Gujaratis, 50 Moors and 82 Sinhalese from Sri Lanka, 109 Punjabis of different caste rank from the Punjab, 25 Brahmins from Uttar Pradesh, 35 Rajputs from Rajasthan, 55 Parsees from Maharashtra and 100 subjects from Cochin, Kerala (including 45 Jews who have moved to Israel) (Table 1).
The Iranian sample of 436 individuals was collected in different locations mainly from southwestern and northwestern Iran (Table 1).
The new Indian mtDNA sequence data was combined with that previously published on Indian populations [3, 13, 15, 17, 18, 29, 35, 49–51] to produce a pooled dataset (n = 2572) in which the tribal populations constitute slightly over 50% (Table 6, see Additional file 1). By including also the data on Iranian and published data on Pakistani [13, 27, 29], Bangladeshi [29], Chinese [7, 22, 52–54] and Thai [53, 55, 56] populations (n = 145, 29, 919 and 552, respectively), a dataset of over 4600 samples spanning from West to East Asia (Table 6 and 7, see Additional files 1 and 2) was obtained. In many cases the published data was reanalyzed. Analogy with the newly obtained HVS-I motifs, that were classified into haplogroups using diagnostic markers of the mtDNA coding region, served as basis for haplogroup assignments of the published HVS-I sequences.
An even wider range of data was used for the production of the isofrequency maps: the published data from Armenia (n = 192) [32] and Kyrgyz (n = 95) [57], and the unpublished data from Yemen (n = 118), Oman (n = 79), Saudi Arabia (n = 205), Ethiopia (n = 270), Uzbekistan (n = 114) and Tajikistan (n = 41). Further details about the mtDNA variation of these populations will be published elsewhere.
MtDNA molecular analyses
DNA was extracted using standard phenol-chloroform methods [58]. The hypervariable segment (HVS)-I (between nucleotide positions (np) 16024–16400) of the control region was sequenced in all the 796 Indian and 436 Iranian samples. Preparation of sequencing templates was carried out following Kaessmann et al. [59]. Purified products were sequenced with the DYEnamic™ ET terminator cycle sequencing kit (Amersham Pharmacia Biotech) and analyzed on ABI 377 DNA or Megabace2000 Sequencers. Sequences were aligned and analyzed with the Wisconsin Package (GCG). In addition, informative mtDNA coding region positions [3, 11, 27] were assayed (Table 7, see Additional file 2) in selected individuals from different HVS-I haplotypes to determine haplogroup affiliations.
Data analysis
Median networks [60, 61] were constructed using Network 3.111 and Network 2.10B programs [62] with default settings (r = 2; ε = 0). MtDNA coding region markers were given five times the weight of the HVS-I positions. Coalescence of mtDNA haplogroups and sub-haplogroups was calculated using ρ (the averaged distance to a specified founder haplotype) and a mutation rate of one transition per 20,180 years between nps 16090–16365 [63]. Standard errors for coalescence estimates and efficiency of a sample for coalescence time calculation (ρ/nδ2) were calculated following [64]. Coalescence times were further calculated with the exclusion of tribal populations sequences, yet preserving the cluster topology (coalescence times marked with an asterisk). A more intense genetic drift (particularly founder effects) could introduce a bias into the coalescence time calculation (for example, see below the coalescence time of haplogroup M18 with and without the Lodha sample).
The software kindly provided by Vincent Macaulay was adopted in order to calculate the 95% credible regions (CR) from the posterior distribution of the proportion of a haplogroup/sub-haplogroup in the population.
Haplotype diversity was estimated as
where n is the number of sequences, k the number of distinct haplotypes, and ni number of sequences with one distinct haplotype.
Haplogroup isofrequency maps were generated using the Kriging method in Surfer 7 program of Golden Software. Haplogroup frequencies were averaged over populations from the same state in India, provinces in China and Thailand and geographic divisions in Iran. The data points for Kriging are shown as black dots, while the sample size applicable to the data point is given adjacent to the dot. In relatively small and isolated groups (e.g. tribal groups) random genetic drift might seriously affect the haplogroup frequencies, which may become uninformative when a whole region (e.g. state) is considered (e.g. M18 among the Lodha, see below). Therefore, the tribal data were excluded from the haplogroup isofrequency maps calculation. When illustrating the spread of mtDNA haplogroups native to West Eurasia, East Eurasia and India (Figure 11, panel D) we present these data as pie diagrams. The respective sample size and origin are indicated adjacent to the diagrams.
Spatial autocorrelation analysis was done using the PASSAGE software packet [65, 66]. The correlograms of Moran's I autocorrelation coefficient were calculated using binary weight matrix with five distance classes. Data on tribal populations was not used (see previous paragraph for explanation).
Haplogroup frequencies based admixture proportions were calculated using the ADMIX 2.0 software [67].
Haplotypes were defined as the HVS-I motif combined with the haplogroup label for haplotype analysis. In this way it was possible to discriminate between mtDNAs with identical HVS-I motifs but otherwise known to belong to different haplogroups (e.g. sequences with the CRS motif in HVS-I belonging to haplogroups H or R). In order to assess the results of haplotype sharing analysis between population groups (e.g. tribal and caste populations) we divided the Indian populations ten times randomly into two sets and analyzed the level of haplotype sharing between these sets.
We thank Lluis Quintana-Murci for providing data prior to publication, Vincent Macaulay for the algorithm for calculating credible regions for haplogroup frequencies, and Jaan Lind and Hille Hilpus for technical assistance. This work was supported by Estonian Science Foundation grants 514 (to RV), 5574 (to TK), 5807 (to EM), the Italian Ministry of the University: Progetti Ricerca Interesse Nazionale 2002 and 2003 (to AT), and European Commission grants ICA1CT20070006 and QLG2-CT-2002-90455 (to RV).
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Bunny Ears, Bow Ties at Annual Gay Pride Parade in Tel Aviv
Ripped men in white underwear, pink bunny ears and black bow ties gyrated through the streets of Israel's Tel Aviv on Friday along with drag queens and others to loud trance music for the annual Gay Pride Parade - the biggest event of its kind in the region.
The Tel Aviv Municipality said over 250,000 people celebrated at the city's 20th Gay Pride Parade, an event that draws people from around the world to party.
Cordelia Lange, from Germany, said Tel Aviv is "a very vibrant city, it's a city that embraces everything connected to gays, lesbians and LGBT and I think it's a combination of city at the beach and good vibes."
The good times in Tel Aviv contrasted sharply with events just 70 kilometers (44 miles) to the south, were Israeli soldiers braced for a mass Palestinian rally along the Gaza border. The Islamic militant group that rules Gaza has been leading weekly demonstrations that have turned violent, with 115 Palestinians killed since late March. Israel says it's defending its sovereign border, including nearby communities, and accuses Hamas of trying to carry out attacks under the guise of the mass protests.
Israel has emerged as one of the world's most gay-friendly travel destinations in recent years, in sharp contrast to the rest of the Middle East where gay culture is often not tolerated.
The pervasiveness of religion in everyday life of the region, along with strict cultural norms, plays a major factor in making gay culture taboo. Same-sex relations are punishable by death or prison in Iran and elsewhere.
In Israel, homosexuals serve openly in Israel's military and parliament, and many popular artists and entertainers are homosexual.
Among Palestinians, most homosexuals tend to be secretive about their social lives and some have crossed into Israel to live safely.
Sahreef Awad, an Arab participant from Israel, said "There's no difference between anyone, it's just like, you know, culture, color, nationality, it doesn't matter, come one, it's like, we are all people, that's what matters, love is love, so love wins, yeah!"


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Awareness of any change while looking and feeling one's breasts is an essential way of identifying breast cancer in its early stage and arresting any further harm. Any alteration to how they feel and look requires a consultation with a doctor. Alteration of breasts occurs periodically all through the monthly cycle and throughout a woman's lifespan. Other factors like menarche, age, pregnancy, breast feeding, hormone replacement therapy (HRT) and menopause affect and alter breast development. Taking the time to study one's breasts and knowing what is normal will benefit in being aware of any alterations in order to stop any further damage to the body.
The best way to examine one's breasts is in a shower using either soapy water, talc or a moisturizer which will help move hands more smoothly over the breasts. For women that are big in build, lying down would make it easier to palpate breasts
An ideal check would comprise of palpating breasts every few days as changes occur around the time of monthly periods and might be misleading. Knowing how breasts feel around that time and at other times would set the benchmark for discerning unusual changes. For menopausal women a breast exam should be conducted by themselves as often as possible to check for unusual feeling lumps.
Though there are many breast self exam kits available in the market over the counter, it is still advisable for women that qualify for examination to go for a mammogram if they detect any signs or symptoms like lumps in the breast or seek advice from a GP who might refer the case to a breast clinic that specializes in the detection of a breast condition. A biopsy or ultrasound would help detect or rule out the possibility of a serious condition.