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by Denise Chow, LiveScience The discovery of a fossilized brain in the preserved remains of an extinct "mega-clawed" creature has revealed an ancient nervous system that is remarkably similar to that of modern-day spiders and scorpions, according to a new study. The fossilized Alalcomenaeus is a type of arthropod known as a megacheiran (Greek for "large claws") that lived approximately 520 million years ago, during a period known as the Lower Cambrian. The creature was unearthed in the fossil-rich Chengjiang formation in southwest China. VIDEO: Bugs, Arthropods, and Insects! Oh My! Researchers studied the fossilized brain, the earliest known complete nervous system, and found similarities between the extinct creature's nervous system and the nervous systems of several modern arthropods, which suggest they may be ancestrally related. [Photos of Clawed Arthropod & Other Strange Cambrian Creatures] Living arthropods are commonly separated into two major groups: chelicerates, which include spiders, horseshoe crabs and scorpions, and a group that includes insects, crustaceans and millipedes. The new findings shed light on the evolutionary processes that may have given rise to modern arthropods, and also provide clues about where these extinct mega-clawed creatures fit in the tree of life. "We now know that the megacheirans had central nervous systems very similar to today's horseshoe crabs and scorpions," senior author Nicholas Strausfeld, a professor in the department of neuroscience at the University of Arizona in Tucson, said in a statement. "This means the ancestors of spiders and their kin lived side by side with the ancestors of crustaceans in the Lower Cambrian." © 2013 Discovery Communications, LLC.
Related chapters from BP7e: Chapter 6: Evolution of the Brain and Behavior
Related chapters from MM:None
Link ID: 18804 - Posted: 10.17.2013
by Bob Holmes The great flowering of human evolution over the past 2 million years may have been driven not by the African savannahs, but by the lakes of that continent's Great Rift Valley. This novel idea, published this week, may explain why every major advance in the evolution of early humans, from speciation to the vast increase in brain size, appears to have taken place in eastern Africa. Anthropologists have surmised for several years that early humans, or hominins, might have evolved their unusually large, powerful brains to cope with an increasingly variable climate over the past few million years. However, studies testing this hypothesis have been equivocal, perhaps because most use global or continental-scale measures of climate, such as studying trends in the amount of airborne dust from dry earth that is blown into the ocean and incorporated into deep-sea sediments. Mark Maslin, a palaeoclimatologist at University College London, and his colleague Susanne Shultz at the University of Manchester, UK, have taken a local approach instead, by studying whether the presence or absence of lakes in the Rift Valley affected the hominins living there. Maslin's hunch is that relatively short periods of extreme variability 2.6, 1.8, and 1 million years ago – which are important periods for human evolution – corresponded to times of rapid change in the large lakes of the Great Rift Valley. Because the valley concentrates rainfall from a wide area into relatively small basins, these lakes are unusually sensitive to rainfall and swell or disappear depending on climate. © Copyright Reed Business Information Ltd.
Related chapters from BP7e: Chapter 6: Evolution of the Brain and Behavior
Related chapters from MM:None
Link ID: 18802 - Posted: 10.17.2013
by Erika Engelhaupt Could I interest you in eating the partially digested stomach contents of a porcupine? No? Maybe a spot of reindeer stomach, then. Still no? Well, that's curious. The Western aversion to these dishes is odd, because people around the world have long partaken of — even delighted in — the delicacy known to medical science as chyme. That's what becomes of food after it's chewed, swallowed and mushed around in the stomach for a while with a healthy dose of hydrochloric acid. And, researchers now suggest, Neandertals were no exception. Eating chyme may even explain the presence of some puzzling plant matter found in Neandertal's tartar-crusted teeth. Neandertals didn't have great dental care, and in the last few years anthropologists have begun to take advantage of monstrous tartar buildup on fossilized teeth to figure out what the hominids ate. Various chemical signatures, starch grains and even tiny plant fossils called phytoliths get preserved in the tartar, also known as calculus. Just what Neandertals ate has been more of a puzzle than paleo dieters might have you believe. Isotope analyses of fossilized bones and teeth suggest Neandertals ate very high on the food chain, with high-protein diets akin to those of wolves or hyenas. But wear marks on their teeth suggest the Neandertal diet consisted of more animals in colder high-latitude areas, and more of a mix of plants and animals in warmer areas. Tartar analyses support the idea that Neandertals ate their veggies, and have also suggested the presence of plants considered inedible, or at least unpalatable and non-nutritious. These include some plants like yarrow and chamomile with medicinal value, so one team suggested Neandertals self-medicated. © Society for Science & the Public 2000 - 2013
Related chapters from BP7e: Chapter 6: Evolution of the Brain and Behavior; Chapter 13: Homeostasis: Active Regulation of the Internal Environment
Related chapters from MM:Chapter 9: Homeostasis: Active Regulation of the Internal Environment
Link ID: 18776 - Posted: 10.12.2013
By CARL ZIMMER Evolutionary biologists have come to recognize humans as a tremendous evolutionary force. In hospitals, we drive the evolution of resistant bacteria by giving patients antibiotics. In the oceans, we drive the evolution of small-bodied fish by catching the big ones. In a new study, a University of Minnesota biologist, Emilie C. Snell-Rood, offers evidence suggesting we may be driving evolution in a more surprising way. As we alter the places where animals live, we may be fueling the evolution of bigger brains. Dr. Snell-Rood bases her conclusion on a collection of mammal skulls kept at the Bell Museum of Natural History at the University of Minnesota. Dr. Snell-Rood picked out 10 species to study, including mice, shrews, bats and gophers. She selected dozens of individual skulls that were collected as far back as a century ago. An undergraduate student named Naomi Wick measured the dimensions of the skulls, making it possible to estimate the size of their brains. Two important results emerged from their research. In two species — the white-footed mouse and the meadow vole — the brains of animals from cities or suburbs were about 6 percent bigger than the brains of animals collected from farms or other rural areas. Dr. Snell-Rood concludes that when these species moved to cities and towns, their brains became significantly bigger. Dr. Snell-Rood and Ms. Wick also found that in rural parts of Minnesota, two species of shrews and two species of bats experienced an increase in brain size as well. Dr. Snell-Rood proposes that the brains of all six species have gotten bigger because humans have radically changed Minnesota. Where there were once pristine forests and prairies, there are now cities and farms. In this disrupted environment, animals that were better at learning new things were more likely to survive and have offspring. © 2013 The New York Times Company
Related chapters from BP7e: Chapter 6: Evolution of the Brain and Behavior; Chapter 2: Functional Neuroanatomy: The Nervous System and Behavior
Related chapters from MM:Chapter 2: Cells and Structures: The Anatomy of the Nervous System
Link ID: 18555 - Posted: 08.24.2013
By Jessica Shugart Sometimes it pays to be mediocre. A new study shows that sheep with a 50/50 blend of genes for small and big horns pass along more of their genes over a lifetime than their purely big-horned brethren, who mate more often. The finding offers rare insight into an enduring evolutionary paradox—why some traits persist despite creating a reproductive disadvantage. The results, published online August 21 in Nature, reveal that while big-horned sheep mated most successfully each season, small-horned sheep survived longer. Rams who inherited one of each type of gene from their parents got the best of both worlds: they lived longer than bigger-horned sheep and mated more successfully than those with the smallest horns. As a result, middle-of-the-road sheep passed on more of their genes over time. "They're the fittest of them all," says Jon Slate of the University of Sheffield in Scotland, who led the study. "This is a marvelous combination of using the most modern tools available to confirm classic older views of sexual selection," says evolutionary geneticist Allen Moore of the University of Georgia in Athens, who was not involved in the study. Traits such as bold peacock feathers and giant antlers evolved to garner the attention of prospective females and boost reproductive success. Yet if each generation of females continues to pick the most stellar males, Charles Darwin wondered, how do sub-par versions of a trait continue to persist? "It's something that has preoccupied evolutionary biologists ever since," Slate says. © Society for Science & the Public 2000 - 2013
Related chapters from BP7e: Chapter 6: Evolution of the Brain and Behavior; Chapter 12: Sex: Evolutionary, Hormonal, and Neural Bases
Related chapters from MM:Chapter 8: Hormones and Sex
Link ID: 18550 - Posted: 08.22.2013
By Melissa Hogenboom Science reporter, BBC News Several ancient dinosaurs evolved the brainpower needed for flight long before they could take to the skies, scientists say. Non-avian dinosaurs were found to have "bird brains", larger than that of Archaeopteryx, a 150 million-year-old bird-like dinosaur. Once regarded as a unique transition between dinosaurs and birds, scientists say Archaeopteryx has now lost its pivotal place. The study is published in Nature. A recent discovery in China which unveiled the earliest creature yet discovered on the evolutionary line to birds, also placed Archaeopteryx in less of a transitional evolutionary place. Bird brains tend to be more enlarged compared to their body size than reptiles, vital for providing the vision and coordination needed for flight. Scientists using high-resolution CT scans have now found that these "hyper-inflated" brains were present in many ancient dinosaurs, and had the neurological hardwiring needed to take to the skies. This included several bird-like oviraptorosaurs and the troodontids Zanabazar junior, which had larger brains relative to body size than that of Archaeopteryx. This latest work adds to previous studies which found the presence of feathers and wishbones on ancient dinosaurs. BBC © 2013
Related chapters from BP7e: Chapter 6: Evolution of the Brain and Behavior; Chapter 11: Motor Control and Plasticity
Related chapters from MM:Chapter 5: The Sensorimotor System
Link ID: 18439 - Posted: 08.01.2013
Andrew Curry In the 1970s, archaeologist Peter Bogucki was excavating a Stone Age site in the fertile plains of central Poland when he came across an assortment of odd artefacts. The people who had lived there around 7,000 years ago were among central Europe's first farmers, and they had left behind fragments of pottery dotted with tiny holes. It looked as though the coarse red clay had been baked while pierced with pieces of straw. Looking back through the archaeological literature, Bogucki found other examples of ancient perforated pottery. "They were so unusual — people would almost always include them in publications," says Bogucki, now at Princeton University in New Jersey. He had seen something similar at a friend's house that was used for straining cheese, so he speculated that the pottery might be connected with cheese-making. But he had no way to test his idea. The mystery potsherds sat in storage until 2011, when Mélanie Roffet-Salque pulled them out and analysed fatty residues preserved in the clay. Roffet-Salque, a geochemist at the University of Bristol, UK, found signatures of abundant milk fats — evidence that the early farmers had used the pottery as sieves to separate fatty milk solids from liquid whey. That makes the Polish relics the oldest known evidence of cheese-making in the world1. © 2013 Nature Publishing Group
Related chapters from BP7e: Chapter 6: Evolution of the Brain and Behavior; Chapter 7: Life-Span Development of the Brain and Behavior
Related chapters from MM:Chapter 13: Memory, Learning, and Development
Link ID: 18438 - Posted: 08.01.2013
John Hawks Humans are known for sporting big brains. On average, the size of primates' brains is nearly double what is expected for mammals of the same body size. Across nearly seven million years, the human brain has tripled in size, with most of this growth occurring in the past two million years. Determining brain changes over time is tricky. We have no ancient brains to weigh on a scale. We can, however, measure the inside of ancient skulls, and a few rare fossils have preserved natural casts of the interior of skulls. Both approaches to looking at early skulls give us evidence about the volumes of ancient brains and some details about the relative sizes of major cerebral areas. For the first two thirds of our history, the size of our ancestors' brains was within the range of those of other apes living today. The species of the famous Lucy fossil, Australopithecus afarensis, had skulls with internal volumes of between 400 and 550 milliliters, whereas chimpanzee skulls hold around 400 ml and gorillas between 500 and 700 ml. During this time, Australopithecine brains started to show subtle changes in structure and shape as compared with apes. For instance, the neocortex had begun to expand, reorganizing its functions away from visual processing toward other regions of the brain. The final third of our evolution saw nearly all the action in brain size. Homo habilis, the first of our genus Homo who appeared 1.9 million years ago, saw a modest hop in brain size, including an expansion of a language-connected part of the frontal lobe called Broca's area. The first fossil skulls of Homo erectus, 1.8 million years ago, had brains averaging a bit larger than 600 ml. © 2013 Scientific American
Related chapters from BP7e: Chapter 6: Evolution of the Brain and Behavior; Chapter 1: Biological Psychology: Scope and Outlook
Related chapters from MM:Chapter 1: An Introduction to Brain and Behavior
Link ID: 18418 - Posted: 07.29.2013
By Cristy Gelling Lemur species that live in large groups can tell when to steal food from a competitor in a lab experiment, researchers report June 26 in PLOS ONE. The finding supports the idea that brainpower in primates evolved to fit their complex social lives. Because the sneakier lemurs don't have bigger brains than less sneaky ones living in smaller groups, researchers suggest that social smarts don't always depend on brain size. Much of the evidence for sociality's role in the evolution of intelligence comes from indirect measures such as brain size, says study coauthor Evan MacLean of Duke University. But brain size does not always correspond to brainpower, so MacLean uses behavioral tests. He and his colleagues tested the social intelligence of six species of lemur, primates from Madagascar distantly related to monkeys and apes. Each of the species lives in social groups ranging from families of just three, mongoose lemurs' preferred posse, to gangs of about 16, a typical size for a group of ring-tailed lemurs. The researchers trained lemurs to view humans as competitors for food, then presented the animals with a choice between pilfering treats from one of two people: one facing the animals or another with his or her back turned. Species that live in small groups reached for the food under a competitor's nose as often as they did behind people's backs. But the ring-tailed lemurs were much more likely to choose the unguarded food. © Society for Science & the Public 2000 - 2013
Related chapters from BP7e: Chapter 6: Evolution of the Brain and Behavior; Chapter 15: Emotions, Aggression, and Stress
Related chapters from MM:Chapter 11: Emotions, Aggression, and Stress
Link ID: 18324 - Posted: 06.29.2013
Sid Perkins Sporting feats such as baseball's 100-mile-per-hour fastball are made possible by a suite of anatomical features that appeared in our hominin ancestors about 2 million years ago, a video study of college athletes suggests. And this ability to throw projectiles may have been crucial for human hunting, which in turn may have had a vital role in our evolution. "Throwing projectiles probably enabled our ancestors to effectively and safely kill big game," says Neil Roach, a biological anthropologist at George Washington University in Washington DC, who led the work. Eating more calorie-rich meat and fat would have helped early hominins' brains and bodies to grow, enabling our ancestors to expand into new regions of the world, he suggests. The study is published today in Nature1. Although some primates occasionally throw objects, and with a fair degree of accuracy, only humans can routinely hurl projectiles with both speed and accuracy, says Roach. Adult male chimpanzees can throw objects at speeds of around 30 kilometres per hour, but even a 12-year-old human can pitch a baseball three times faster than that, he notes. In fact, the quickest motion that the human body produces — rotation of the humerus, the long bone in the upper arm, at a rate that is briefly equivalent to 25 full rotations in a single second — occurs while a person is throwing a projectile. © 2013 Nature Publishing Group,
Related chapters from BP7e: Chapter 6: Evolution of the Brain and Behavior; Chapter 11: Motor Control and Plasticity
Related chapters from MM:Chapter 5: The Sensorimotor System
Link ID: 18323 - Posted: 06.29.2013
By Melissa Hogenboom Science reporter, BBC News The social brain theory - that animals in large social groups have bigger brains - has now been supported by a computer model. For animals in smaller social groups, the cost of having a large brain outweighs the benefits. Scientists used a simulation modelling technique to confirm that large social groups are only possible through sophisticated communication. The study is published in Proceedings of the Royal Society B. The human brain is a very costly organ which consumes a lot of energy. Animals that live in small social groups could therefore be at a disadvantage if they had large brains taking up processing power that could better be used elsewhere. A team at Oxford University has now looked at the cognitive demands of making social decisions using a method called agent-based modelling, which models simplified representations of reality. As expected, they found that more complex social decisions take up more 'brain' power. The cognitive complexity of language evolved as social groups became larger and more complex, said lead author of the study Tamas David-Barrett from the University of Oxford. He explained that a group of five is an ideal number to coordinate an event such as a hunt, but as the group size increases, the coordination involved would become increasingly complex. BBC © 2013
Related chapters from BP7e: Chapter 15: Emotions, Aggression, and Stress; Chapter 6: Evolution of the Brain and Behavior
Related chapters from MM:Chapter 11: Emotions, Aggression, and Stress
Link ID: 18312 - Posted: 06.26.2013
Sid Perkins The near-complete fossil of a tiny creature unearthed in China in 2002 has bolstered the idea that the anthropoid group of primates — whose modern-day members include monkeys, apes and humans — had appeared by at least 55 million years ago. The fossil primate does not belong to that lineage, however: it is thought to be the earliest-discovered ancestor of small tree-dwelling primates called tarsiers, showing that even at this early time, the tarsier and anthropoid groups had split apart. The slender-limbed, long-tailed primate, described today in Nature1, was about the size of today's pygmy mouse lemur and would have weighed between 20 and 30 grams, the researchers estimate. The mammal sports an odd blend of features, with its skull, teeth and limb bones having proportions resembling those of tarsiers, but its heel and foot bones more like anthropoids. "This mosaic of features hasn't been seen before in any living or fossil primate," says study author Christopher Beard, a palaeontologist at the Carnegie Museum of Natural History in Pittsburgh, Pennsylvania. By analysing almost 1,200 morphological aspects of the fossil and comparing them to those of 156 other extant and extinct mammals, the team put the ancient primate near the base of the tarsier family tree. The creature is dubbed Archicebus achilles, in which the genus name Archicebus roughly translates as 'original long-tailed monkey', while the species name achilles is a wry nod to the primate's anthropoid-like heel bone. © 2013 Nature Publishing Group
Related chapters from BP7e: Chapter 6: Evolution of the Brain and Behavior
Related chapters from MM:None
Link ID: 18240 - Posted: 06.06.2013
By Rachel Ehrenberg The salad days of human evolution saw a dietary shift toward grasses and probably grass-fed animals, analyses of more than 100 fossilized teeth from eight species of ancient hominids indicate. "These changes in diet have been predicted," says paleoanthropologist Richard Klein of Stanford University. "But it's very nice to have some data, and these data support it very strongly." Changes in the size and shape of jaws and teeth in both ancient hominids and their ape relatives point to changes in diet. The new study adds to these lines of anatomical evidence chemical analyses that look at different forms of carbon in the fossilized teeth. The ratio of two types of carbon in tooth enamel reflects diet, says geochemist Thure Cerling of the University of Utah, who spent weeks in a vault in the National Museum of Kenya collecting milligram-sized samples of tooth enamel for the analyses. Grasses, grasslike sedges and many other plants in hot, arid environments have evolved a trick that helps prevent water loss. The metabolic adjustment results in taking up more of a heavier form of carbon, known as carbon-13, than most trees and shrubs do. The tooth studies, which cover more than 3 million years and include specimens from southern, eastern and central Africa, found greater quantities of this heavier carbon in hominids that are closer to humans on the evolutionary tree. This pattern suggests that, compared with humans' more ancient relatives, recent ones were eating more grass or more grass-feeding animals, like zebras. The analyses appear June 3 in the Proceedings of the National Academy of Sciences. © Society for Science & the Public 2000 - 2013
Related chapters from BP7e: Chapter 6: Evolution of the Brain and Behavior; Chapter 13: Homeostasis: Active Regulation of the Internal Environment
Related chapters from MM:Chapter 9: Homeostasis: Active Regulation of the Internal Environment
Link ID: 18224 - Posted: 06.04.2013
by Michael Balter From the human perspective, few events in evolution were more momentous than the split among primates that led to apes (large, tailless primates such as today's gorillas, chimpanzees, and humans) and Old World monkeys (which today include baboons and macaques). DNA studies of living primates have estimated that the rift took place between 25 million and 30 million years ago, but the earliest known fossils of both groups date no earlier than 20 million years ago. Now, a team working in Tanzania has found teeth and partial jaws from what it thinks are 25-million-year-old ancestors of both groups. If the interpretations hold up, the finds would reconcile the molecular and fossil evidence and possibly provide insights into what led to the split in the first place. Researchers have long been frustrated by a paucity of fossils from this key period in evolution, which sits at the borderline between two major geological epochs: the Miocene (about 23 million to 5 million years ago) and the Oligocene (about 34 million to 23 million years ago). The earliest known fossils of early apes and Old World monkeys date from the early Miocene and have been found in just a handful of sites in Kenya, Uganda, and North Africa. Meanwhile, molecular studies of existing primates consistently suggest that these two groups arose during the Oligocene, leading scientists to wonder whether the molecular dates are wrong or if paleontologists have been looking in the wrong places. For more than a decade, researchers from the United States and Tanzania have been combing Tanzania's Rukwa Rift Basin, searching for fossils of all kinds. During the 2011 and 2012 seasons, a team led by Nancy Stevens, a vertebrate paleontologist at Ohio University in Athens, discovered fossils that it identified as belonging to two previously unknown species of primates: one, an apparent ape ancestor the team has named Rukwapithecus fleaglei; the other, a claimed Old World monkey ancestor dubbed Nsungwepithecus gunnelli. © 2010 American Association for the Advancement of Science.
Related chapters from BP7e: Chapter 6: Evolution of the Brain and Behavior
Related chapters from MM:None
Link ID: 18163 - Posted: 05.16.2013
By Bruce Bower Human ancestors living in East Africa 2 million years ago weren't a steak-and-potatoes crowd. But they had a serious hankering for gazelle meat and antelope brains, fossils discovered in Kenya indicate. Three sets of butchered animal bones unearthed at Kenya's Kanjera South site provide the earliest evidence of both long-term hunting and targeted scavenging by a member of the human evolutionary family, anthropologist Joseph Ferraro of Baylor University in Waco, Texas, and his colleagues conclude. An early member of the Homo genus, perhaps Homo erectus, hunted small animals and scavenged predators' leftovers of larger creatures, researchers report April 25 in PLOS ONE. Along with hunting relatively small game such as gazelles, these hominids scavenged the heads of antelope and wildebeests, apparently to add a side of fatty, nutrient-rich brain tissue to their diets, the scientists say. Those dietary pursuits could have provided the extra energy Homo erectus needed to support large bodies, expanded brains and extensive travel across the landscape, Ferraro says. A few East African sites dating to as early as 3.4 million years ago had previously produced small numbers of animal bones bearing butchery marks made by stone tools. Scientists think those bones indicate occasional meat eating (SN: 9/11/10, p. 8). Now Kanjera South has yielded several thousand complete and partial animal bones, representing at least 81 individual animals. A known reversal of Earth's magnetic field preserved in an excavated soil layer allowed Ferraro's team to determine the age of the finds, which accumulated over a few thousand years at most. © Society for Science & the Public 2000 - 2013
Related chapters from BP7e: Chapter 13: Homeostasis: Active Regulation of the Internal Environment; Chapter 6: Evolution of the Brain and Behavior
Related chapters from MM:Chapter 9: Homeostasis: Active Regulation of the Internal Environment
Link ID: 18113 - Posted: 05.04.2013
By Kate Wong Odds are you carry DNA from a Neandertal, Denisovan or some other archaic human. Just a few years ago such a statement would have been virtually unthinkable. For decades evidence from genetics seemed to support the theory that anatomically modern humans arose as a new species in a single locale in Africa and subsequently spread out from there, replacing archaic humans throughout the Old World without mating with them. But in recent years geneticists have determined that, contrary to that conventional view, anatomically modern Homo sapiens did in fact interbreed with archaic humans, and that their DNA persists in people today. In the May issue of Scientific American, Michael Hammer of the University of Arizona in Tucson examines the latest genetic findings and explores the possibility that DNA from these extinct relatives helped H. sapiens become the wildly successful species it is today. As Scientific American's anthropology editor, I have an enduring interest in the rise of H. sapiens; and as longtime readers of this blog may know, I'm fascinated (you might even say obsessed) with Neandertals. So naturally I've been keen to find out how much, if any, Neandertal DNA I have in my own genome. Several consumer genetic testing companies now test for Neandertal genetic markers as part of their broader ancestry analysis, and after 23andMe lowered the price of their kit to $99 in December, I decided to take the plunge. As it happens, National Geographic's Genographic Project had recently updated their own genetic test to look for Neandertal DNA, and they sent me a kit (retail price: $299) for editorial review, much as publishers do with new books. And so it was on a chilly Saturday in late January that I found myself spitting into a test tube for 23andMe and swabbing my cheek for the Genographic Project. © 2013 Scientific American
Related chapters from BP7e: Chapter 6: Evolution of the Brain and Behavior; Chapter 7: Life-Span Development of the Brain and Behavior
Related chapters from MM:Chapter 13: Memory, Learning, and Development
Link ID: 18057 - Posted: 04.23.2013
Sid Perkins The two-million-year-old remains of a novel hominin discovered in August 2008 are an odd blend of features seen both in early humans and in the australopithecines presumed to have preceded them. A battery of six studies1–6 published today in Science scrutinizes the fossils of Australopithecus sediba from head to heel and yields unprecedented insight into how the creature walked, chewed and moved. Together, the studies suggest that this hominin was close to the family tree of early humans — although it remains controversial whether it was one of our direct ancestors. "We see evolution in action across this skeleton," says Lee Berger, a palaeoanthropologist at the University of the Witwatersrand in Johannesburg, South Africa. For instance, whereas the creature's arms are ape-like, its hands and wrists are remarkably like those of humans. And although the hominin's pelvis is shaped like a modern human's, its torso included a narrow upper rib cage like those found in apes. One of the six studies focused on Au. sediba's teeth1, comparing 22 different aspects across hundreds of teeth from several other species of australopithecines and thousands of early human teeth. Tooth similarities among the species are more likely to signify common ancestry than independent evolution towards a beneficial design, says Debbie Guatelli-Steinberg, an anthropologist at Ohio State University in Columbus. That's because most of the characteristics the team chose to study, such as the subtle curvature of a portion of the tooth's surface, are not likely to be evolutionarily useful. © 2013 Nature Publishing Group
Related chapters from BP7e: Chapter 6: Evolution of the Brain and Behavior
Related chapters from MM:None
Link ID: 18027 - Posted: 04.13.2013
by Michael Marshall Neanderthals may have had bigger eyes than modern humans, but while this helped them see better, it may have meant that they did not have brainpower to spare for complex social lives. If true, this may have been a disadvantage when the ice age reduced access to food, as they would not have had the skills to procure help from beyond their normal social group, speculates Robin Dunbar at the University of Oxford. Neanderthals' brains were roughly the same size as modern humans, but may have been organised differently. To find out, a team led by Dunbar studied the skulls of 13 Neanderthals and 32 anatomically modern humans. The Neanderthals had larger eye sockets. There are no Neanderthal brains to examine, but primates with larger eyes tend to have larger visual systems in their brains, suggesting Neanderthals did too. Their large bodies would also have required extra brain power to manage. Together, their larger eyes and bodies would have left them with less grey matter to dedicate to other tasks. Neanderthals may have evolved enhanced visual systems to help them see in the gloom of the northern hemisphere, Dunbar says. "It makes them better at detecting things in grim, grey conditions." As a by-product of larger eyes, they may not have been able to expand their frontal lobes – a brain area vital for social interaction – as much as modern humans. As a result, Dunbar estimates they could only maintain a social group size of around 115 individuals, rather than the 150 that we manage. © Copyright Reed Business Information Ltd.
Related chapters from BP7e: Chapter 6: Evolution of the Brain and Behavior; Chapter 10: Vision: From Eye to Brain
Related chapters from MM:Chapter 7: Vision: From Eye to Brain
Link ID: 17896 - Posted: 03.13.2013
by Michael Marshall Humans aren't built for giving birth. Babies' heads are big to accommodate their big brains, but the mother's hips are small because they walk upright. As a result, birth takes hours and is extremely painful – and midwives almost always help out. Other animals may find birth difficult, particularly if the babies have been gestating for a long time and have grown large. Nevertheless, most mammals have it easier than humans. Monkeys give birth in less than ten minutes. So it is a surprise that female black snub-nosed monkeys may be assisted by "midwives" when they give birth. This behaviour has only been seen once in this species, but it suggests that it's not just human mothers that need help giving birth. Black snub-nosed monkeys live in societies called bands, which can be over 400 strong. Each is divided into smaller groups of around 10 monkeys. Most groups contain one male and several females plus offspring, but there are also all-male groups. Wen Xiao of Dali University in Yunnan, China, and colleagues have been observing black snub-nosed monkeys in the province for years, but had never seen one give birth: the monkeys normally deliver at night. Then on 18 March last year, they got lucky. © Copyright Reed Business Information Ltd.
Related chapters from BP7e: Chapter 6: Evolution of the Brain and Behavior; Chapter 1: Biological Psychology: Scope and Outlook
Related chapters from MM:Chapter 1: An Introduction to Brain and Behavior
Link ID: 17781 - Posted: 02.11.2013
By Erin Wayman The story of the Neandertals may need a new ending, a controversial study suggests. Using improved radiocarbon methods, scientists redated two of the youngest known Neandertal cave sites and concluded that they are at least 10,000 years older than previous studies have found. The findings cast doubt on the reliability of radiocarbon dates from other recent Neandertal sites, the researchers suggest online February 4 in the Proceedings of the National Academy of Sciences. This means the last Neandertals might have died out much earlier than previously thought, which could cause anthropologists to rethink how and why these hominids vanished. Researchers have long debated whether the harsh Ice Age climate, the appearance of modern humans migrating out of Africa, or some other factor drove Neandertals to extinction. "The paper is simply excellent," says archaeologist Olaf Jöris of the Romano-Germanic Central Museum in Mainz, Germany. The new research supports Jöris' own review of Neandertal dates, in which he concluded that the most-recent Neandertals probably lived around 42,000 years ago. The standard view suggests that the last of these hominids occupied Europe as recently as about 28,000 years ago. But other archaeologists are not convinced by the new work. "We shouldn't get too carried away over results that amount to a few radiocarbon dates from two sites," says Paul Pettitt, an archaeologist at Durham University in England. © Society for Science & the Public 2000 - 2013
Related chapters from BP7e: Chapter 6: Evolution of the Brain and Behavior
Related chapters from MM:None
Link ID: 17758 - Posted: 02.05.2013


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Osijek has a well-earned reputation for being the most relaxing of Croatian cities. Its inhabitants are notoriously laid-back and welcoming, its city centre is full of parks, and the stick-around-a-while-longer quality of its cafes and restaurants stands as a warning to anyone who thinks that life is there to be hurried. Walking is a pleasure rather than a burden in a predominantly low-rise centre whose skyline is periodically punctured by some unusual architectural treat. And if you're not much of a walker, then don't forget that the flat nature of the terrain ensures that Osijek is very much a bicycle-lovers' kind of place. It is also the only city outside Zagreb that has a tram network, with two lines that take you almost everywhere you need to go in the city centre.
Osijek is very much the capital of the Croatian southeast, an up-and-coming tourism centre that is increasingly well celebrated for its distinctive local cuisine and boutique wineries. There are also signs that Osijek is beginning to punch its cultural weight: the Croatian National Theatre in Osijek's ground-breaking production of Unterstadt, staged in the open air during the Osijek Cultural Summer, won the best production in 2012's Teatr.hr awards; while Osijek-based design team Lega-Lega are becoming a major nationwide retailer of T-Shirts and stationery, opening shops in Dubrovnik and Zagreb.

subset: commoncrawl


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For issues related to testing applications written using Microsoft Silverlight.
1k views
How to deserialize and correlate Binary Large Objects in Load Runner 11.5 Web Custom Request?
I'm working on a Silverlight based application. We've recorded the scripts using Load Runner 11.5 and while replaying to find dynamic values for correlation, we found out that the entire data in a ...


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Estee Lauder Companies, Inc. (EL) Ex-Dividend Date Scheduled for February 26, 2014
Estee Lauder Companies, Inc. ( EL ) will begin trading ex-dividend on February 26, 2014. A cash dividend payment of $0.2 per share is scheduled to be paid on March 17, 2014. Shareholders who purchased EL stock prior to the ex-dividend date are eligible for the cash dividend payment. This represents an 11.11% increase over the same period a year ago.
The previous trading day's last sale of EL was $67.9, representing a -10.39% decrease from the 52 week high of $75.77 and a 10.77% increase over the 52 week low of $61.30.
EL is a part of the Consumer Non-Durables sector, which includes companies such as Procter & Gamble Company ( PG ) and Unilever PLC ( UL ). EL's current earnings per share, an indicator of a company's profitability, is $2.54. Zacks Investment Research reports EL's forecasted earnings growth in 2014 as 8.24%, compared to an industry average of 2%.
For more information on the declaration, record and payment dates, visit the EL Dividend History page. Our Dividend Calendar has the full list of stocks that have an ex-dividend today.


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text:
Michael Spalter eBooks
eBooks found: 1
Real World IPv6
Michael Spalter & Michael Boone's Spalter
SEG Communications, August 2012
Format: PDF, ePub
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Funding via Thanh Hoa Microfinance Institution Limited Liability
In 2002, Thuý joined TYM Fund to gain access to financial services to help improve her living situation and her ability to engage in business activities. Thuý has successfully repaid a previous loan from TYM Fund. This previous loan was used to purchase breeding livestock.
Thuý is now requesting a new loan of 20,000,000 VND which will be used to purchase baby livestock to raise and sell. The loan will be the 6th loan taken out by the borrower from TYM Fund. Thuý plans to use the additional revenue generated from the business to improve the general living conditions of her family.
Success!! The loan was 100% repaid


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When changing the air filter, it is easy to knock off a hose on the air filter housing, which is part of the emission control system. If the vehicle is driven with this hose disconnected, the Check Engine Light will illuminate.
RepairPal Verified
Average mileage: 208,000 (208,000–208,000)
Engine affected: 3.0L V6
2 people reported this problem


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SN's Top 25 Worldwide Food Retailers Data: Global Headquarters: France Top Executive: Vianney Mulliez Number of Stores: 3,317 Sales: $60.3 billion (U.S.) Website: www.auchan.fr AREAS OF OPERATION Angola, China, France, Hungary, India (entered in September 2012), Italy, Luxembourg, Poland, Portugal, Romania, Russia, Spain, Taiwan, Ukraine
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Mirro Bread Maker Dough kneading Mixer Hand Crank
- Sold for: Start Free Trial or Sign In to see what it's worth.
- Item Category: Furniture & Furnishings
- Source: eBay
- Sold Date: Apr 15, 2007
- Channel: Auction House
THANK YOU FOR LOOKING IN TODAY IS WHAT WE HAVE UP FOR AUCTION If you Love making your OWN bread...this one is for you!!! MIRRO HAND CRANK BREAD/DOUGH MIXER Mix and knead your dough in this large aluminum bowl with a hand crank stainless steel dough hook. Make bread the way Grandma did. You don't have to get your hands full of flour and stick with dough...just turn the crank to make your favorite recipe for bread. Make your own breads, rolls, coffee cakes and know what goes into your dough!!! Any recipe for kneaded dough can easily be used... at home, in the RV or when you're camping. Bake it or freeze it! This is in GREAT condition. Looks like it was never even used. Hand crank has a wood knob and what looks like hammered aluminum finish also found on the cross bar which has wood handle. Large aluminum gold tone bucket. (Please see photos) Breaks down for easy storage. Bucket measures 8" deep x 11" across the top. Dough hook: 8" x 10 ½" Crank :8 ½ " Mixer assembled over all size: 17 x13" GET OFF THE GRID...NON-ELECTRIC...Cut your power bills! This is a great price just check out the other dough mixers and you will see DON'T MISS OUT ON THIS ONE BID NOW!! This is a great find for any Cook or Collector I have been known to raise the opening bid on items that have a lot of watchers and no bidders YOU HAVE 3 DAYS TO NOTIFY US AFTER THE AUCTION ENDS. Payment must be received within 10 days of auction end. PAYPAL or Postal money orders preferred, money orders accepted . IF YOU CHOOSE NOT TO INSURE, WE ARE NOT RESPONSIBLE FOR LOSS OR DAMAGES. NO CHECKS THANK YOU!!


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Skip to comments.Heat-Thunder should be epic Finals [Another Reason to Root for OKC]
Posted on 06/12/2012 6:48:21 AM PDT by SoFloFreeper
This should be a fun NBA Finals.
Lebron James vs. Kevin Durant has me as intrigued as any individual matchup weve had since Olajuwon-Ewing or more so a couple years earlier in Jordan-Drexler.
This is the 12th time in NBA history that the players who finished first and second in the MVP voting will meet in the NBA Finals. The MVP winner is 9-2.
(Excerpt) Read more at blog.chron.com ...
So far, IMHO, much of the press has made this out to be a series where the only real question is, "Will Lebron prove once and for all that he is the god of basketball, or not?" On the other hand, to their credit, it looks like most of the ESPN commentators who actually played the game are picking OKC.
I think it's a toss up. But I love what it is doing for my city.
Thunder Up!
OKC sure has done well in a very short time as a NBA team.
Thunder Up!
I am hoping Durant and the Thunder will keep me tied with Lebron for the number of NBA titles won.
I'm rooting for Seattle to make LeBron a prophet about Miami winning "Not one, not two, not three, etc." championships.
As a Texas native, I cannot bring myself to root for a team from Oklahoma, so I am refusing to admit the Supersonics have moved.
What is this "NBA" you speak of?
Go Thunder!! Destroy the Trayvons.
"As a Texas native, I cannot bring myself to root for a team from Oklahoma"
Time to pull together and beat the Miami Trayvons!
Which town will have the best riots if they win? or lose?
As a fellow Texan, why the hate on Oklahoma? I like Oklahoma, I think of Oklahoma as our more conservative little brother. Now Arkansas, that's a whole other matter.
Hate is a strong word for what I feel. I would say I have a morose feeling toward the state.
Amen - at least they'll sell you gas in Oklahoma if you have Texas plates! I recall that issue when UT played Arkansas back in the day - advice was to bring a jerry can of gas, because the stations wouldn't sell to cars with Texas plates that weekend.
The Thunder beat my Spurs, but I'm rooting for them to go all the way, ESPECIALLY against "King" James.
The hotter the Heat the louder the Thunder. Go OKC!
Oh sure. A 20-62 team made the finals. Thunder Up!
Thunder is a great group of athletes & OKC is a wonderful community & OK a great state. My bro lives there and I love the visits to area & the fine people
HERE IS A GOOD SITE for info on the finals/series
Go Thunder!!!!!
Go HEAT!
Your magnanimity far exceeds that of a number of other Sonic fans. Personally, I see both sides of the "theft" issue. But then, since they wound up here, that is easy for me to say.
Regardless, glad to hear you are on board. Thunder Up!
I'm not a University of Texas fan either so those scores just make me smile.
Rooting for the Seattle SuperSonics of Oklahoma City (heck, it works for the Angels )
The Heat are going down, just like Trayvon.
Disclaimer: Opinions posted on Free Republic are those of the individual posters and do not necessarily represent the opinion of Free Republic or its management. All materials posted herein are protected by copyright law and the exemption for fair use of copyrighted works.


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'Homeland's' Navid Negahban is 'The Nicest Terrorist' on TV
Navid Negahban plays formidable terrorist, Abu Nazir on Showtime's "Homeland," but the charismatic actor shows his sense of humor as he shares his stories and acting advice.
'Homeland's' Navid Negahban is 'The Nicest Terrorist' on TV
Navid Negahban plays formidable terrorist, Abu Nazir on Showtime's "Homeland," but the charismatic actor shows his sense of humor as he shares his stories and acting advice.
'Da Vinci's Demons' Star Tom Riley Discusses His College Experience
Riley, who stars in the upcoming Starz series "Da Vinci's Demons," discusses his experience attending college at the University of Birmingham in the United Kingdom.
'Hangover' Writer Jon Lucas On Auditions, Writing, and TV
Jon Lucas, with his writing partner Scott Moore make their first foray into feature film directing with "21 and Over," a story about a med student celebrating his 21st birthday.
Study: More Female TV Roles, But For Younger Actors
The number of female characters on television edged up last year as more women took senior roles behind the camera. Despite the gains, women's representation on television continues to lag behind their proportion of the U.S. population.
TV Networks Commit to More Pilots in Development
ABC, NBC, Fox, and the CW go on a late summer shopping spree for new scripted projects to develop for the 2013-14 television season.
ABC Family-Driven Comedy Pilots Get CDs
Casting Directors Confirmed for Pilots "Bad Management," Keep Calm and Karey On," and "Middle Age Rage."
Best known for his roles in the "Harold & Kumar" films, Kal Penn now stars in the new cop dramedy "Battle Creek," and chats with Backstage about racists in the audition room.
Film and Television Production Heats Up in Chicago
Over the last few years the Windy City has experienced a renaissance of sorts, thanks in part to its iconic skyline, skilled production workforce, and universal esteem—but mostly thanks to the 30-percent tax credit enacted in early 2009.
'New Girl' Star Zooey Deschanel Starts Production Company, Signs TV Deal
Deschanel and her Hello Giggles partner Sophia Rossi are launching a production company and will also develop new comedy and drama projects for 20th Century Fox TV.
Brooklyn's Steiner Studios Plans Expansion of Sound Stages and Media Campus
The largest studio for film and TV production outside Hollywood could double in size over the next decade, adding to New York's growing entertainment industry infrastructure.


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Phase I of this study examines whether the likelihood of successfully achieving outcomes at specific times along the academic career path is related to doctorate recipients' sex. Phase II is longitudinal and examines whether doctorate recipients' sex is related to the amount of time it takes to achieve career milestones. Both phases use data from nationally representative samples of people who earned doctorates in S&E and who are employed in academia. Both also use multivariate statistical methods that control for factors other than sex that might affect career success.
Data for both phases of this study were taken from the SDR. The SDR data include only those individuals who have earned S&E doctorates in the United States. Consequently, our analyses do not consider career outcomes of those employed in academia who have not earned doctorates, those who earned degrees in fields other than S&E, or those who earned doctorates outside the United States.
The Phase I data include individuals who reported working full time in academia and who appeared in the 1981 through 1997 wave of the SDR. When this study was undertaken, SDR data were available for odd-numbered years 1973 through 1997; however, the surveys conducted during the 1970s do not provide sufficient detail on the ages and numbers of children (dependents), so we excluded them from our analyses. Some of our analyses required constraints on the samples we used. We describe these sample restrictions later in this section of the report.
The Phase II data include doctorate recipients who reported full-time employment in academia in the 1997 SDR wave. Because this part of the analysis tracks individuals from the time they earned their doctorates until the time of the 1997 survey, Phase II also uses some data from earlier SDR waves. These data include information required to construct work and family histories.[1]
As in Phase I, some of the Phase II analyses required that we exclude certain respondents from the samples we used. These exclusions are described later in this section of the report.
Below, we describe the Phase I models used to compare female scientists and engineers to their male counterparts. Specifically, we identify the career outcomes of interest, describe the statistical methods employed, list the control variables included in the analyses, and describe sample restrictions and model specifications.
Phase I focuses on three career outcomes for doctorate recipients employed in academia. The first, tenure track, is whether the individual is employed in a tenure-track position. The second, tenure, is whether the individual has earned tenure. And the third, academic rank, is whether the individual is employed at the rank of full professor, associate professor, or a junior rank (assistant professor or other rank below associate or full professor).
In Phase I, we used multivariate logit analysis as the primary statistical tool. Logit analysis allows estimation of the probability of success (e.g., the probability of earning tenure) after controlling for differences in individual characteristics among doctorate recipients included in the sample. Outcomes for the tenure and tenure-track analyses are discrete binomial occurrences in that only two outcomes are possibletenure or not tenured, and on tenure track or not on tenure track. Outcomes for the analysis of academic rank, however, are multinomial in that several outcomes are possiblefull professor, associate professor, or junior rank.
Table 2-1 lists the control variables included in the Phase I analyses. These include human capital proxies, personal characteristics, family characteristics, female interactions, year of the survey wave, and selection variables related to employment. In addition to the listed controls, each of the Phase I analyses includes a dichotomous (dummy) variable distinguishing females from males. The estimated coefficient on the "female" variable allows us to compute gender differences in the probability of a career success after accounting for the effects of controls.
|Table 2-1 source data: Excel file
Human Capital
Other things being the same, individuals who have accumulated more human capital are more likely to have earned tenure and to have been promoted to higher academic ranks. Table 2-1 lists human capital variables used as controls in the Phase I analyses. We emphasize that these variables are not direct measures of human capital; rather, they should be interpreted as proxies.
We included "years since earning the doctorate" as a measure of postdoctoral experience. We also included a set of variables distinguishing between the kinds of financial support that doctoral candidates receive in graduate school. We interpret these variables as proxies for differences in experience and training and differences in academic ability. For example, those who earned the doctorate while supported by research assistantships are likely to have experience and training different from that of doctoral candidates who were supported by teaching assistantships. And doctorate earners who were supported by fellowships are likely to be more academically able than those who received other kinds of support.
Some doctorate earners opt for additional training by taking postdoctoral appointments before they enter the full-time academic labor market. Our list of controls includes a variable reflecting whether individuals were planning postdoctoral appointments at the time they received their doctorates. The potential effect of this variable is an empirical issue. Postdoctoral appointments afford individuals opportunities for additional training that might improve chances for success in academia, but they delay entry into full-time faculty positions and thus can delay tenure and promotions to higher academic ranks.[2]
Field switching occurs when individuals earn undergraduate degrees, masters' degrees, or doctorates in different academic fields. Expertise in two or more fields could enhance chances for success in academic careers. Alternatively, individuals who stay in a single field might realize benefits from specialization that also affect career outcomes.
We included three variables that distinguish characteristics of the institutions at which individuals earned degreesearning a bachelor's degree at a foreign institution, earning a doctorate at a research institution, and earning a doctorate at a public institution. Although these variables distinguish possible differences in accumulated human capital, we regard their effects on success in academic jobs as an empirical issue.
Chances for earning tenure and promotion are likely to vary considerably across different academic fields. We included a set of control variables that distinguish 17 different fields in which individuals earned their doctorates. These fields are identified in table 2-2.[3]
|Table 2-2 source data: Excel file
Personal Characteristics
Table 2-1 lists three sets of control variables reflecting the personal characteristics of doctorate recipientsage at the time the doctorate was earned, citizenship, and race/ethnicity. We included these variables as controls to capture variations in backgrounds, opportunities, and preferences that could affect chances for tenure and promotion.
Family Characteristics
We included a set of three "family" variables as controls in the Phase I analyses. These are marital status (married or unmarried), the number of dependent children less than 6 years of age, and the number of children between the ages of 6 and 18.
Family characteristics can reasonably be expected to influence chances for tenure and promotion, but the direction of their effects is unclear. For example, being married might enhance a doctorate recipient's career if the spouse provides support and motivation. Having children might also provide motivation. Alternatively, the burden of supporting a family might divert time and energy from job responsibilities, thus reducing chances for tenure and promotion.
The ages of a doctorate recipient's dependent children convey information for estimating the effects of family composition on career success.[4] Three potentially important effects are (1) differences in child-rearing requirements, (2) cumulative care-giving effects, and (3) fertility timing. Children of different ages require different kinds and levels of care. Children of pre-school age, for example, require very different kinds of care than do children of high-school age. Also, older children have required a period of parental care longer than that required by younger children. For example, a two-year-old child has required care for only 20 percent of the time that a 10-year-old child has. Ages of children also convey information about the timing of fertility decisions. For example, an individual with 10 years of postdoctoral experience who is caring for a two-year-old child has likely postponed starting a family until after the time academic institutions typically make tenure decisions. An individual with the same experience and a 10-year-old child has probably been faced with child-rearing responsibilities before receiving tenure. This discussion points to the importance of measuring family composition at comparable times in the careers of doctorate recipients. For example, a two-year-old in the family of an individual with six years of experience might have a quite different effect on career success than would a two-year-old under the care of an individual with 10 years of experience.
Family characteristics might also affect chances for career success indirectly by affecting job choices. Doctorate recipients with spouses and children face location constraints that unmarried doctorate recipients without children do not. These constraints might cause individuals to compromise job choices and, eventually, reduce their chances for tenure and promotion.
We close this discussion with an important caveat about the difficulty in assigning causal links between family composition and career success. It could be that both marital status and timing of fertility are influenced by doctorate recipients' expectations of chances for tenure and promotion. Evidence from the literature suggests that women who perceive gender bias are more likely to marry and to have children earlier than they might otherwise (NSF 2003). To the extent that this occurs, observed relationships between family variables and career success will partly reflect selective decisions by individuals who believe their chances for tenure and promotion are relatively low.
Female Interactions
Some of the models we estimated include three female-interaction variables. These are interactions between "female" and marital status, "female" and the number of children younger than 6, and "female" and the number of children between ages 6 and 18. We included these variables to measure possible gender differences in the effects of family composition on chances for tenure and promotion.
There are several reasons to think that family composition might affect the academic careers of women and men differently. Gender differences in household and child-rearing activities could give rise to differential effects. If women, as a group, tend to undertake more household and child-rearing responsibilities than men do, they will have less time and energy to devote to their careers.
The constraint that marriage imposes on job choices might also be expected to differ by sex. Because academic job openings are limited, households in which both spouses seek faculty jobs face especially difficult location decisions, which often require compromise. Men and women both face this compromise in families where both spouses hold doctorates. But because fewer women than men have doctoral degrees, men are less likely than women to have their job choices constrained owing to their spouse also holding a doctorate.[5]
Survey Wave
Changes in labor market conditions and promotion requirements over the 19811997 time period spanned by our data are likely to affect success rates for academic careers. Accordingly, we controlled for the survey year in which individuals are observed in the data.
Employment Selection
Variables for employment selection reflect either voluntary or involuntary selections made by doctorate recipients about the kinds of activities they undertake on the job and the characteristics of the institutions at which they are employed. Relationships between the selection variables and career success rates should be interpreted cautiously. Because the selection variables themselves are career outcomes, they could be determined by the same forcesgender bias and other gender-specific factorsthat affect tenure and promotion decisions. For example, if women as a group tend to emphasize teaching as a primary work activity because there is gender bias against women in research, controlling for primary work activity in the tenure analysis might mask gender differences in tenure rates. Because of this potential problem, we adopted the convention of conducting each of the Phase I analyses twicewith and without the selection variables as controls.
This discussion provides a framework for interpreting the results of the Phase I analyses presented later in this report. It includes restrictions imposed on the samples used for estimating the tenure, tenure track, and academic rank models; specifications of the models we estimate; and guidance for interpreting results.
Data for the Phase I analyses were from the 19811997 SDR waves. All Phase I analyses were restricted to those doctorate recipients who reported full-time employment in academia; however, the samples we used were further restricted. These sample-selection criteria were years since earning the doctorate, outcome not applicable, not on tenure track, and missing observations.
Years Since Earning the Doctorate
Each of the Phase I analyses is based on a sample of doctorate recipients selected by postdoctoral experience (the number of years elapsed since earning the doctorate). Both the tenure track and tenure analyses were conducted using two different samples characterized by years of experience: one sample restricted to individuals with 8 or 9 years of postdoctoral experience, and a second sample restricted to individuals with 14 or 15 years of experience. The academic rank analysis was also conducted using two different samples: a first sample restricted to individuals with 14 or 15 years of experience, and a second sample restricted to individuals with 20 or 21 years of experience.
Each analysis includes a sample selected by an even-numbered year and an adjacent odd-numbered year. We used this selection procedure to exploit fully the sample sizes available in the SDR. Given that the SDR is conducted every other year, selecting by a single year (say, only even-numbered years) would yield only about one-half the available sample.
Our principal motive for selecting samples by years since the doctorate was earned was to ensure that the time-dependent control variables were observed at about the same point in each individual's postdoctoral career. The most important of these are the family and female-interaction variables. Selecting samples by years of experience also ensured that the selection variables characterizing work activities and employers were also observed at about the same time in postdoctoral careers.[6]
Ensuring that individuals appear in a given sample only once was a second motive for selecting by years of experience.[7] This avoided problems associated with uneven weighting (doctorate recipients with more experience appear in more SDR waves than those with less experience) and correlated statistical errors across individual observations.
Outcome Not Applicable
When asking individuals to report on tenure and rank status, the SDR survey instruments permit "not applicable" responses. Some of the analyses we conducted use samples that exclude individuals who made "not applicable" responses. This allowed us to determine the extent to which "not applicable" job assignments (which might be involuntary) explain differences in tenure and promotion rates.
Nontenure-Track Positions
The SDR survey instruments also allow respondents to report that they are employed in "not on tenure track" positions. Again, we conducted some tenure and rank analyses excluding these individuals to determine the extent to which assignments to nontenure-track positions explain gender differences in career success rates.
Missing Observations
Some SDR respondents do not complete the questionnaire. We excluded from our analyses doctorate recipients who did not report on tenure and rank status.[8]
We estimated six models for each of the Phase I tenure and academic rank analyses (table 2-3). Comparisons across these six models allowed us to determine whether selection variables (primary work activities and employer characteristics), assignments to job positions in which tenure or academic rank are not applicable, and assignments to nontenure-track positions affect estimates of gender differences in success rates.
|Table 2-3 source data: Excel file
Model 2, for example, differs from Model 1 in that it includes selection variables as controls.[9] Thus, by comparing estimates of gender differences across these two models we could determine whether work activities or employer characteristics explain some of the observed gender differences in career success rates. Models 3 and 4 and Models 5 and 6 are paired in the same respect.
Models 1 through 4 differ only in the treatment of "not applicable" responses. Thus, by comparing estimates of Models 1 and 2 with those of Models 3 and 4 we could determine if "not applicable" job assignments affect estimates of gender differences in outcomes.
Finally, Models 5 and 6 exclude individuals who reported being in nontenure-track positions. These two models allowed us to determine the extent to which assignments to nontenure-track positions affect estimates of gender differences.[10]
We estimated each of the models listed in table 2-3 twice, with and without the female-interaction variables. By comparing the two sets of results we could determine how gender differences in the effects of family characteristics affect gender differences in tenure and promotion rates.
Several aspects of the Phase II analyses are distinctly different from the Phase I analyses. Below we describe the Phase II study design.
The Phase II analyses examine tenure and academic rank for doctorate recipients who reported full-time academic employment. Unlike Phase I, Phase II does not include a formal analysis of tenure-track status. The Phase II analyses look at the time required for a clearly defined transition from one state to another in a career path (e.g., nontenured to tenured).
We used multivariate hazard analysis as the principal statistical tool in the Phase II analyses.[11] Hazard analysis looks at the time required for a transitional event to occur (e.g., time elapsed between earning the doctorate and earning tenure). Estimates of hazard models provide information needed to compute the probability that an individual will be tenured or promoted to senior academic ranks at a given point in time.[12] Like Phase I, the Phase II analyses are multivariate in the sense that we compared career success across gender after controlling for other factors that might affect tenure and promotion.
Estimating the hazard models for the tenure analysis required the following information: the time elapsed between earning the doctorate and receiving tenure for those individuals who have been tenured (at or before the 1997 SDR wave); the time elapsed between earning the doctorate and the 1997 SDR wave for those individuals who have not received tenure; and a censoring indicator distinguishing those individuals who have received tenure from those who have not.
Phase II analyses include doctorate recipients who were employed full time in academia as of the 1997 SDR wave. We constructed a variable measuring time elapsed between earning the doctorate and earning tenure by searching previous SDR records (i.e., SDR waves before 1997) for the first occurrence of reported tenure.[13] Then, we took the date of the first reported occurrence as the date of tenure and counted years elapsed since the year of the doctorate. If no SDR wave indicated tenure, we assumed that the individual had never been tenured and counted years elapsed between earning the doctorate and the 1997 survey wave.
The Phase II academic rank analysis required the same kind of information as the tenure analysis. Of course, the rank analysis required data on the time elapsed between earning the doctorate and promotion to either associate or full professor. We constructed these variables using the same method described above for the tenure analysis.
The method we used to create measures of time elapsed before tenure or promotion introduced a potential bias. Specifically, we overstate time required to achieve tenure or promotion if those outcomes are not reported in the survey that corresponds to the date of the outcome. For example, suppose that an individual was first tenured as of the date of the 1993 SDR but failed to complete the section of the 1993 questionnaire on tenure status. Suppose further that the same individual reported being tenured on the 1995 survey. Our method will overstate time required for tenure by two years for this individual.[14]
We have compared missing responses to survey items on tenure and rank status for men and women in our sample. We found that women are about 3.5 percent less likely than men to have missing observations for these outcomes before they become tenured or promoted to the rank of associate professor.[15] This raises the possibility that we overstate time required for tenure and promotion for men relative to women, or equivalently, that we understate relative differences in male to female success rates. Unfortunately, we cannot tell for certain whether the bias exists, and if it does, the extent to which it occurs.[16]
Like Phase I, the Phase II analyses are multivariate in that we attempt to measure gender differences in career success rates after accounting for factors other than sex that might affect tenure and promotions. All of the Phase II analyses also include the dichotomous variable "female," which distinguishes female from male doctorate recipients. The estimated coefficient of the female variable serves the same purpose in Phase II as it does in Phase I.
Table 2-4 lists the control variables used in the Phase II analyses. The human capital variables and personal characteristics are the same as those used in Phase I and are not discussed further here.
|Table 2-4 source data: Excel file
Family Characteristics
The family variables used in Phase IImarital status, dependents younger than 6, and dependents between ages 6 and 18are the same as those used in Phase I. When we measure them, however, is slightly different. For Phase II, we measured family variables three waves (about 6 years) and six waves (about 12 years) after the doctorate was earned for the tenure and the academic rank analyses, respectively. For the academic rank analysis, we measured family variables later in postdoctoral careers to coincide more closely with the time at which promotion to full professor might occur.
Female Interactions
The Phase II analyses include the full set of female-interaction variables. Our reasons for including these as controls are the same as those described earlier for the Phase I analyses. We defined the female interactions so that they are observed at the same time in the postdoctoral career as the family variables.
Decade of the Doctorate
The Phase II analyses include a set of dichotomous variables that identify the decade (1970s, 1980s, 1990s) in which individuals earned their doctorates. We included these variables to control for changes over time in labor market conditions and tenure and promotion requirements.[17]
Selection Variables
The Phase II analyses include two kinds of selection controlsan outcome-status variable and several work-history variables. We refer to these as selection variables because, as outcomes themselves, they are determined by either voluntary self-selection or involuntary assignment.
Although several of the Phase II selection variables listed in table 2-4 are similar to those used in Phase I, their construction is quite different. The Phase I selection variables are a snapshot of the individual's status at the time of the survey wave. The Phase II selection variables are more informative because they reflect work histories that track the individual's status between when the doctorate was earned and either tenure or promotion (or time elapsed up to the 1997 SDR wave if the individual has not been tenured or promoted).
We constructed the Phase II selection variables using a method similar to the procedure for computing time elapsed between earning the doctorate and achieving either tenure or promotion. Specifically, we traced each individual appearing in the 1997 SDR wave through earlier waves, counted the number of times before tenure or promotion that the individual reported being in a given status, and calculated the percentage of survey waves for which that status was reported. For example, if an individual reported employment status in four waves before being tenured and in one case reported employment outside academia, the variable reflecting "not working in academia full time" takes on a value of 25 percent for the Phase II tenure analysis.
The variable "outcome status not applicable" measures the percentage of survey waves before tenure or promotion that an individual reports employment in a position in which either tenure or academic rank is not applicable. Other factors being the same, we would expect this variable to be positively correlated with time elapsed before tenure or promotion (or negatively related to the probability of being tenured or promoted at a given point in time).
The set of work-history variables includes a measure of the percentage of survey waves before either tenure or promotion that an individual reports not being employed full time in academia. In most cases, we would also expect this variable to be positively correlated with time elapsed before tenure or promotion.[18]
The remaining four work-history variables reflect the characteristics of employers and primary work activities. These variables coincide with selection variables used in Phase I, except that they reflect employment histories rather than current employment status.
The Phase II analyses used a selected sample of the doctorate recipients who reported full-time academic employment in the 1997 SDR wave. The analyses for tenure and promotion to associate-professor rank include only individuals with 6 or more years of postdoctoral experience; the analysis of promotion to full professor includes only individuals with 12 or more years of postdoctoral experience. Doctorate recipients whose years of postdoctoral experience fall below these limits were excluded from the analyses.
These exclusions principally were made to allow us to measure the variables for family and for female interactions for each individual at a comparable time, close to when tenure and promotions occur in a typical academic career. Family variables are undefined (not yet observed) for these less-experienced doctorate recipients because they have not yet reached the later career points being measured.[19]
Secondarily, the sample exclusions are motivated by censoring effects. Observations for less-experienced doctorate recipients are heavily censored in that very few individuals receive tenure in less than 6 years or are promoted to full professor in less than 12 years. Although hazard analysis is designed to deal with censoring effects, including the less-experienced doctorate recipients in our samples would provide little information about career success rates.
Table 2-5 lists the Phase II model specifications. These models allowed us to determine whether the selection variables reflecting work histories affected estimates of gender differences in career success rates. For example, Models 1 and 2 differ in that the latter includes variables for work history. Comparing estimates of these two models allowed us to determine whether estimates of gender differences in success rates are sensitive to employment-related histories. Similarly, Models 3 and 4 differ from Models 1 and 2 in that the latter include the variables reflecting time spent in outcome-not-applicable job positions.
|Table 2-5 source data: Excel file
We have reviewed several statistical studies of gender differences in academic careers (NSF 2003). Some of these studies are similar to ours in that they used multivariate analyses, used data from nationally representative samples, and included broad coverage of academic fields. Our study design, however, offers three important contributions to the literature. These are our treatment of family characteristics, systematic control for selection variables, and the longitudinal nature of our Phase II analyses.
Relatively few studies provide evidence on the effects of family characteristics on women's academic careers. Farber (1977), McDowell and Smith (1992), and Kahn (1993) provided suggestive evidence that family responsibilities hinder women's careers in that women appear to be disadvantaged at points in their careers when they are likely to have young children.
Two studies, Long (2001) and Olson (1999), included direct controls for family characteristics in their analyses of tenure and promotions. Both, however, estimated separate models for men and women, and neither conducted formal hypothesis testing of differential gender effects. Moreover, both Long and Olson measured family characteristics at different points in the careers of doctorate recipients included in the data. As we have argued, it is reasonable to expect that the timing of both marital and fertility decisions are important.
We have attempted to resolve both issues with our study design. First, our specification of the female-interaction variables permits straightforward hypothesis tests for gender differences in the influence of family characteristics on academic careers. Second, we have been careful to measure family characteristics at common points in postdoctoral careers in both our Phase I and Phase II analyses.
Some of the studies we have reviewed include variables reflecting the characteristics of the employing institution as controls (NSF 2003). This kind of model specification is understandable, given that requirements for tenure and promotion are likely to vary across different kinds of institutions. For example, it is reasonable to expect that tenure and promotion requirements are usually more stringent at research universities than at most four-year liberal arts colleges. Nonetheless, we have concerns about interpreting estimates of gender differences in career success rates from models that control for employer characteristics. The problem is that the characteristics of the employer are themselves outcomes resulting from a selection process that may be affected by factors related to career success, including individual preferences, human capital, opportunities, and real or perceived gender bias.
These same comments apply to primary work activities. Decisions to engage in research or teaching, which are likely to affect career success, result from a selection process reflecting preferences, skills, and opportunities. If women are disadvantaged with respect to tenure and promotion, they may also be limited in their choices of work activities.
Our study design does not completely resolve the problems associated with using selection variables as controls. However, the sequence of models we estimated includes specifications both with and without employer characteristics and primary work activities. As we noted earlier, this feature of our study design allowed us to determine whether estimates of gender differences in career success rates are sensitive to model specifications that include selection variables as controls.
Many of the studies we have reviewed use multivariate analyses in that they attempt to estimate gender differences in career success rates after accounting for controls. However, because these studies measure controls for each individual at only a single point in time, they cannot account for the potential effects of career histories on outcomes. We have attempted to resolve this problem in our Phase II analyses. Some of the models we estimated include work-history variables as controls. These include variables measuring the percentage of time before tenure and promotions that individuals spent working outside of academia, working for employers with various characteristics, and engaging in different work activities.[20]
Several important limitations of our study design should be considered when interpreting the results of our analyses, which are presented in later sections of this report. In particular, our results do not prove the presence or absence of gender bias in academia; rather, the study's findings should be interpreted within the broader context of the empirical literature on gender differences in academic careers.
The potential for selection bias is perhaps the most serious limitation of this study. Doctorate recipients included in our analyses were not randomly assigned to the samples we used. Individuals included in the sample were those who selected science or engineering as a field of study and who completed requirements for a doctorate. They also selected and obtained a full time position in academia rather than a part-time academic position or employment in a nonacademic job. The selection process itself, however, may be determined in part by differences in individual preferences or by discriminatory treatment that could be related both to sex and to chances for career success. Although we attempted to control for differences among individuals in our analyses, we were limited to characteristics that are measurable and available in the data we used. As is typically the case in empirical work, we could not control for remaining unobserved differences among individuals that could affect outcomes. These unobserved differences could be related to sex and the selection process, thus raising the possibility of selection bias.
There are also selection issues related to the samples we have chosen to use, which exclude doctorate recipients employed part time in academia. A selection issue arises if, other factors being the same, women are more likely than men to work part time. Also, because we limited our samples to doctorate recipients employed in academia, we did not account for attrition from the academic workforce. The selection issue here is whether women are more likely than men to remain in academia if they fail to land tenure-track positions, receive tenure, or earn promotions.
Statistical methods for adjusting for selection bias have been developed.[21] The data required to adjust for some of the potential sources of bias described above, however, are unavailable in the samples we used. For example, adjusting for selection into science and engineering fields requires information on individuals who have selected fields other than science and engineering. Similarly, adjusting for selection into the sample of doctorate recipients requires data on individuals who have not earned doctorates. This information is not available in the data we used.
In theory, the data required to adjust for selection into full-time academic positions are available, given that the SDR data include some doctorate recipients who hold nonacademic jobs and some who are employed part time. However, estimating models that adjust for selection bias requires a priori identification restrictions on factors affecting job choices and tenure and promotions. Given the choices of variables available in the data we used, appropriate identification restrictions were not obvious to us.[22]
Although the multivariate analyses we conducted account for a relatively large set of factors other than gender that might affect career success, the controls we could use were necessarily limited by the data available to us. For example, the variables for human capital we included in our models are proxies, not direct measures of skills and abilities that might enhance doctorates' chances for tenure and promotion. Also, our analyses did not control for variations in measures of productivity, which include scholarly output, quality of teaching, and service to the academic community.[23] Finally, we had limited information about individuals' predoctoral careers. Some information about predoctoral education was available, but we had no information about predoctoral work experience.
We may systematically overstate the relative time required for male tenure and promotions in our Phase II analyses because of missing responses in the SDR data (see "Phase II Control Variables," above). We have evidence that women are more consistent than men in completing the SDR questionnaires. Women are about 3.5 percent less likely than men to have omitted information on their rank before they achieved tenure and promotion to associate professor, and they are about 3.0 percent less likely to have omitted this information before they achieved promotion to full professor.[24] This raises the possibility that the Phase II analysis overstates the relative time required for men to achieve promotions. To the extent that this occurs, our estimates of female disadvantages in the Phase II analyses will be understated.
This study focuses on gender differences for a limited set of career outcomes, but the available data are suited to address several other important questions about the academic careers of female scientists and engineers. These include such questions as whether among scientists and engineers women are more or less likely than men to take academic jobs, take part-time employment in academia, or remain in academia, especially after failing to receive tenure and promotions, and whether women face greater mobility constraints than men when selecting jobs, especially when they must find new employment after failing to receive tenure.
Several of our recommendations for future research address some of the previously noted limitations of this study. For example, we noted that gender differences in preferences for academic versus nonacademic jobs raises the potential for selection bias. A study of gender differences in job choicesespecially the first job after earning the doctoratewould help us assess the potential for selection bias. Studies of gender differences in full-time versus part-time employment and in attrition rates would also address selection issues.
A job-mobility study might shed light on whether immobility compromises the academic careers of female scientists and engineers. We are particularly interested in whether gender differences in mobility exist among doctorate recipients who fail to receive tenure in their first academic job. Many doctorate recipients who take first jobs at prestigious research institutions fail to earn tenure. Their ability to earn tenure at a subsequent position is likely to depend on the freedom they have to choose jobs that are well suited to their experience and skills.
This study provides evidence that gender differences in the influence of family variablesmarital status and family sizeare related to women's chances for career success. Accordingly, we recommend that future studies be designed to control for potential gender differences in the influence of family characteristics.
[1] The SDR is longitudinal in the sense that individuals reappear in successive survey waves throughout their careers as long as they remain in the sample frame. The SDR data are not maintained in a longitudinal format, however, so constructing employment and family histories for individuals requires linking survey identification numbers across SDR waves.
[2] Postdoctoral appointments could also reflect selection by ability. For example, the most able doctoral candidates might be more likely to receive faculty appointments immediately after earning their degrees.
[3] Identifying the parameters of the logit models required us to combine a few of the fields in table 2-2 for the academic rank analyses. See Appendices C and D.
[4] Over the period 19811997, the SDR survey instruments have solicited different information about dependents' ages. The distinctions we makechildren under age 6 and children between the ages of 6 and 18reflect the most detail consistently available since the 1981 SDR wave.
[5] One might argue that dual-career families have financial resources that enable them to sacrifice current income for positions that offer better career opportunities. This possibility, however, poses difficult modeling issues. Even if data on spouses' education and income were available, it would be unclear whether the selection of a position with lower pay but better opportunities for career advancement was permitted by the spouse's income or whether it resulted from a location compromise.
[6] Selecting samples based on years of postdoctoral experience does not resolve timing issues related to predoctoral careers. Before earning their doctorates, some individuals accumulate human capital and credentials that might enhance their postdoctoral academic careers. The data we used provides no information about individuals' work histories before they earned doctorates; however, some of the effects of predoctoral careers are likely to be captured by controls for age and academic field. Older doctorate recipients are more likely to have accumulated predoctoral experience, and the extent to which predoctoral credentials affect academic careers is likely to vary by field. In our analyses, we controlled for both age and field.
[7] Once individuals are selected for the SDR, they are followed in subsequent waves as long as they remain in the sampling frame. As a result, the same individual can appear in several SDR waves.
[8] We did not exclude individuals when the value of a control variable was missing. Instead, we constructed dichotomous (dummy) variables for missing control variables.
[9] All six models include the other controls listed in table 2-1.
[10] Because Models 5 and 6 include only doctorate recipients in tenure-track positions, they could not be estimated for the analysis that examines gender differences in tenure-track placements.
[11] Hazard analysis is sometimes referred to in the literature as duration or survival analysis.
[12] See Appendix A for a more detailed discussion of hazard analysis.
[13] Unfortunately, the SDR data report whether individuals are tenured as of each survey but do not report the date of tenure. The same is true for academic rank.
[14] The SDR questionnaire simply asks whether an individual is tenured as of the date of the questionnaire; it does not ask when tenure was received.
[15] Gender differences in missing outcomes before promotion to the full professor rank are small and are statistically insignificant.
[16] The bias occurs only if the individual fails to respond to questions of tenure and rank status on the first survey wave after either tenure or promotion actually occurs.
[17] Because the Phase I analyses control for year of the survey wave and time since earning the doctorate, controlling for the year of the doctorate as well would be redundant.
[18] Some individuals might acquire skills or experience in employment outside of academia that enhance their chances for success in academia, but we expect in most cases a history of full-time academic employment would allow individuals to acquire job-specific human capital that would confer greater advantages in the academic labor market.
[19] For example, we do not observe family characteristics six years after earning the doctorate for individuals reporting only two years of postdoctoral experience in the 1997 survey.
[20] Note that the work-history variables can be viewed as selection variables in the sense that they result from a selection process that could be affected by the same factors that influence tenure and promotion.
[21] See, for example, Heckman (1974, 1976).
[22] The identifications restrictions require that different sets of factors influence job choices and tenure or promotion outcomes.
[23] The 1995 SDR is the only wave that provides information on scholarly output (the number of articles published and papers presented). However, the sample size for the 1995 wave alone is too small to estimate the models we specify. None of the SDR waves provides data on teaching quality or service to the academic community (e.g., committee assignments).
[24] The gender difference in response rates for the associate-professor analysis is statistically significant. Even though the difference for the full-professor analysis is not statistically significant, there is still potential for bias in the measure of time to promotion.


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text:
My name is Vasily Dolgushev and I am an Associate Professor at the Department of Mathematics of Temple University
My research is concerned with deformation quantization, homotopy algebras, operads, configuration spaces, finite type invariants and manifold calculus.
In the spring semester of 2015, I taught algebraic topology. In the fall semester of 2015, I am teaching graduate algebra (part 1) and commutative algebra. Jointly with Ed Letzter and Martin Lorenz we run the Algebra Seminar at Temple.
Some of my pictures


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