|
ScienceWeek
SCIENCEWEEK
May 4, 2007
Vol. 11 - Number 17
--------------------------------
Announcement: If you have an interest in science fiction, you might want to have a look at PRISM-X, a new online journal. PRISM-X is devoted to quality adult literary fiction about science, scientists, future science, future culture, dystopias, conflicts between science and society, speculative fiction... the far horizons of the human imagination. Free access at http://scienceweek.com/prismx
--------------------------------
This is *our* Universe, our museum of wonder and beauty, our cathedral.
-- John Archibald Wheeler
--------------------------------
Contents (full text below):
1. Developmental Biology: Dance of the Embryo
2. Planetary Science: Hot News on Mercury's Core
3. Climatology: Recent Climate Observations Compared to Projections
4. Neuroscience: Unconscious networking
5. Applied Science: Connections -- The Best Is Yet to Come
6. Evolution: Natural Selection as a Population-Level Causal Process
7. Biology of Aging: Aging Pathways in Worms, Flies, Mice and Humans
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
1.
Science 4 May 2007: Vol. 316. no. 5825, pp. 697 - 698 DOI: 10.1126/science.1142967
Developmental Biology: Dance of the Embryo
Richard R. Behringer
A picture may be worth a thousand words, but in biology it has become increasingly clear that static images are not sufficient to elucidate the complex behaviors of cells in their natural environment. This is especially true in the field of embryology, where snapshots of the progression from fertilized egg to a developing organism--with distinct tissues and a defined body axis--provide limited information about what happened before the picture was taken and offer little clue as to what might happen next. Technical difficulties have hindered progress in understanding the earliest stages of mammalian embryonic development--before implantation in the uterus. On page 719 in this issue, Kurotaki et al. (1) report using live fluorescent imaging to directly view a preimplantation-stage mouse embryo as it develops from a fertilized egg into a blastocyst, a spherical structure of about 50 to 60 cells. The cells (blastomeres) of these embryos move much more than previously thought, which has obscured our understanding of how the blastocyst is formed. The movies of Kurotaki et al. provide the clearest views of early mammalian embryo axis formation.
So-called preimplantation embryonic development includes fertilization of the oocyte followed by its cleavage to produce blastomeres. The first steps of cellular differentiation occur within an acellular glycoprotein-rich membrane called the zona pellucida (see the figure). Kurotaki et al. find that blastomeres move extensively, causing the embryo to wiggle and "dance" within the zona pellucida.
The mammalian blastocyst consists of a single layer of cells called the trophoblast that surrounds a fluid-filled cavity. An inner cell mass of pluritpotent cells is located at one side of the blastocyst called the embryonic (E) pole. Opposite of this is the abembryonic (Ab) pole, creating the embryonic-abembryonic (E-Ab) axis (2). The trophoblast gives rise to placental tissues, whereas the inner cell mass gives rise to the embryo proper and some placental tissues. Although the functional importance of the E-Ab axis has yet to be determined, it is the first morphological axis that can be distinguished in the developing mouse embryo, and researchers have concentrated on the molecular and cellular mechanisms that lead to its formation (3). It has been thought that the E-Ab axis may presage the anterior-posterior body axis.
The ideal way to follow the fates of cells within a developing embryo is to watch the embryo, unperturbed, in its natural environment. Intrusive methods, such as injecting molecules into blastomeres to mark them, or removing or adding blastomeres, can alter normal development. In recent years, fluorescent proteins have been created to visualize specific organelles (for example, the nucleus) within live cells (4, 5). Kurotaki et al. generated a transgenic mouse line in which the gene for a nuclear-localized green fluorescent protein was introduced into a ubiquitously expressed genetic locus. The fluorescent protein is thus expressed in all cells, and precisely pinpoints the position of each blastomere and its cellular progeny by "painting" chromosomes as the cells divide. The authors could continuously image the embryos from the two-cell stage to the blastocyst stage in vitro by fluorescence microscopy. Consistent with previous reports (6), Kurotaki et al. found that the blastomeres move extensively at each stage of cell cleavage, causing the embryos to move within the zona pellucida. By following the cellular progeny of each of the two initial blastomeres (of the two-cell stage embryo), the authors determined their final positions within the blastocyst. Each of the initial two blastomeres gives rise to cells in both the embryonic and abembryonic regions.
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
2.
Science 4 May 2007: Vol. 316. no. 5825, pp. 702 - 703 DOI: 10.1126/science.1142328
Planetary Science: Hot News on Mercury's Core
Sean C. Solomon
Some 30 years ago the planetary science community was surprised when the Mariner 10 spacecraft flew by the planet Mercury and detected an internal magnetic field (1). Earth's internal field is produced by a magnetic dynamo sustained by convective motions in the planet's molten, iron-rich outer core. Although Mercury's high bulk density indicates that its dominantly iron central core is the largest by fractional mass among the planets (2), the detection of its magnetic field was surprising because Venus has no field and Mars and the Moon show evidence only for ancient global fields. With a mass about 5% that of Earth, Mercury had been expected to have cooled internally to the point where either the core had solidified or core convection no longer occurs. A necessary condition for Mercury's magnetic field to arise from an active Earth-like dynamo is that at least the outer shell of its core be molten. On page 710 of this issue, Margot et al. report new observations of variations in Mercury's spin rate made with Earth-based radar, providing strong evidence that this condition is met (3).
The radar measurements constitute a triumph of two theoretical ideas developed decades ago. Shortly after the Mariner 10 discovery, Peale (4) outlined a procedure to determine whether the planet has a fluid outer core. His method was based on the observation that Mercury is in an orbital state in which the planet completes three rotations about its spin axis for every two revolutions around the Sun. The procedure requires the measurement of the small oscillation in the planet's spin rate (libration)--a few hundred meters in amplitude--forced by solar torques as Mercury follows its 88-day eccentric orbit. Additional parameters that must be known include the tilt of the spin axis and the components of the planet's gravity field describing the degree to which the field is flattened at the poles and out of round along the equator. The last two quantities have been estimated, albeit with low precision, from Mariner 10 tracking observations made during the probe's three encounters with Mercury during 1974-75, but the libration amplitude and a sufficiently accurate pole position were not known before now.
The second theoretical development, by Green (5) and Holin (6), stems from the recognition that irregularities, or speckles, in the radar signal returned from a planetary target rotate in space as the planet spins. Under suitable geometric constraints, analysis of radar signals recorded at two stations on Earth can detect this rotation as the speckle pattern sweeps coherently across Earth's surface. By combining many such paired measurements at different times and observing geometries, the position of the target planet's spin axis and periodic variations in the spin rate may be ascertained.
Margot and his team (3) applied these two theories with spectacular results. From radar signals bounced off Mercury and recorded at pairs of radio antennas in California, West Virginia, and Puerto Rico during more than 20 observation periods from 2002 through 2006, the group determined the position of Mercury's spin axis with a precision two orders of magnitude superior to the previous best estimate. Equally important, they detected Mercury's forced libration and determined its amplitude for the first time. The amplitude is sufficiently large that Mercury's solid mantle and crust must be decoupled from the planet's core on an 88-day time scale. This result indicates that Mercury has a molten outer core at 95% confidence, a level limited at present by uncertainty in the knowledge of Mercury's gravity field.
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
3.
Science 4 May 2007: Vol. 316. no. 5825, p. 709 DOI: 10.1126/science.1136843
Climatology: Recent Climate Observations Compared to Projections
S. Rahmstorf et al.
We present recent observed climate trends for carbon dioxide concentration, global mean air temperature, and global sea level, and we compare these trends to previous model projections as summarized in the 2001 assessment report of the Intergovernmental Panel on Climate Change (IPCC). The IPCC scenarios and projections start in the year 1990, which is also the base year of the Kyoto protocol, in which almost all industrialized nations accepted a binding commitment to reduce their greenhouse gas emissions. The data available for the period since 1990 raise concerns that the climate system, in particular sea level, may be responding more quickly to climate change than our current generation of models indicates.
Observations of the climate system are crucial to establish actual climatic trends, whereas climate models are used to project how quantities like global mean air temperature and sea level may be expected to respond to anthropogenic perturbations of the Earth's radiation budget. We compiled the most recent observed climate trends for carbon dioxide concentration, global mean air temperature, and global sea level, and we compare these trends to previous model projections as summarized in the 2001 assessment report of the Intergovernmental Panel on Climate Change (IPCC) (1). The IPCC scenarios and projections start in the year 1990, which is also the base year of the Kyoto protocol, in which almost all industrialized nations accepted a binding commitment to reduce their greenhouse gas emissions. Although published in 2001, these model projections are essentially independent from the observed climate data since 1990: Climate models are physics-based models developed over many years that are not "tuned" to reproduce the most recent temperatures, and global sea-level data were not yet available at the time. The data now available raise concerns that the climate system, in particular sea level, may be responding more quickly than climate models indicate.
Carbon dioxide concentration follows the projections almost exactly (Fig. 1), bearing in mind that the measurements shown from Mauna Loa (Hawaii) have a slight positive offset due to the slightly higher CO2 concentration in the Northern Hemisphere compared with the global mean. The level of agreement is partly coincidental, a result of compensating errors in industrial emissions [based on the IS92a scenario (1)] and carbon sinks in the projections.
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
4.
Nature 447, 46-47 (3 May 2007) | doi:10.1038/447046a; Published online 2 May 2007
Neuroscience: Unconscious Networking
Mark A. Pinsk1 & Sabine Kastner1
What are neural networks doing when the brain is at rest? It turns out that in primates, even under conditions of deep anaesthesia, some of these networks undergo highly organized patterns of activity.
Our brains use up enormous amounts of energy, even when we are daydreaming with our eyes closed and not performing any demanding mental operations1. In fact, intensive cognitive operations — arithmetic calculations, for example — increase the brain's energy consumption only minimally.
When subjects are in a resting state, spontaneous brain activity is not as chaotic as one might expect. Instead, that activity correlates systematically across anatomically and functionally connected areas that are normally used when performing tasks such as reading this article2. The significance of this correlated activity during states of rest has remained unclear. Vincent and colleagues (page 83 of this issue)3 shed light on the matter by showing that organized activity patterns in neural networks are similar across primate species, and are not tied to a conscious state of mind.
Vincent et al. used functional magnetic resonance imaging (fMRI) to investigate spontaneous fluctuations of neural activity in the monkey brain during anaesthesia. The fMRI signals are indirect measures of neural activity, and determine spatially specific changes in blood oxygenation levels across the brain (referred to as the BOLD signal). The authors chose a starting point, or 'seed' region, in the frontal cortex known as the frontal eye field, and looked at how spontaneous fluctuations of fMRI signals in this region correlated over time with signals in the rest of the brain.
The frontal eye field is part of the oculomotor system, a network of brain areas that subserves the planning and execution of eye movements, and it is well understood in both monkeys and humans4. Only a few other discrete regions in the frontal and parietal cortex showed temporally coherent correlations with the spontaneous signal fluctuations in the frontal eye field. These brain regions are known to be interconnected, and are all part of the oculomotor system. When a different seed region in the oculomotor system was chosen, the same discrete network was revealed.
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
5.
Nature 447, 39 (3 May 2007) | doi:10.1038/447039a; Published online 2 May 2007
Applied Science: Connections -- The Best Is Yet to Come
Mark Buchanan
Optimality is a key organizing principle of science, but the patterns of connections within real-world networks do not always respect it.
An 'optimal' solution to a problem is, in some sense, the 'best' solution. It's the shortest route to work, or the way of packing oranges that takes up the least volume. In science, optimality has long been an organizing principle. Mathematical physics views the Universe as unfolding with dynamics that minimize a quantity known as the 'action', whereas economists and other social scientists often take optimality as a guide to human behaviour: we act, they say, to maximize our utility, be it financial or otherwise.
Looking at our networked and interconnected world, we may wonder whether anything is optimal here, from the food webs that underlie ecosystems, to technological networks such as the Internet. And science recognizes that the real world often falls short of optimality. Physicists know of many materials in which, as in glass, complex interactions among the molecules prevent the ordered arrangement of lowest (free) energy; these materials persist naturally in disordered, suboptimal confusion. Meanwhile, the economists' Homo economicus has been replaced by a biologically more plausible creature acting on the basis of fast yet fallible instincts. We solve life's problems the way we pack the dishwasher — not optimally, after long calculation, but quickly and more or less efficiently.
What about networks? Is the Internet optimal, in any sense? Is there a 'best' way to design the connections that link together a collection of cells, people or computers? This question has many possible answers — which always depend on what, if anything, a network is 'designed' to do.
In 1964, American engineer Paul Baran conceptualized a layout for the command and control network of the US military aiming to withstand an attack by the Soviet Union. He visualized it as a meshwork, something like a fishnet, with roughly the same number of links emanating from each element. No one element would be a communications hub — and primary target — and the natural redundancy of paths would allow messages to find a route through the network even if much of it was destroyed.
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
6.
The British Journal for the Philosophy of Science 2006 57(4):627-653; doi:10.1093/bjps/axl025
Evolution: Natural Selection as a Population-Level Causal Process
Roberta L. Millstein
Recent discussions in the philosophy of biology have brought into question some fundamental assumptions regarding evolutionary processes, natural selection in particular. Some authors argue that natural selection is nothing but a population-level, statistical consequence of lower-level events (Matthen and Ariew [2002]Go; Walsh et al. [2002]Go). On this view, natural selection itself does not involve forces. Other authors reject this purely statistical, population-level account for an individual-level, causal account of natural selection (Bouchard and Rosenberg [2004]Go). I argue that each of these positions is right in one way, but wrong in another; natural selection indeed takes place at the level of populations, but it is a causal process nonetheless.
Recent discussions of natural selection have given conflicting answers to a pair of questions: first, is natural selection a causal process or is it a purely statistical aggregation? And second, is natural selection at the population level or at the level of individuals? Denis Walsh, Tim Lewens and André Ariew ([2002]) and Mohan Matthen and Ariew ([2002])Go (hereafter, when I am referring to the two papers together, WALM) have argued that natural selection is purely statistical and on the population level, whereas Frédéric Bouchard and Alex Rosenberg ([2004])Go (hereafter, BR) argue that natural selection is causal and on the individual level. In this essay, I argue for a third logical possibility: natural selection is indeed a causal process, but it operates at the population level.
The issues at stake in this debate will be fleshed out in more detail in the discussion below. However, at the outset it is important to distinguish this debate from the more well-known debates over the units (or levels) of selection; unfortunately, similar terminology makes this distinction difficult. One obvious difference between the debates is that the units of selection debates assume a causal basis for natural selection, whereas the existence of selection's causal basis is one of the points of contention in the debate at hand. With regard to the issue of levels, however, the difference between the two debates can best be seen by the following example. Suppose that a person believes, in terms of the levels of selection debate, that selection (either sometimes or always) acts on organisms. Such a person still might ask whether that selection process was acting on individual organisms or populations of organisms.1 It is the latter question that concerns us here, and indeed, throughout this paper I have, for the sake of simplicity, assumed organismic selection, although I believe that similar arguments can be made for other units of selection. Now, it may turn out that the present debate has interesting consequences for the debates over the units of selection, but I will not explore such consequences here.
Further terminological confusion arises because of the distinction being made between an individual and a population. Indeed, one might wish to speak of an ‘individual’ population rather than a class or type of population. However, the reader should understand that, in what follows, ‘individual’ refers to an individual organism, whereas a population refers to a particular spatiotemporal collection of organisms.
Some philosophers might object to the very idea of population-level causality. In response to this concern, I will argue in Section 2.1 that anyone who accepts the reality of frequency-dependent selection is already committed to population-level causality. Moreover, in Section 2.2 I will argue that population-level causality is consistent with three commonly accepted accounts of causality; thus, population-level causality is at least of no more philosophical concern than they are. Turning to positive support for my claim that natural selection is a causal process that operates at the level of populations, I will need to make the case for both natural selection as ‘causal’ and natural selection as ‘population-level.’ I will take up the former issue in Section 3 and the latter issue in Section 4. Recall, as I noted above, that I agree with BR, but disagree with WALM, on the question of whether natural selection is causal; thus, Section 3 responds to WALM's claims.2 On the other hand, I agree with WALM, but disagree with BR, on the question of whether natural selection is population level; thus, Section 4 responds to BR's claims. Both Sections 3 and 4 involve an examination of Nathan Rank's and Elizabeth Dahlhoff's studies of the montane willow leaf beetle (in particular, Rank [1992]Go; Dahlhoff and Rank [2000]Go; Rank and Dahlhoff [2002])Go, on the assumption that it is important not only to examine biological theory, but also to consider how that theory is applied in an actual case.
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
7.
Journal of Experimental Biology 210, 1607-1612 (2007) doi: 10.1242/jeb.004887
Biology of Aging: Aging Pathways in Worms, Flies, Mice and Humans
Stuart K. Kim
Development of functional genomics tools has made it possible to define the aging process by performing genome-wide scans for transcriptional differences between the young and the old. Global screens for age regulation have been performed for worms and flies, as well as many tissues in mice and humans. Recent work has begun to analyze the similarities and differences in transcriptional changes in aging among different species. Most age-related expression changes are specific for a given species, but genes in one pathway (the electron transport chain pathway) show common age regulation in species from worms to humans. Evolutionary theories of aging provide a basis to understand how age regulation of a genetic pathway might be preserved between distantly related species.
Aging is a complex process driven by diverse molecular pathways and biochemical events. The gradual decline in cellular functions associated with aging is not caused by changes in the expression or activity of just a few individual genes, but rather by the cumulative changes from many genes. To elucidate molecular differences associated with aging, an attractive approach is to use DNA microarrays to scan the entire genome for genes that change expression with age. The set of age-regulated genes provides a comprehensive and unbiased view of molecular changes associated with age. The identities of particular age-regulated genes may suggest specific mechanisms for aging; e.g. decreased expression of the electron transport chain genes in old age suggests changes in energy generation and oxidative damage at the end of life. Furthermore, analysis of the entire set of age-regulated genes (a gene expression profile) can reveal emergent themes about the aging process; e.g. evaluation of all age-regulated genes in normal and calorically restricted mice shows that caloric restriction may slow down age-related expression changes (Park and Prolla, 2005Go).
Nearly all organisms age, and yet lifespan can be very different between species. For example, among major model organisms, the worm Caenorhabditis elegans lives for 2 weeks, the fly Drosophila melanogaster lives for 2 months, the mouse Mus musculus lives for 2 years and humans lives for ~80 years. A great deal might be learned by comparing the aging process in different species, revealing why humans age so slowly compared with worms. For example, one could ask whether human cells are exceptionally well protected against mitochondrial oxidative damage, DNA damage or telomerase shortening compared with worm cells. Several recent papers have compiled gene expression profiles for aging from multiple species and then compared them to each other to distinguish aspects of aging that are species specific and those that are shared.
There is a rich set of literature on using DNA microarray experiments to profile gene expression differences for aging in worms, flies, mice and humans (Kim, in pressGo). Transcriptional profiles for aging contain quantitative data on age-related changes in expression for a large fraction of the genome. However, relatively few studies have integrated aging transcriptional profiles from different studies in a systematic way to find similarities and differences in aging among different species. Genes that show age-related transcriptional differences in multiple species are exceptionally interesting as biomarkers for age. Their age-related decline scales with lifespan, such that age-related changes occur relatively quickly in short-lived animals but slowly in long-lived ones. By contrast, genes that show age regulation in mice but not humans may help identify pathways and mechanisms that account for much longer lifespan in humans.
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
ScienceWeek is automatically sent by Email to all members of the Science and Politics Discussion Group.
To post to the group, send email to scipol@googlegroups.com
To unsubscribe from the group, send email to scipol-unsubscribe@googlegroups.com
For more options and to subscribe, visit the group at http://groups.google.com/group/scipol?hl=en
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
|