Subject: [SciPol 572] ScienceWeek May 4, 2007

From: "Dan Agin" <[email-masked]>
Date: Fri, 04 May 2007 10:17:56 -0500

SCIENCEWEEK

May 4, 2007

Vol. 11 - Number 17

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our cathedral.

-- John Archibald Wheeler

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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

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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