Thermal and electrical conductivity of iron at Earth's core conditions

The Earth acts as a gigantic heat engine driven by the decay of radiogenic isotopes and slow cooling, which gives rise to plate tectonics, volcanoes and mountain building. Another key product is the geomagnetic field, generated in the liquid iron core by a dynamo running on heat released by cooling and freezing (as the solid inner core grows), and on chemical convection (due to light elements expelled from the liquid on freezing). The power supplied to the geodynamo, measured by the heat flux across the core-mantle boundary (CMB), places constraints on Earth's evolution. Estimates of CMB heat flux depend on properties of iron mixtures under the extreme pressure and temperature conditions in the core, most critically on the thermal and electrical conductivities. These quantities remain poorly known because of inherent experimental and theoretical difficulties. Here we use density functional theory to compute these conductivities in liquid iron mixtures at core conditions from first principles--unlike previous estimates, which relied on extrapolations. The mixtures of iron, oxygen, sulphur and silicon are taken from earlier work and fit the seismologically determined core density and inner-core boundary density jump. We find both conductivities to be two to three times higher than estimates in current use. The changes are so large that core thermal histories and power requirements need to be reassessed. New estimates indicate that the adiabatic heat flux is 15 to 16 terawatts at the CMB, higher than present estimates of CMB heat flux based on mantle convection; the top of the core must be thermally stratified and any convection in the upper core must be driven by chemical convection against the adverse thermal buoyancy or lateral variations in CMB heat flow. Power for the geodynamo is greatly restricted, and future models of mantle evolution will need to incorporate a high CMB heat flux and explain the recent formation of the inner core.     

The landscape of cancer genes and mutational processes in breast cancer

All cancers carry somatic mutations in their genomes. A subset, known as driver mutations, confer clonal selective advantage on cancer cells and are causally implicated in oncogenesis, and the remainder are passenger mutations. The driver mutations and mutational processes operative in breast cancer have not yet been comprehensively explored. Here we examine the genomes of 100 tumours for somatic copy number changes and mutations in the coding exons of protein-coding genes. The number of somatic mutations varied markedly between individual tumours. We found strong correlations between mutation number, age at which cancer was diagnosed and cancer histological grade, and observed multiple mutational signatures, including one present in about ten per cent of tumours characterized by numerous mutations of cytosine at TpC dinucleotides. Driver mutations were identified in several new cancer genes including AKT2, ARID1B, CASP8, CDKN1B, MAP3K1, MAP3K13, NCOR1, SMARCD1 and TBX3. Among the 100 tumours, we found driver mutations in at least 40 cancer genes and 73 different combinations of mutated cancer genes. The results highlight the substantial genetic diversity underlying this common disease.     

Global variation in copy number in the human genome

Copy number variation (CNV) of DNA sequences is functionally significant but has yet to be fully ascertained. We have constructed a first-generation CNV map of the human genome through the study of 270 individuals from four populations with ancestry in Europe, Africa or Asia (the HapMap collection). DNA from these individuals was screened for CNV using two complementary technologies: single-nucleotide polymorphism (SNP) genotyping arrays, and clone-based comparative genomic hybridization. A total of 1,447 copy number variable regions (CNVRs), which can encompass overlapping or adjacent gains or losses, covering 360 megabases (12% of the genome) were identified in these populations. These CNVRs contained hundreds of genes, disease loci, functional elements and segmental duplications. Notably, the CNVRs encompassed more nucleotide content per genome than SNPs, underscoring the importance of CNV in genetic diversity and evolution. The data obtained delineate linkage disequilibrium patterns for many CNVs, and reveal marked variation in copy number among populations. We also demonstrate the utility of this resource for genetic disease studies.     

Why do funding agencies favor hypothesis testing?

Exploratory inquiry has difficulty attracting research funding because funding agencies have little sense of how to detect good science in exploratory contexts. After documenting and explaining the focus on hypothesis testing among a variety of institutions responsible for distinguishing between good and bad science, I analyze the NIH grant review process. I argue that a good explanation for the focus on hypothesis testing—at least at the level of science funding agencies—is the fact that hypothesis-driven research is relatively easy to appraise. I then explore one method by which we might gauge the epistemic merits of different styles of inquiry.