Two-Particle Correlations in Relativistic Heavy-Ion Collisions

Two-particle momentum correlations between pairs of identical particles produced in relativistic heavy-ion reactions can be analyzed to extract the space-time structure of the collision fireball. We review recent progress in the application of this method, based on newly developed theoretical tools and new high-quality data from heavy-ion collision experiments. Implications for our understanding of the collision dynamics and for the search for the quark-gluon plasma are discussed.Comment: 44 pages, LaTeX, 11 Figures, uses special style files (included), prepared for Ann. Rev. Nucl. Part. Sci. 49 (1999). Error in Chapt. 1 corrected and a few references adde

Coverage, Continuity and Visual Cortical Architecture

The primary visual cortex of many mammals contains a continuous representation of visual space, with a roughly repetitive aperiodic map of orientation preferences superimposed. It was recently found that orientation preference maps (OPMs) obey statistical laws which are apparently invariant among species widely separated in eutherian evolution. Here, we examine whether one of the most prominent models for the optimization of cortical maps, the elastic net (EN) model, can reproduce this common design. The EN model generates representations which optimally trade of stimulus space coverage and map continuity. While this model has been used in numerous studies, no analytical results about the precise layout of the predicted OPMs have been obtained so far. We present a mathematical approach to analytically calculate the cortical representations predicted by the EN model for the joint mapping of stimulus position and orientation. We find that in all previously studied regimes, predicted OPM layouts are perfectly periodic. An unbiased search through the EN parameter space identifies a novel regime of aperiodic OPMs with pinwheel densities lower than found in experiments. In an extreme limit, aperiodic OPMs quantitatively resembling experimental observations emerge. Stabilization of these layouts results from strong nonlocal interactions rather than from a coverage-continuity-compromise. Our results demonstrate that optimization models for stimulus representations dominated by nonlocal suppressive interactions are in principle capable of correctly predicting the common OPM design. They question that visual cortical feature representations can be explained by a coverage-continuity-compromise.Comment: 100 pages, including an Appendix, 21 + 7 figure

Get Phases from Arsenic Anomalous Scattering: de novo SAD Phasing of Two Protein Structures Crystallized in Cacodylate Buffer

The crystal structures of two proteins, a putative pyrazinamidase/nicotinamidase from the dental pathogen Streptococcus mutans (SmPncA) and the human caspase-6 (Casp6), were solved by de novo arsenic single-wavelength anomalous diffraction (As-SAD) phasing method. Arsenic (As), an uncommonly used element in SAD phasing, was covalently introduced into proteins by cacodylic acid, the buffering agent in the crystallization reservoirs. In SmPncA, the only cysteine was bound to dimethylarsinoyl, which is a pentavalent arsenic group (As (V)). This arsenic atom and a protein-bound zinc atom both generated anomalous signals. The predominant contribution, however, was from the As anomalous signals, which were sufficient to phase the SmPncA structure alone. In Casp6, four cysteines were found to bind cacodyl, a trivalent arsenic group (As (III)), in the presence of the reducing agent, dithiothreitol (DTT), and arsenic atoms were the only anomalous scatterers for SAD phasing. Analyses and discussion of these two As-SAD phasing examples and comparison of As with other traditional heavy atoms that generate anomalous signals, together with a few arsenic-based de novo phasing cases reported previously strongly suggest that As is an ideal anomalous scatterer for SAD phasing in protein crystallography

Setting priorities for land management to mitigate climate change

<p>Abstract</p> <p>Background</p> <p>No consensus has been reached how to measure the effectiveness of climate change mitigation in the land-use sector and how to prioritize land use accordingly. We used the long-term cumulative and average sectorial C stocks in biomass, soil and products, C stock changes, the substitution of fossil energy and of energy-intensive products, and net present value (NPV) as evaluation criteria for the effectiveness of a hectare of productive land to mitigate climate change and produce economic returns. We evaluated land management options using real-life data of Thuringia, a region representative for central-western European conditions, and input from life cycle assessment, with a carbon-tracking model. We focused on solid biomass use for energy production.</p> <p>Results</p> <p>In forestry, the traditional timber production was most economically viable and most climate-friendly due to an assumed recycling rate of 80% of wood products for bioenergy. Intensification towards "pure bioenergy production" would reduce the average sectorial C stocks and the C substitution and would turn NPV negative. In the forest conservation (non-use) option, the sectorial C stocks increased by 52% against timber production, which was not compensated by foregone wood products and C substitution. Among the cropland options wheat for food with straw use for energy, whole cereals for energy, and short rotation coppice for bioenergy the latter was most climate-friendly. However, specific subsidies or incentives for perennials would be needed to favour this option.</p> <p>Conclusions</p> <p>When using the harvested products as materials prior to energy use there is no climate argument to support intensification by switching from sawn-wood timber production towards energy-wood in forestry systems. A legal framework would be needed to ensure that harvested products are first used for raw materials prior to energy use. Only an effective recycling of biomaterials frees land for long-term sustained C sequestration by conservation. Reuse cascades avoid additional emissions from shifting production or intensification.</p

Profiling Early Lung Immune Responses in the Mouse Model of Tuberculosis

Tuberculosis (TB) is caused by the intracellular bacteria Mycobacterium tuberculosis, and kills more than 1.5 million people every year worldwide. Immunity to TB is associated with the accumulation of IFNγ-producing T helper cell type 1 (Th1) in the lungs, activation of M.tuberculosis-infected macrophages and control of bacterial growth. However, very little is known regarding the early immune responses that mediate accumulation of activated Th1 cells in the M.tuberculosis-infected lungs. To define the induction of early immune mediators in the M.tuberculosis-infected lung, we performed mRNA profiling studies and characterized immune cells in M.tuberculosis-infected lungs at early stages of infection in the mouse model. Our data show that induction of mRNAs involved in the recognition of pathogens, expression of inflammatory cytokines, activation of APCs and generation of Th1 responses occurs between day 15 and day 21 post infection. The induction of these mRNAs coincides with cellular accumulation of Th1 cells and activation of myeloid cells in M.tuberculosis-infected lungs. Strikingly, we show the induction of mRNAs associated with Gr1+ cells, namely neutrophils and inflammatory monocytes, takes place on day 12 and coincides with cellular accumulation of Gr1+ cells in M.tuberculosis-infected lungs. Interestingly, in vivo depletion of Gr1+ neutrophils between days 10–15 results in decreased accumulation of Th1 cells on day 21 in M.tuberculosis-infected lungs without impacting overall protective outcomes. These data suggest that the recruitment of Gr1+ neutrophils is an early event that leads to production of chemokines that regulate the accumulation of Th1 cells in the M.tuberculosis-infected lungs

Ovarian cancer

Ovarian cancer is not a single disease and can be subdivided into at least five different histological subtypes that have different identifiable risk factors, cells of origin, molecular compositions, clinical features and treatments. Ovarian cancer is a global problem, is typically diagnosed at a late stage and has no effective screening strategy. Standard treatments for newly diagnosed cancer consist of cytoreductive surgery and platinum-based chemotherapy. In recurrent cancer, chemotherapy, anti-angiogenic agents and poly(ADP-ribose) polymerase inhibitors are used, and immunological therapies are currently being tested. High-grade serous carcinoma (HGSC) is the most commonly diagnosed form of ovarian cancer and at diagnosis is typically very responsive to platinum-based chemotherapy. However, in addition to the other histologies, HGSCs frequently relapse and become increasingly resistant to chemotherapy. Consequently, understanding the mechanisms underlying platinum resistance and finding ways to overcome them are active areas of study in ovarian cancer. Substantial progress has been made in identifying genes that are associated with a high risk of ovarian cancer (such as BRCA1 and BRCA2), as well as a precursor lesion of HGSC called serous tubal intraepithelial carcinoma, which holds promise for identifying individuals at high risk of developing the disease and for developing prevention strategies

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe