10 research outputs found
EU Energy Security in the Gas Sector: Evolving Dynamics, Policy Dilemmas and Prospects. By FILIPPOS PROEDROU. Pp. 192. Farnham: Ashgate. 2012. £55. Hardback. ISBN: 978-1-4094-3804-5.
Metabolic Rift or Metabolic Shift? Dialectics, Nature, and the World-Historical Method
Abstract In the flowering of Red-Green Thought over the past two decades, metabolic rift thinking is surely one of its most colorful varieties. The metabolic rift has captured the imagination of critical environmental scholars, becoming a shorthand for capitalism’s troubled relations in the web of life. This article pursues an entwined critique and reconstruction: of metabolic rift thinking and the possibilities for a post-Cartesian perspective on historical change, the world-ecology conversation. Far from dismissing metabolic rift thinking, my intention is to affirm its dialectical core. At stake is not merely the mode of explanation within environmental sociology. The impasse of metabolic rift thinking is suggestive of wider problems across the environmental social sciences, now confronted by a double challenge. One of course is the widespread—and reasonable—sense of urgency to evolve modes of thought appropriate to an era of deepening biospheric instability. The second is the widely recognized—but inadequately internalized—understanding that humans are part of nature
Inter-state relations and state capacity: the rise and fall of Chinese foreign direct investment in the Philippines
The Political Economy of Environmental Degradation and Climate Disaster in Southeast Asia
Long-term therapy with recombinant human erythropoietin (rHu-EPO) in progressing multiple myeloma
Large-scale targeted sequencing identifies risk genes for neurodevelopmental disorders
AbstractMost genes associated with neurodevelopmental disorders (NDDs) were identified with an excess of de novo mutations (DNMs) but the significance in case–control mutation burden analysis is unestablished. Here, we sequence 63 genes in 16,294 NDD cases and an additional 62 genes in 6,211 NDD cases. By combining these with published data, we assess a total of 125 genes in over 16,000 NDD cases and compare the mutation burden to nonpsychiatric controls from ExAC. We identify 48 genes (25 newly reported) showing significant burden of ultra-rare (MAF < 0.01%) gene-disruptive mutations (FDR 5%), six of which reach family-wise error rate (FWER) significance (p < 1.25E−06). Among these 125 targeted genes, we also reevaluate DNM excess in 17,426 NDD trios with 6,499 new autism trios. We identify 90 genes enriched for DNMs (FDR 5%; e.g., GABRG2 and UIMC1); of which, 61 reach FWER significance (p < 3.64E−07; e.g., CASZ1). In addition to doubling the number of patients for many NDD risk genes, we present phenotype–genotype correlations for seven risk genes (CTCF, HNRNPU, KCNQ3, ZBTB18, TCF12, SPEN, and LEO1) based on this large-scale targeted sequencing effort.</jats:p
Large-scale targeted sequencing identifies risk genes for neurodevelopmental disorders
Most genes associated with neurodevelopmental disorders (NDDs) were identified with an excess of de novo mutations (DNMs) but the significance in case–control mutation burden analysis is unestablished. Here, we sequence 63 genes in 16,294 NDD cases and an additional 62 genes in 6,211 NDD cases. By combining these with published data, we assess a total of 125 genes in over 16,000 NDD cases and compare the mutation burden to nonpsychiatric controls from ExAC. We identify 48 genes (25 newly reported) showing significant burden of ultra-rare (MAF < 0.01%) gene-disruptive mutations (FDR 5%), six of which reach family-wise error rate (FWER) significance (p < 1.25E−06). Among these 125 targeted genes, we also reevaluate DNM excess in 17,426 NDD trios with 6,499 new autism trios. We identify 90 genes enriched for DNMs (FDR 5%; e.g., GABRG2 and UIMC1); of which, 61 reach FWER significance (p < 3.64E−07; e.g., CASZ1). In addition to doubling the number of patients for many NDD risk genes, we present phenotype–genotype correlations for seven risk genes (CTCF, HNRNPU, KCNQ3, ZBTB18, TCF12, SPEN, and LEO1) based on this large-scale targeted sequencing effort
Author Correction: Large-scale targeted sequencing identifies risk genes for neurodevelopmental disorders
An amendment to this paper has been published and can be accessed via a link at the top of the paper.</jats:p
