How political scientists got Trump exactly wrong

One of the major casualties of the 2016 election season has been the reputation of political science, a discipline whose practitioners had largely dismissed Donald Trump’s chances of gaining the Republican nomination. Lloyd Gruber describes just how wrong political scientists were about Trump, and explains why they should have been able to predict his success. Looking ahead to the fall general election, he questions whether voters will want Trump’s trigger-happy fingers on America’s nuclear button and suggests the remoteness of a Trump presidency is what had made it safe for primary voters to support him

Studying for an MPA: the two year advantage

Many policy schools now offer an MPA or MPP in one year, but our LSE MPA remains two years. Why? Dr Lloyd Gruber, the Founding Dean of LSE’s Master of Public Administration explains the advantages of studying our two year MPA programme

Analysing the Trump of the matter

Trump’s unconventional political performance has grasped the attention of the global media leaving many baffled by what his popularity means for politics today, and, could mean for the international community tomorrow. Lloyd Gruber and Rajesh Venugopal, both lecturers in the department of International Development, write about an election campaign that has left people more on the edge of their seats then a Hollywood blockbuster

Sampling re-design increases power to detect change in the Great Barrier Reef’s inshore water quality

Monitoring programs are fundamental to understanding the state and trend of aquatic ecosystems. Sampling designs are a crucial component of monitoring programs and ensure that measurements evaluate progress toward clearly stated management objectives, which provides a mechanism for adaptive management. Here, we use a well-established marine monitoring program for inshore water quality in the Great Barrier Reef (GBR), Australia to investigate whether a sampling re-design has increased the program’s capacity to meet its primary objectives. Specifically, we use bootstrap resampling to assess the change in statistical power to detect temporal water quality trends in a 15-year inshore marine water quality data set that includes data from both before and after the sampling re-design. We perform a comprehensive power analysis for six water quality analytes at four separate study areas in the GBR Marine Park and find that the sampling re-design (i) increased power to detect trends in 23 of the 24 analyte-study area combinations, and (ii) resulted in an average increase in power of 34% to detect increasing or decreasing trends in water quality analytes. This increase in power is attributed more to the addition of sampling locations than increasing the sampling rate. Therefore, the sampling re-design has substantially increased the capacity of the program to detect temporal trends in inshore marine water quality. Further improvements in sampling design need to focus on the program’s capability to reliably detect trends within realistic timeframes where inshore improvements to water quality can be expected to occur

Hybrid Rules with Well-Founded Semantics

A general framework is proposed for integration of rules and external first order theories. It is based on the well-founded semantics of normal logic programs and inspired by ideas of Constraint Logic Programming (CLP) and constructive negation for logic programs. Hybrid rules are normal clauses extended with constraints in the bodies; constraints are certain formulae in the language of the external theory. A hybrid program is a pair of a set of hybrid rules and an external theory. Instances of the framework are obtained by specifying the class of external theories, and the class of constraints. An example instance is integration of (non-disjunctive) Datalog with ontologies formalized as description logics. The paper defines a declarative semantics of hybrid programs and a goal-driven formal operational semantics. The latter can be seen as a generalization of SLS-resolution. It provides a basis for hybrid implementations combining Prolog with constraint solvers. Soundness of the operational semantics is proven. Sufficient conditions for decidability of the declarative semantics, and for completeness of the operational semantics are given

Carbon isotope discrimination of arctic and boreal biomes inferred from remote atmospheric measurements and a biosphere-atmosphere model

Estimating discrimination against ^(13)C during photosynthesis at landscape, regional, and biome scales is difficult because of large-scale variability in plant stress, vegetation composition, and photosynthetic pathway. Here we present estimates of ^(13)C discrimination for northern biomes based on a biosphere-atmosphere model and on National Oceanic and Atmospheric Administration Climate Monitoring and Diagnostics Laboratory and Institute of Arctic and Alpine Research remote flask measurements. With our inversion approach, we solved for three ecophysiological parameters of the northern biosphere (^(13)C discrimination, a net primary production light use efficiency, and a temperature sensitivity of heterotrophic respiration (a Q10 factor)) that provided a best fit between modeled and observed δ^(13)C and CO_2. In our analysis we attempted to explicitly correct for fossil fuel emissions, remote C4 ecosystem fluxes, ocean exchange, and isotopic disequilibria of terrestrial heterotrophic respiration caused by the Suess effect. We obtained a photosynthetic discrimination for arctic and boreal biomes between 19.0 and 19.6‰. Our inversion analysis suggests that Q10 and light use efficiency values that minimize the cost function covary. The optimal light use efficiency was 0.47 gC MJ^(−1) photosynthetically active radiation, and the optimal Q10 value was 1.52. Fossil fuel and ocean exchange contributed proportionally more to month-to-month changes in the atmospheric growth rate of δ^(13)C and CO_2 during winter months, suggesting that remote atmospheric observations during the summer may yield more precise estimates of the isotopic composition of the biosphere

First-year Results of Broadband Spectroscopy of the Brightest Fermi-GBM Gamma-Ray Bursts

We present here our results of the temporal and spectral analysis of a sample of 52 bright and hard gamma-ray bursts (GRBs) observed with the Fermi Gamma-ray Burst Monitor (GBM) during its first year of operation (July 2008-July 2009). Our sample was selected from a total of 253 GBM GRBs based on each event peak count rate measured between 0.2 and 40MeV. The final sample comprised 34 long and 18 short GRBs. These numbers show that the GBM sample contains a much larger fraction of short GRBs, than the CGRO/BATSE data set, which we explain as the result of our (different) selection criteria and the improved GBM trigger algorithms, which favor collection of short, bright GRBs over BATSE. A first by-product of our selection methodology is the determination of a detection threshold from the GBM data alone, above which GRBs most likely will be detected in the MeV/GeV range with the Large Area Telescope (LAT) onboard Fermi. This predictor will be very useful for future multiwavelength GRB follow ups with ground and space based observatories. Further we have estimated the burst durations up to 10MeV and for the first time expanded the duration-energy relationship in the GRB light curves to high energies. We confirm that GRB durations decline with energy as a power law with index approximately -0.4, as was found earlier with the BATSE data and we also notice evidence of a possible cutoff or break at higher energies. Finally, we performed time-integrated spectral analysis of all 52 bursts and compared their spectral parameters with those obtained with the larger data sample of the BATSE data. We find that the two parameter data sets are similar and confirm that short GRBs are in general harder than longer ones.Comment: 40 pages, 11 figures, 3 tables, Submitted to Ap

Protein disulfide-isomerase interacts with a substrate protein at all stages along its folding pathway

In contrast to molecular chaperones that couple protein folding to ATP hydrolysis, protein disulfide-isomerase (PDI) catalyzes protein folding coupled to formation of disulfide bonds (oxidative folding). However, we do not know how PDI distinguishes folded, partly-folded and unfolded protein substrates. As a model intermediate in an oxidative folding pathway, we prepared a two-disulfide mutant of basic pancreatic trypsin inhibitor (BPTI) and showed by NMR that it is partly-folded and highly dynamic. NMR studies show that it binds to PDI at the same site that binds peptide ligands, with rapid binding and dissociation kinetics; surface plasmon resonance shows its interaction with PDI has a Kd of ca. 10−5 M. For comparison, we characterized the interactions of PDI with native BPTI and fully-unfolded BPTI. Interestingly, PDI does bind native BPTI, but binding is quantitatively weaker than with partly-folded and unfolded BPTI. Hence PDI recognizes and binds substrates via permanently or transiently unfolded regions. This is the first study of PDI's interaction with a partly-folded protein, and the first to analyze this folding catalyst's changing interactions with substrates along an oxidative folding pathway. We have identified key features that make PDI an effective catalyst of oxidative protein folding – differential affinity, rapid ligand exchange and conformational flexibility

Connective Tissue Growth Factor Causes Persistent Proα2(I) Collagen Gene Expression Induced by Transforming Growth Factor-β in a Mouse Fibrosis Model

Skin fibrotic disorders such as systemic sclerosis (SSc) are characterized by an excessive production of extracellular matrix (ECM) and understood to develop under the influence of certain growth factors. Connective tissue growth factor (CTGF) is a cysteine-rich mitogenic peptide that is implicated in various fibrotic disorders and induced in fibroblasts after activation with transforming growth factor-beta (TGF-beta). To better understand the mechanisms of persistent fibrosis seen in SSc, we previously established an animal model of skin fibrosis induced by exogenous application of growth factors. In this model, TGF-beta transiently induced subcutaneous fibrosis and serial injections of CTGF after TGF-beta caused persistent fibrosis. To further define the mechanisms of skin fibrosis induced by TGF-beta and CTGF in vivo, we investigated in this study, the effects of growth factors on the promoter activity of the pro alpha 2 (1) collagen (COL1A2) gene in skin fibrosis. For this purpose, we utilized transgenic reporter mice harboring the -17 kb promoter sequence of the mouse COL1A2 linked to either a firefly luciferase gene or a bacterial P-galactosidase gene. Serial injections of CTGF after TGF-beta resulted in a sustained elevation of COL1A2 mRNA expression and promoter activity compared with consecutive injection of TGF-beta alone on day 8. We also demonstrated that the number of fibroblasts with activated COL1A2 transcription was increased by serial injections of CTGF after TGF-beta in comparison with the injection of TGF-beta alone. Furthermore, the serial injections recruited mast cells and macrophages. The number of mast cells reached a maximum on day 4 and remained relatively high up to day 8. In contrast to the kinetics of mast cells, the number of macrophages was increased on day 4 and continued to rise during the subsequent consecutive CTGF injections until day 8. These results suggested that CTGF maintains TGF-beta-induced skin fibrosis by sustaining COL1A2 promoter activation and increasing the number of activated fibroblasts. The infiltrated mast cells and macrophages may also contribute to the maintenance of fibrosis