APPLEBAUM SCHOLARSHIP PAPER: EXPORT BEHAVIOR AND ATTITUDES OF AUSTRALASIA MEAT INDUSTRY FIRMS: A NORTH AMERICAN MARKET FOCUS

International Relations/Trade,

Reduced emissions from deforestation and forest degradation (REDD): a climate change mitigation strategy on a critical track

<p>Abstract</p> <p>Background</p> <p>Following recent discussions, there is hope that a mechanism for reduction of emissions from deforestation and forest degradation (REDD) will be agreed by the Parties of the UNFCCC at their 15th meeting in Copenhagen in 2009 as an eligible action to prevent climate changes and global warming in post-2012 commitment periods. Countries introducing a REDD-regime in order to generate benefits need to implement sound monitoring and reporting systems and specify the associated uncertainties. The principle of conservativeness addresses the problem of estimation errors and requests the reporting of reliable minimum estimates (RME). Here the potential to generate benefits from applying a REDD-regime is proposed with reference to sampling and non-sampling errors that influence the reliability of estimated activity data and emission factors.</p> <p>Results</p> <p>A framework for calculating carbon benefits by including assessment errors is developed. Theoretical, sample based considerations as well as a simulation study for five selected countries with low to high deforestation and degradation rates show that even small assessment errors (5% and less) may outweigh successful efforts to reduce deforestation and degradation.</p> <p>Conclusion</p> <p>The generation of benefits from REDD is possible only in situations where assessment errors are carefully controlled.</p

The Dynamic Striated Metropolis-Hastings Sampler for High-Dimensional Models

Having efficient and accurate samplers for simulating the posterior distribution is crucial for Bayesian analysis. We develop a generic posterior simulator called the "dynamic striated Metropolis-Hastings (DSMH)" sampler. Grounded in the Metropolis-Hastings algorithm, it draws its strengths from both the equienergy sampler and the sequential Monte Carlo sampler by avoiding the weaknesses of the straight Metropolis-Hastings algorithm as well as those of importance sampling. In particular, the DSMH sampler possesses the capacity to cope with incredibly irregular distributions that are full of winding ridges and multiple peaks and has the flexibility to take full advantage of parallelism on either desktop computers or clusters. The high-dimensional application studied in this paper provides a natural platform to put to the test generic samplers such as the DSMH sampler

Genome-wide analysis of ivermectin response by Onchocerca volvulus reveals that genetic drift and soft selective sweeps contribute to loss of drug sensitivity

Treatment of onchocerciasis using mass ivermectin administration has reduced morbidity and transmission throughout Africa and Central/South America. Mass drug administration is likely to exert selection pressure on parasites, and phenotypic and genetic changes in several Onchocerca volvulus populations from Cameroon and Ghana-exposed to more than a decade of regular ivermectin treatment-have raised concern that sub-optimal responses to ivermectin's anti-fecundity effect are becoming more frequent and may spread.Pooled next generation sequencing (Pool-seq) was used to characterise genetic diversity within and between 108 adult female worms differing in ivermectin treatment history and response. Genome-wide analyses revealed genetic variation that significantly differentiated good responder (GR) and sub-optimal responder (SOR) parasites. These variants were not randomly distributed but clustered in ~31 quantitative trait loci (QTLs), with little overlap in putative QTL position and gene content between the two countries. Published candidate ivermectin SOR genes were largely absent in these regions; QTLs differentiating GR and SOR worms were enriched for genes in molecular pathways associated with neurotransmission, development, and stress responses. Finally, single worm genotyping demonstrated that geographic isolation and genetic change over time (in the presence of drug exposure) had a significantly greater role in shaping genetic diversity than the evolution of SOR.This study is one of the first genome-wide association analyses in a parasitic nematode, and provides insight into the genomics of ivermectin response and population structure of O. volvulus. We argue that ivermectin response is a polygenically-determined quantitative trait (QT) whereby identical or related molecular pathways but not necessarily individual genes are likely to determine the extent of ivermectin response in different parasite populations. Furthermore, we propose that genetic drift rather than genetic selection of SOR is the underlying driver of population differentiation, which has significant implications for the emergence and potential spread of SOR within and between these parasite populations

Arboviruses as an unappreciated cause of non-malarial acute febrile illness in the Dschang Health District of western Cameroon

Acute febrile illness is a common problem managed by clinicians and health systems globally, particularly in the Tropics. In many regions, malaria is a leading and potentially deadly cause of fever; however, myriad alternative etiologies exist. Identifying the cause of fever allows optimal management, but this depends on many factors including thorough knowledge of circulating infections. Arboviruses such as dengue (DENV) cause fever and may be underdiagnosed in sub-Saharan Africa where malaria is a major focus. We examined cases of fever in western Cameroon that tested negative for malaria and found 13.5% (13/96) were due to DENV, with 75% (9/12) of these being DENV serotype 2 infections. Two complete DENV2 genomes were obtained and clustered closely to recent isolates from Senegal and Burkina Faso. The seroprevalence of DENV in this region was 24.8% (96/387). Neutralizing antibodies to DENV2 were detected in all (15/15) seropositive samples tested. Chikungunya (CHIKV) is an arthritogenic alphavirus that is transmitted by Aedes mosquitoes, the same principal vector as DENV. The seroprevalence for CHIKV was 15.7% (67/427); however, CHIKV did not cause a single case of fever in the 96 subjects tested. Of note, being seropositive for one arbovirus was associated with being seropositive for the other (Χ2 = 16.8, p<0.001). Taken together, these data indicate that Aedes-transmitted arboviruses are endemic in western Cameroon and are likely a common but underappreciated cause of febrile illness. This work supports the need for additional study of arboviruses in sub-Saharan Africa and efforts to improve diagnostic capacity, surveillance systems, and arbovirus prevention strategies

Markov-Switching Structural Vector Autoregressions: Theory and Application

This paper develops a new and easily implementable necessary and sufficient condition for the exact identification of a Markov-switching structural vector autoregression (SVAR) model. The theorem applies to models with both linear and some nonlinear restrictions on the structural parameters. We also derive efficient MCMC algorithms to implement sign and long-run restrictions in Markov-switching SVARs. Using our methods, four well-known identification schemes are used to study whether monetary policy has changed in the euro area since the introduction of the European Monetary Union. We find that models restricted to only time-varying shock variances dominate the other models. We find a persistent post-1993 regime that is associated with low volatility of shocks to output, prices, and interest rates. Finally, the output effects of monetary policy shocks are small and uncertain across regimes and models. These results are robust to the four identification schemes studied in this paper

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery