Historical costs of coal-fired electricity and implications for the future

We study the cost of coal-fired electricity in the United States between 1882 and 2006 by decomposing it in terms of the price of coal, transportation cost, energy density, thermal efficiency, plant construction cost, interest rate, capacity factor, and operations and maintenance cost. The dominant determinants of cost have been the price of coal and plant construction cost. The price of coal appears to fluctuate more or less randomly while the construction cost follows long-term trends, decreasing from 1902 to 1970, increasing from 1970 to 1990, and leveling off since then. Our analysis emphasizes the importance of using long time series and comparing electricity generation technologies using decomposed total costs, rather than costs of single components like capital. By taking this approach we find that the history of coal-fired electricity suggests there is a fluctuating floor to its future costs, which is determined by coal prices. Even if construction costs resumed a decreasing trend, the cost of coal-based electricity would drop for a while but eventually be determined by the price of coal, which fluctuates while showing no long-term trend.National Science Foundation (U.S.) (NSF Grant SBE0738187

Network Properties of Economic Input-Output Networks

This paper investigates applications of network analysis to national input-output tables. This includes initial steps to become familiar with sources for input-output data and the assumptions that go into their compilation; traditional input-output analysis; ecological input-output metrics; the difficulties in the analysis of weighted, directed graphs; the overall structure of economic input-output networks; and possible bases for comparison of network metrics. Both quantitative and qualitative regularities were observed across the OECD economies. Specifically, flow sizes and industry sizes appear to follow the same distribution for all OECD countries; the overall structure of flows within the network, as characterized by the relative amount of cycled and first-passage flow, followed a similar pattern for most OECD countries; and similar groups of closely connected sectors were found. More work needs to be done to understand these results in depth. Directions for future research are outlined; in particular, exploring (1) the stability of these results to IO data with different levels of detail, (2) community structure within the IO networks, and (3) generative/dynamic models of IO networks

How production networks amplify economic growth

Technological improvement is the most important cause of long-term economic growth. We study the effects of technology improvement in the setting of a production network, in which each producer buys input goods and converts them to other goods, selling the product to households or other producers. We show how this network amplifies the effects of technological improvements as they propagate along chains of production. Longer production chains for an industry bias it towards faster price reduction, and longer production chains for a country bias it towards faster GDP growth. These predictions are in good agreement with data and improve with the passage of time, demonstrating a key influence of production chains in price change and output growth over the long term

Safe food and feed through an integrated toolbox for mycotoxin management: the MyToolBox approach

There is a pressing need to mobilise the wealth of knowledge from the international mycotoxin research conductedover the past 25-30 years, and to perform cutting-edge research where knowledge gaps still exist. This knowledgeneeds to be integrated into affordable and practical tools for farmers and food processors along the chain inorder to reduce the risk of mycotoxin contamination of crops, feed and food. This is the mission of MyToolBox – a four-year project which has received funding from the European Commission. It mobilises a multi-actorpartnership (academia, farmers, technology small and medium sized enterprises, food industry and policystakeholders) to develop novel interventions aimed at achieving a significant reduction in crop losses due tomycotoxin contamination. Besides a field-to-fork approach, MyToolBox also considers safe use options ofcontaminated batches, such as the efficient production of biofuels. Compared to previous efforts of mycotoxin reduction strategies, the distinguishing feature of MyToolBox is to provide the recommended measures to theend users along the food and feed chain in a web-based MyToolBox platform (e-toolbox). The project focuseson small grain cereals, maize, peanuts and dried figs, applicable to agricultural conditions in the EU and China. Crop losses using existing practices are being compared with crop losses after novel pre-harvest interventionsincluding investigation of genetic resistance to fungal infection, cultural control (e.g. minimum tillage or cropdebris treatment), the use of novel biopesticides suitable for organic farming, competitive biocontrol treatment and development of novel modelling approaches to predict mycotoxin contamination. Research into post-harvestmeasures includes real-time monitoring during storage, innovative sorting of crops using vision-technology, novelmilling technology and studying the effects of baking on mycotoxins at an industrial scale

PolyTB: a genomic variation map for Mycobacterium tuberculosis

Tuberculosis (TB) caused by Mycobacterium tuberculosis (Mtb) is the second major cause of death from an infectious disease worldwide. Recent advances in DNA sequencing are leading to the ability to generate whole genome information in clinical isolates of M. tuberculosis complex (MTBC). The identification of informative genetic variants such as phylogenetic markers and those associated with drug resistance or virulence will help barcode Mtb in the context of epidemiological, diagnostic and clinical studies. Mtb genomic datasets are increasingly available as raw sequences, which are potentially difficult and computer intensive to process, and compare across studies. Here we have processed the raw sequence data (>1500 isolates, eight studies) to compile a catalogue of SNPs (n = 74,039, 63% non-synonymous, 51.1% in more than one isolate, i.e. non-private), small indels (n = 4810) and larger structural variants (n = 800). We have developed the PolyTB web-based tool (http://pathogenseq.lshtm.ac.uk/polytb) to visualise the resulting variation and important meta-data (e.g. in silico inferred strain-types, location) within geographical map and phylogenetic views. This resource will allow researchers to identify polymorphisms within candidate genes of interest, as well as examine the genomic diversity and distribution of strains. PolyTB source code is freely available to researchers wishing to develop similar tools for their pathogen of interest

Field test of a novel detection device for Mycobacterium tuberculosis antigen in cough

BACKGROUND: Tuberculosis is a highly infectious disease that is spread from person to person by infected aerosols emitted by patients with respiratory forms of the disease. We describe a novel device that utilizes immunosensor and bio-optical technology to detect M. tuberculosis antigen (Ag85B) in cough and demonstrate its use under field conditions during a pilot study in an area of high TB incidence. METHODS: The TB Breathalyzer device (Rapid Biosensor Systems Ltd) was field tested in the outpatient clinic of Adama Hospital, Ethiopia. Adults seeking diagnosis for respiratory complaints were tested. Following nebulization with 0.9% saline patients were asked to cough into a disposable collection device where cough aerosols were deposited. Devices were then inserted into a portable instrument to assess whether antigen was present in the sample. Demographic and clinical data were recorded and all patients were subjected to chest radiogram and examination of sputum by Ziehl-Nielsen microscopy. In the absence of culture treatment decisions were based on smear microscopy, chest x-ray and clinical assessment. Breathalyzer testing was undertaken by a separate physician to triage and diagnostic assessment. RESULTS: Sixty individuals were each subjected to a breathalyzer test. The procedure was well tolerated and for each patient the testing was completed in less than 10 min. Positive breath test results were recorded for 29 (48%) patients. Of 31 patients with a diagnosis of tuberculosis 23 (74%; 95% CI 55-87) were found positive for antigen in their breath and 20 (64%; 95% CI 45-80) were smear positive for acid fast bacilli in their sputum. Six patients provided apparent false positive breathalyzer results that did not correlate with a diagnosis of tuberculosis. CONCLUSIONS: We propose that the breathalyzer device described warrants further investigation as a tool for studying exhalation of M. tuberculosis. The portability, simplicity of use and speed of the test device suggest it may also find use as a tool to aid early identification of infectious cases. We recommend studies be undertaken to determine the diagnostic sensitivity and specificity of the device when compared to microbiological and clinical indicators of tuberculosis disease

Machine learning predicts accurately mycobacterium tuberculosis drug resistance from whole genome sequencing data

Background: Tuberculosis disease, caused by Mycobacterium tuberculosis, is a major public health problem. The emergence of M. tuberculosis strains resistant to existing treatments threatens to derail control efforts. Resistance is mainly conferred by mutations in genes coding for drug targets or converting enzymes, but our knowledge of these mutations is incomplete. Whole genome sequencing (WGS) is an increasingly common approach to rapidly characterize isolates and identify mutations predicting antimicrobial resistance and thereby providing a diagnostic tool to assist clinical decision making. Methods: We applied machine learning approaches to 16,688 M. tuberculosis isolates that have undergone WGS and laboratory drug-susceptibility testing (DST) across 14 antituberculosis drugs, with 22.5% of samples being multidrug resistant and 2.1% being extensively drug resistant. We used non-parametric classification-tree and gradientboosted-tree models to predict drug resistance and uncover any associated novel putative mutations. We fitted separate models for each drug, with and without “co-occurrent resistance” markers known to be causing resistance to drugs other than the one of interest. Predictive performance was measured using sensitivity, specificity, and the area under the receiver operating characteristic curve, assuming DST results as the gold standard. Results: The predictive performance was highest for resistance to first-line drugs, amikacin, kanamycin, ciprofloxacin, moxifloxacin, and multidrug-resistant tuberculosis (area under the receiver operating characteristic curve above 96%), and lowest for thirdline drugs such as D-cycloserine and Para-aminosalisylic acid (area under the curve below 85%). The inclusion of co-occurrent resistance markers led to improved performance for some drugs and superior results when compared to similar models in other largescale studies, which had smaller sample sizes. Overall, the gradient-boosted-tree models performed better than the classification-tree models. The mutation-rank analysis detected no new single nucleotide polymorphisms linked to drug resistance. Discordance between DST and genotypically inferred resistance may be explained by DST errors, novel rare mutations, hetero-resistance, and nongenomic drivers such as efflux-pump upregulation. Conclusion: Our work demonstrates the utility of machine learning as a flexible approach to drug resistance prediction that is able to accommodate a much larger number of predictors and to summarize their predictive ability, thus assisting clinical decision making and single nucleotide polymorphism detection in an era of increasing WGS data generation

Statistical Basis for Predicting Technological Progress

Forecasting technological progress is of great interest to engineers, policy makers, and private investors. Several models have been proposed for predicting technological improvement, but how well do these models perform? An early hypothesis made by Theodore Wright in 1936 is that cost decreases as a power law of cumulative production. An alternative hypothesis is Moore's law, which can be generalized to say that technologies improve exponentially with time. Other alternatives were proposed by Goddard, Sinclair et al., and Nordhaus. These hypotheses have not previously been rigorously tested. Using a new database on the cost and production of 62 different technologies, which is the most expansive of its kind, we test the ability of six different postulated laws to predict future costs. Our approach involves hindcasting and developing a statistical model to rank the performance of the postulated laws. Wright's law produces the best forecasts, but Moore's law is not far behind. We discover a previously unobserved regularity that production tends to increase exponentially. A combination of an exponential decrease in cost and an exponential increase in production would make Moore's law and Wright's law indistinguishable, as originally pointed out by Sahal. We show for the first time that these regularities are observed in data to such a degree that the performance of these two laws is nearly tied. Our results show that technological progress is forecastable, with the square root of the logarithmic error growing linearly with the forecasting horizon at a typical rate of 2.5% per year. These results have implications for theories of technological change, and assessments of candidate technologies and policies for climate change mitigation

Dominant Role of Oncogene Dosage and Absence of Tumor Suppressor Activity in Nras-Driven Hematopoietic Transformation

Biochemical properties of Ras oncoproteins and their transforming ability strongly support a dominant mechanism of action in tumorigenesis. However, genetic studies unexpectedly suggested that wild-type (WT) Ras exerts tumor suppressor activity. Expressing oncogenic Nras[superscript G12D] in the hematopoietic compartment of mice induces an aggressive myeloproliferative neoplasm that is exacerbated in homozygous mutant animals. Here, we show that increased Nras[superscript G12D] gene dosage, but not inactivation of WT Nras, underlies the aggressive in vivo behavior of Nras[superscript G12D over G12D] hematopoietic cells. Modulating Nras[superscript G12D] dosage had discrete effects on myeloid progenitor growth, signal transduction, and sensitivity to MAP-ERK kinase (MEK) inhibition. Furthermore, enforced WT N-Ras expression neither suppressed the growth of Nras-mutant cells nor inhibited myeloid transformation by exogenous Nras[superscript G12D]. Importantly, NRAS expression increased in human cancer cell lines with NRAS mutations. These data have therapeutic implications and support reconsidering the proposed tumor suppressor activity of WT Ras in other cancers.Pfizer Inc. (PD0325901)National Institutes of Health (U.S.) (Grant R37CA72614)National Institutes of Health (U.S.) (Grant P01CA40046)National Institutes of Health (U.S.) (Grant K08CA134649)Leukemia & Lymphoma Society of America (Specialized Center of Research Award LLS 7019-04))American Lebanese Syrian Associated Charitie

Emergence of Multidrug-Resistant Mycobacterium Tuberculosis of the Beijing Lineage in Portugal and Guinea-Bissau: a Snapshot of Moving Clones by Whole-Genome Sequencing

The Beijing genotype comprises a highly disseminated strain type that is frequently associated with multidrug resistant (MDR) tuberculosis (TB) and increased transmissibility but, countries such as Portugal and Guinea-Bissau fall outside the regions phylogeographically associated with this specific genotype. Nevertheless, recent data shows that this genotype might be gradually emerging in these two countries as an underlying cause of primary MDR-TB. Here, we describe the emergence of Mycobacterium tuberculosis Beijing strains associated with MDR-TB in Portugal and Guinea-Bissau demonstrating the presence of the well described superclusters 100-32 and 94-32 in Portugal and Guinea-Bissau, respectively. Genome-wide analysis and comparison with a global genomic dataset of M. tuberculosis Beijing strains, revealed the presence of two genomic clusters encompassing isolates from Portugal and Guinea-Bissau, GC1 (n = 121) and GC2 (n = 39), both of which bore SNP signatures compatible with the 100-32/B0/W148 and 94-32/Central Asia Outbreak clades, respectively. Moreover, GC2 encompasses a cross-border cluster between Portugal, Guinea-Bissau and Brazil thus supporting migration-associated introduction of MDR-TB and subsequent clonal expansion at the community-level. The comparison with global Beijing datasets demonstrates the global reach of the disease and its complex dissemination across multiple countries while in parallel there are clear microevolutionary trajectories towards extensively drug resistant TB.info:eu-repo/semantics/publishedVersio