Chicago Board of Trade Ethanol Contract Efficiency

Firms producing ethanol may find management of the price risk associated with production of this leading alternative fuel a key factor to continued success. As with other agricultural commodities, the influence and ability of futures contracts to serve as a risk management tool deserves attention.contract efficiency, ethanol, futures contracts, Crop Production/Industries, Risk and Uncertainty, Q13, Q43, M31,

Energetic efficiency of egg production and the influence of live weight thereon.

Publication authorized February 23, 1938.Digitized 2007 AES.Includes bibliographical references

Weight loss history as a predictor of weight loss: results from Phase I of the weight loss maintenance trial

Past studies have suggested that weight loss history is associated with subsequent weight loss. However, questions remain whether method and amount of weight lost in previous attempts impacts current weight loss efforts. This study utilized data from the Weight Loss Maintenance Trial to examine the association between weight loss history and weight loss outcomes in a diverse sample of high-risk individuals. Multivariate regression analysis was conducted to determine which specific aspects of weight loss history predict change in weight during a 6-month weight loss intervention. Greater weight loss was predicted by fewer previous weight loss attempts with assistance (p = 0.03), absence of previous dietary/herbal weight loss supplement use (p = 0.01), and greater maximum weight loss in previous attempts (p <0.001). Future interventions may benefit from assessment of weight loss history and tailoring of interventions based on past weight loss behaviors and outcomes

Weight Loss During the Intensive Intervention Phase of the Weight-Loss Maintenance Trial

To improve methods for long-term weight management, the Weight Loss Maintenance (WLM) trial, a four-center randomized trial, was conducted to compare alternative strategies for maintaining weight loss over a 30-month period. This paper describes methods and results for the initial 6-month weight-loss program (Phase I)

2′-O Methylation of the Viral mRNA Cap by West Nile Virus Evades Ifit1-Dependent and -Independent Mechanisms of Host Restriction In Vivo

Prior studies have shown that 2′-O methyltransferase activity of flaviviruses, coronaviruses, and poxviruses promotes viral evasion of Ifit1, an interferon-stimulated innate immune effector protein. Viruses lacking 2′-O methyltransferase activity exhibited attenuation in primary macrophages that was rescued in cells lacking Ifit1 gene expression. Here, we examined the role of Ifit1 in restricting pathogenesis in vivo of wild type WNV (WNV-WT) and a mutant in the NS5 gene (WNV-E218A) lacking 2′-O methylation of the 5′ viral RNA cap. While deletion of Ifit1 had marginal effects on WNV-WT pathogenesis, WNV-E218A showed increased replication in peripheral tissues of Ifit1−/− mice after subcutaneous infection, yet this failed to correlate with enhanced infection in the brain or lethality. In comparison, WNV-E218A was virulent after intracranial infection as judged by increased infection in different regions of the central nervous system (CNS) and a greater than 16,000-fold decrease in LD50 values in Ifit1−/− compared to wild type mice. Ex vivo infection experiments revealed cell-type specific differences in the ability of an Ifit1 deficiency to complement the replication defect of WNV-E218A. In particular, WNV-E218A infection was impaired in both wild type and Ifit1−/− brain microvascular endothelial cells, which are believed to participate in blood-brain barrier (BBB) regulation of virus entry into the CNS. A deficiency of Ifit1 also was associated with increased neuronal death in vivo, which was both cell-intrinsic and mediated by immunopathogenic CD8+ T cells. Our results suggest that virulent strains of WNV have largely evaded the antiviral effects of Ifit1, and viral mutants lacking 2′-O methylation are controlled in vivo by Ifit1-dependent and -independent mechanisms in different cell types

Fermi Large Area Telescope Constraints on the Gamma-ray Opacity of the Universe

The Extragalactic Background Light (EBL) includes photons with wavelengths from ultraviolet to infrared, which are effective at attenuating gamma rays with energy above ~10 GeV during propagation from sources at cosmological distances. This results in a redshift- and energy-dependent attenuation of the gamma-ray flux of extragalactic sources such as blazars and Gamma-Ray Bursts (GRBs). The Large Area Telescope onboard Fermi detects a sample of gamma-ray blazars with redshift up to z~3, and GRBs with redshift up to z~4.3. Using photons above 10 GeV collected by Fermi over more than one year of observations for these sources, we investigate the effect of gamma-ray flux attenuation by the EBL. We place upper limits on the gamma-ray opacity of the Universe at various energies and redshifts, and compare this with predictions from well-known EBL models. We find that an EBL intensity in the optical-ultraviolet wavelengths as great as predicted by the "baseline" model of Stecker et al. (2006) can be ruled out with high confidence.Comment: 42 pages, 12 figures, accepted version (24 Aug.2010) for publication in ApJ; Contact authors: A. Bouvier, A. Chen, S. Raino, S. Razzaque, A. Reimer, L.C. Reye

Global, regional, and national estimates of the population at increased risk of severe COVID-19 due to underlying health conditions in 2020: a modelling study

Background: The risk of severe COVID-19 if an individual becomes infected is known to be higher in older individuals and those with underlying health conditions. Understanding the number of individuals at increased risk of severe COVID-19 and how this varies between countries should inform the design of possible strategies to shield or vaccinate those at highest risk. Methods: We estimated the number of individuals at increased risk of severe disease (defined as those with at least one condition listed as “at increased risk of severe COVID-19” in current guidelines) by age (5-year age groups), sex, and country for 188 countries using prevalence data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 and UN population estimates for 2020. The list of underlying conditions relevant to COVID-19 was determined by mapping the conditions listed in GBD 2017 to those listed in guidelines published by WHO and public health agencies in the UK and the USA. We analysed data from two large multimorbidity studies to determine appropriate adjustment factors for clustering and multimorbidity. To help interpretation of the degree of risk among those at increased risk, we also estimated the number of individuals at high risk (defined as those that would require hospital admission if infected) using age-specific infection–hospitalisation ratios for COVID-19 estimated for mainland China and making adjustments to reflect country-specific differences in the prevalence of underlying conditions and frailty. We assumed males were twice at likely as females to be at high risk. We also calculated the number of individuals without an underlying condition that could be considered at increased risk because of their age, using minimum ages from 50 to 70 years. We generated uncertainty intervals (UIs) for our estimates by running low and high scenarios using the lower and upper 95% confidence limits for country population size, disease prevalences, multimorbidity fractions, and infection–hospitalisation ratios, and plausible low and high estimates for the degree of clustering, informed by multimorbidity studies. Findings: We estimated that 1·7 billion (UI 1·0–2·4) people, comprising 22% (UI 15–28) of the global population, have at least one underlying condition that puts them at increased risk of severe COVID-19 if infected (ranging from &lt;5% of those younger than 20 years to &gt;66% of those aged 70 years or older). We estimated that 349 million (186–787) people (4% [3–9] of the global population) are at high risk of severe COVID-19 and would require hospital admission if infected (ranging from &lt;1% of those younger than 20 years to approximately 20% of those aged 70 years or older). We estimated 6% (3–12) of males to be at high risk compared with 3% (2–7) of females. The share of the population at increased risk was highest in countries with older populations, African countries with high HIV/AIDS prevalence, and small island nations with high diabetes prevalence. Estimates of the number of individuals at increased risk were most sensitive to the prevalence of chronic kidney disease, diabetes, cardiovascular disease, and chronic respiratory disease. Interpretation: About one in five individuals worldwide could be at increased risk of severe COVID-19, should they become infected, due to underlying health conditions, but this risk varies considerably by age. Our estimates are uncertain, and focus on underlying conditions rather than other risk factors such as ethnicity, socioeconomic deprivation, and obesity, but provide a starting point for considering the number of individuals that might need to be shielded or vaccinated as the global pandemic unfolds. Funding: UK Department for International Development, Wellcome Trust, Health Data Research UK, Medical Research Council, and National Institute for Health Research

An expanded evaluation of protein function prediction methods shows an improvement in accuracy

Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. Results: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. Conclusions: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent. Keywords: Protein function prediction, Disease gene prioritizationpublishedVersio

An Expanded Evaluation of Protein Function Prediction Methods Shows an Improvement In Accuracy

Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. Results: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. Conclusions: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent