The technical mitigation potential of demand-side measures in the agri-food sector: a preliminary assessment of available measures

A number of studies have suggested that addressing greenhouse gas (GHG) emissions from agricultural production, or ‘supply-side emissions’, will be insufficient to reduce agri-food sector GHG emissions to limit the increase of global temperatures to well below 2o C. Recent studies have also suggested that ‘demandside measures’ related to food consumption, food value chains, and food loss and waste, will be necessary to reduce emissions and may have a larger technical mitigation potential than supply-side measures. This report assesses the availability of demand-side policies and measures, and looks at evidence of these measures’ impacts on behavior that directly results in emissions from the agri-food sector. Often discussed demand-side measures include ‘soft’ measures (e.g. health promotion initiatives, product labeling) and ‘hard’ measures (e.g. consumption taxes or subsidies). We review here the effectiveness of these measures for dietary change and reductions in food loss and waste, with a focus on developing countries, where agrifood emissions are projected to grow most rapidly and where the gaps in knowledge are largest. This report is linked to CCAFS Info Note "Shifting food consumption to mitigate climate change is critical to fulfilling the Paris Agreement, but how?" https://cgspace.cgiar.org/bitstream/handle/10568/77145/Info%20note%20Demand%20side%20mitigation%20Aug%2022%202016.pd

Shifting food consumption to mitigate climate change is critical to fulfilling the Paris Agreement, but how?

Reducing emissions by changing consumption of foods with large greenhouse gas emissions could have a major impact on climate change. Yet past efforts to change diets through public policy have had mixed results, suggesting that recent estimates of technical mitigation potential likely exceed feasible reductions in emissions. Shifting consumption away from livestock products is a major opportunity for reducing emissions driven by consumption demand. In some contexts, this could also provide health, food security and other environmental benefits. Packages of policy mechanisms and interventions involving health, nutrition, efficiency and sustainability in supply chains will be more effective in achieving dietary change than any one measure. Focusing on reducing food loss and waste in high potential areas and involving key value chain actors can increase returns on efforts to mitigate climate change and improve food security. Private sector investment in reducing food loss and waste requires an enabling environment, support for development of commercially viable investments, and increased awareness among financial institutions of investment opportunities. This Info Note is related to the report on "The technical mitigation potential of demand-side measures in the agri-food sector: a preliminary assessment of available measures" https://cgspace.cgiar.org/bitstream/handle/10568/77142/CCAFS%20Report%2015%20for%20web.pd

Substrate binding and catalysis by the pseudouridine synthases RluA and TruB

xi, 122 leaves : ill. (some col.) ; 29 cmPseudouridine is the most common RNA modification found in all forms of life. The exact role pseudouridines play in the cell is still relatively unknown. However, its extensive incorporation in functionally important areas of the ribosome and the fitness advantage provided to cells by pseudouridines implies that its presence is important for the cell. The enzymes responsible for this modification, pseudouridine synthases, vary greatly in substrate recognition mechanisms, but all enzymes supposedly share a universally conserved catalytic mechanism. Here, I analyze the kinetic mechanisms of pseudouridylation utilized by the exemplary pseudouridine synthase RluA in order to compare it with the previously determined rate of pseudouridylation of the pseudouridine synthase TruB. My results demonstrate that RluA has the same uniformly slow catalytic step as previously determined for TruB and TruA. This constitutes the first step towards identifying the catalytic mechanism of the pseudouridine synthase family. Additionally, it was my aim to identify the major determinants for RNA binding by pseudouridine synthases. By measuring the dissociation constants (KD) for substrate and product tRNA by nitrocellulose filtration assays, I showed that both tRNA species could bind with similar affinities. These binding studies also revealed that TruB’s interaction with the isolated T-arm is the major contact site contributing to the affinity of the enzyme to RNA. Finally, a new contact between tRNA and TruB’s PUA domain was identified which was not observed in the crystal structure. In summary, my results provide new insight into the common catalytic step of pseudouridine synthases and the specific interactions contributing to substrate binding by the enzyme TruB. These results will enable future studies on the kinetic mechanism of pseudouridine synthases, in particular the kinetics of substrate and product binding and release, as well as on the chemical mechanism of pseudouridine formation

Indexes: The Heart of Research

This article, originally a presentation at ACL’s 2023 conference, will delineate the enduring value of indexes for librarians and researchers alike, giving some examples of how indexes have evolved in a technology-driven age

The impact of online misogyny on women’s participation: democracy experts respond

Many women, including a number of high-profile British politicians, have been the targets of misogynistic abuse via social media. Democratic Audit recently featured an article by Laura Bates, arguing that this trend has negative effect on rates of female participation in public life. In this post we ask leading democracy and gender experts to respond, sharing their experiences and views on how misogyny undermines democracy

Functional and mechanistic characterization of two tRNA modifying enzymes

The formation of pseudouridine and 5-methyluridine (m5U) in the T-arm of transfer RNAs (tRNAs) is near-universally conserved. These two modifications are formed in Escherichia coli by the pseudouridine synthase TruB and the S-adenosylmethionine-dependent methyltransferase TrmA, respectively. In this thesis, I investigate the function and mechanisms of these two tRNA modifying enzymes. First, in vitro and in vivo analysis of TruB reveals that this enzyme is acting as a tRNA chaperone which proves a long outstanding hypothesis. Secondly, characterization of ligand binding by TrmA shows that binding is cooperative and disruption of tRNA elbow region tertiary interactions by TrmA is essential for efficient tRNA binding and catalysis, leading to future analysis. In conclusion, my studies further our understanding of the mechanism and function of tRNA modifications and modifying enzymes, as well as shed light on why all cells invest substantial resources into fine-tuning the chemical composition of tRNAs

The Chandra Multi-Wavelength Project: Optical Spectroscopy and the Broadband Spectral Energy Distributions of X-ray Selected AGN

From optical spectroscopy of X-ray sources observed as part of ChaMP, we present redshifts and classifications for a total of 1569 Chandra sources from our targeted spectroscopic follow up using the FLWO, SAAO, WIYN, CTIO, KPNO, Magellan, MMT and Gemini telescopes, and from archival SDSS spectroscopy. We classify the optical counterparts as 50% BLAGN, 16% NELG, 14% ALG, and 20% stars. We detect QSOs out to z~5.5 and galaxies out to z~3. We have compiled extensive photometry from X-ray to radio bands. Together with our spectroscopic information, this enables us to derive detailed SEDs for our extragalactic sources. We fit a variety of templates to determine bolometric luminosities, and to constrain AGN and starburst components where both are present. While ~58% of X-ray Seyferts require a starburst event to fit observed photometry only 26% of the X-ray QSO population appear to have some kind of star formation contribution. This is significantly lower than for the Seyferts, especially if we take into account torus contamination at z>1 where the majority of our X-ray QSOs lie. In addition, we observe a rapid drop of the percentage of starburst contribution as X-ray luminosity increases. This is consistent with the quenching of star formation by powerful QSOs, as predicted by the merger model, or with a time lag between the peak of star formation and QSO activity. We have tested the hypothesis that there should be a strong connection between X-ray obscuration and star-formation but we do not find any association between X-ray column density and star formation rate both in the general population or the star-forming X-ray Seyferts. Our large compilation also allows us to report here the identification of 81 XBONG, 78 z>3 X-ray sources and 8 Type-2 QSO candidates. Also we have identified the highest redshift (z=5.4135) X-ray selected QSO with optical spectroscopy.Comment: 17 pages, 16 figures, accepted for publication in ApJS. Full data table and README file can be found online at http://hea-www.harvard.edu/~pgreen/Papers.htm

Pre-steady-state kinetic analysis of the three Escherichia coli pseudouridine synthases TruB, TruA, and RluA reveals uniformly slow catalysis

Sherpa Romeo green journal. Open access article. Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0) appliesPseudouridine synthases catalyze formation of the most abundant modification of functional RNAs by site-specifically isomerizing uridines to pseudouridines. While the structure and substrate specificity of these enzymes have been studied in detail, the kinetic and the catalytic mechanism of pseudouridine synthases remain unknown. Here, the first pre-steady-state kinetic analysis of three Escherichia coli pseudouridine synthases is presented. A novel stopped-flow absorbance assay revealed that substrate tRNA binding by TruB takes place in two steps with an overall rate of 6 sec 1. In order to observe catalysis of pseudouridine formation directly, the traditional tritium release assay was adapted for the quench-flow technique, allowing, for the first time, observation of a single round of pseudouridine formation. Thereby, the single-round rate constant of pseudouridylation (kC) by TruB was determined to be 0.5 sec 1. This rate constant is similar to the kcat obtained under multiple-turnover conditions in steady-state experiments, indicating that catalysis is the rate-limiting step for TruB. In order to investigate if pseudouridine synthases are characterized by slow catalysis in general, the rapid kinetic quench-flow analysis was also performed with two other E. coli enzymes, RluA and TruA, which displayed rate constants of pseudouridine formation of 0.7 and 0.35 sec 1, respectively. Hence, uniformly slow catalysis might be a general feature of pseudouridine synthases that share a conserved catalytic domain and supposedly use the same catalytic mechanism.Ye

Modeling Chemical Interaction Profiles: II. Molecular Docking, Spectral Data-Activity Relationship, and Structure-Activity Relationship Models for Potent and Weak Inhibitors of Cytochrome P450 CYP3A4 Isozyme

Polypharmacy increasingly has become a topic of public health concern, particularly as the U.S. population ages. Drug labels often contain insufficient information to enable the clinician to safely use multiple drugs. Because many of the drugs are bio-transformed by cytochrome P450 (CYP) enzymes, inhibition of CYP activity has long been associated with potentially adverse health effects. In an attempt to reduce the uncertainty pertaining to CYP-mediated drug-drug/chemical interactions, an interagency collaborative group developed a consensus approach to prioritizing information concerning CYP inhibition. The consensus involved computational molecular docking, spectral data-activity relationship (SDAR), and structure-activity relationship (SAR) models that addressed the clinical potency of CYP inhibition. The models were built upon chemicals that were categorized as either potent or weak inhibitors of the CYP3A4 isozyme. The categorization was carried out using information from clinical trials because currently available in vitro high-throughput screening data were not fully representative of the in vivo potency of inhibition. During categorization it was found that compounds, which break the Lipinski rule of five by molecular weight, were about twice more likely to be inhibitors of CYP3A4 compared to those, which obey the rule. Similarly, among inhibitors that break the rule, potent inhibitors were 2–3 times more frequent. The molecular docking classification relied on logistic regression, by which the docking scores from different docking algorithms, CYP3A4 three-dimensional structures, and binding sites on them were combined in a unified probabilistic model. The SDAR models employed a multiple linear regression approach applied to binned 1D 13C-NMR and 1D 15N-NMR spectral descriptors. Structure-based and physical-chemical descriptors were used as the basis for developing SAR models by the decision forest method. Thirty-three potent inhibitors and 88 weak inhibitors of CYP3A4 were used to train the models. Using these models, a synthetic majority rules consensus classifier was implemented, while the confidence of estimation was assigned following the percent agreement strategy. The classifier was applied to a testing set of 120 inhibitors not included in the development of the models. Five compounds of the test set, including known strong inhibitors dalfopristin and tioconazole, were classified as probable potent inhibitors of CYP3A4. Other known strong inhibitors, such as lopinavir, oltipraz, quercetin, raloxifene, and troglitazone, were among 18 compounds classified as plausible potent inhibitors of CYP3A4. The consensus estimation of inhibition potency is expected to aid in the nomination of pharmaceuticals, dietary supplements, environmental pollutants, and occupational and other chemicals for in-depth evaluation of the CYP3A4 inhibitory activity. It may serve also as an estimate of chemical interactions via CYP3A4 metabolic pharmacokinetic pathways occurring through polypharmacy and nutritional and environmental exposures to chemical mixtures

Approaches to quality improvement in nursing homes: Lessons learned from the six-state pilot of CMS's Nursing Home Quality Initiative

BACKGROUND: In November 2002, the Centers for Medicare & Medicaid Services (CMS) launched a Nursing Home Quality Initiative that included publicly reporting a set of Quality Measures for all nursing homes in the country, and providing quality improvement assistance to nursing homes nationwide. A pilot of this initiative occurred in six states for six months prior to the launch. METHODS: Review and analysis of the lessons learned from the six Quality Improvement Organizations (QIOs) that led quality improvement efforts in nursing homes from the six pilot states. RESULTS: QIOs in the six pilot states found several key outcomes of the Nursing Home Quality Initiative that help to maximize the potential of public reporting to leverage effective improvement in nursing home quality of care. First, public reporting focuses the attention of all stakeholders in the nursing home industry on achieving good quality outcomes on a defined set of measures, and creates an incentive for partnership formation. Second, publicly reported quality measures motivate nursing home providers to improve in certain key clinical areas, and in particular to seek out new ways of changing processes of care, such as engaging physicians and the medical director more directly. Third, the lessons learned by QIOs in the pilot of this Initiative indicate that certain approaches to providing quality improvement assistance are key to guiding nursing home providers' desire and enthusiasm to improve towards a using a systematic approach to quality improvement. CONCLUSION: The Nursing Home Quality Initiative has already demonstrated the potential of public reporting to foster collaboration and coordination among nursing home stakeholders and to heighten interest of nursing homes in quality improvement techniques. The lessons learned from this pilot project have implications for any organizations or individuals planning quality improvement projects in the nursing home setting