Are We Missing the Boat? Recreational Fishing and the Benefits of Oyster Reef Restoration in the Chesapeake Bay

In many salt-water recreational settings an imprecise measure of site choice is often collected based upon the individual's launch-point. For anglers who continue on from the launch-point in a boat, this imprecise measure of site choice is likely missing important on-water trade-offs thereby affecting the accuracy of recreational benefit measures and the usefulness of such models for policy analysis. To investigate this special case, we proceed as follows. First, using revealed preference data collected on recreational angling in the Chesapeake Bay, we estimate a model that links aquatic habitat in the Chesapeake Bay to launch-points around the Bay. Like many other studies of recreational fishing, we couple a household production function to a random utility model. This household production model allows anglers to produce trip quality by combining time and fishing expertise with aquatic attributes at the site. Imprecise data on where anglers fish prevent us from formally modeling fishing sites as the unit of choice. Rather, like many other studies, we are forced to model choice based solely on the angler's launch-point. We use the model to assess the benefits from oyster reef restoration in the Bay. Second, we investigate whether benefit estimates are biased under the situation outlined above because of the measurement error. We conduct a monte carlo analysis over a variety of spatial configurations for on-water sites. The structure implemented in our monte carlo mimics that of the revealed preference study. We generate observations on fishing trips where the on-water choices and site attributes are known, estimate the household production model, the angler's preferences, and welfare measures associated with improvements in aquatic habitat. For these same trips, we construct a measurement error database by aggregate the data back to the launch-point and simulate a situation where on-water sites, travel costs, and site attributes are not known. Similar estimations are conducted using the Measurement Error data. Our findings show striking spatial patters where the aggregate model both under and overestimates the economic value associated with the improvement of aquatic habitat. Our results call into question the applicability of existing data for the evaluation of spatially explicit environmental policy

Assessing Stakeholder Preferences for Chesapeake Bay Restoration Options : a stated preference discrete choice-based assessment

Chesapeake 2000 or C2K is a multi-jurisdictional agreement between the states of Virginia, Maryland, Pennsylvania, the District of Columbia, the Chesapeake Bay Commission and the U.S. Environmental Protection Agency, representing the federal government, to restore the health of the Chesapeake Bay’s ecosystem. This agreement commits the participants to achieve five major restoration goals, 22 sub-objectives or categories, and 102 specific commitments or restoration activities. The five major goals are the following: (1) restore and protect natural living resources; (2) restore and protect vital habitat; (3) restore and protect water quality; (4) promote sound land use; and (5) promote stewardship and community engagement. The sub-categories and specific commitments impose specific restoration requirements relative to each of the five major categories. In 2003, the Chesapeake Bay Commission, utilizing a panel of experts, estimated the cost of achieving all five major objectives equaled approximately $18.7 billion, which equals approximately$21.0 billion in 2007 dollars. Unfortunately, all partners of C2K only committed $5.9 billion ($6.6 billion in 2007 dollars) in funding to achieving the five major objectives. There is, thus, a deficit of $12.8 billion or$14.4 billion in 2007 dollars. The funding available to achieve the goals of C2K is of considerable concern because the single sub-objective of the category of reducing nutrients and sediments requires more than $12.0 billion in 2007 dollars, and this is a major requirement for restoring the health of the Bay’s ecosystem. The cost of restoring the Bay complicates the choices and levels of restoration options. Given the large deficit for achieving the goals and objectives of C2K, it is necessary to assess how restoration might proceed. The available level of funding is simply inadequate for achieving all the goals and objectives necessary to restore the Bay’s ecosystem. In this study, we attempt to provide an assessment of how available funds might be distributed among the restoration goals and objectives in a manner, which generates the greatest social value. (more...

Choice Sets for Fishery Location Choice Models in the Presence of Fine-scale Spatial and Temporal Heterogeneity

A central component of any discrete choice analysis is the selection of alternatives that determine a decision agent's choice set. Failure to properly specify choice sets will generate biased parameter estimates, inaccurate behavioral predictions, and erroneous estimates of policy relevant metrics (e.g., welfare effects of closed areas in fisheries). The development of more behaviorally realistic choice sets is integral to predicting agent behavior and informing public policy. In some contexts such as fisheries, discrete spatial choices are made repeatedly, and the decision-maker invests in the collection of fine-scale spatial information through time. For such data rich environments, we propose constructing choice sets by sampling from a fine-scale grid of location choices or from all observed tow starting points and compare this approach to a traditional conditional logit model with choices sets constructed of discrete fishing areas that aggregate many possible specific fishing locations. We present results from a Monte Carlo study that compares these modeling approaches in terms of parameter bias and prediction. We also compare results from an empirical application of the models to the Pacific Groundfish trawl fishery. We find considerable heterogeneity in the parameter estimates and support for our fine-scale choice set model in terms of both superior choice prediction and smaller parameter bias

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The oral microbiome and breast cancer and nonmalignant breast disease, and its relationship with the fecal microbiome in the Ghana Breast Health Study

The oral microbiome, like the fecal microbiome, may be related to breast cancer risk. Therefore, we investigated whether the oral microbiome was associated with breast cancer and nonmalignant breast disease, and its relationship with the fecal microbiome in a case-control study in Ghana. A total of 881 women were included (369 breast cancers, 93 nonmalignant cases and 419 population-based controls). The V4 region of the 16S rRNA gene was sequenced from oral and fecal samples. Alpha-diversity (observed amplicon sequence variants [ASVs], Shannon index and Faiths Phylogenetic Diversity) and beta-diversity (Bray-Curtis, Jaccard and weighted and unweighted UniFrac) metrics were computed. MiRKAT and logistic regression models were used to investigate the case-control associations. Oral sample alpha-diversity was inversely associated with breast cancer and nonmalignant breast disease with odds ratios (95% CIs) per every 10 observed ASVs of 0.86 (0.83-0.89) and 0.79 (0.73-0.85), respectively, compared to controls. Beta-diversity was also associated with breast cancer and nonmalignant breast disease compared to controls (P ≤ .001). The relative abundances of Porphyromonas and Fusobacterium were lower for breast cancer cases compared to controls. Alpha-diversity and presence/relative abundance of specific genera from the oral and fecal microbiome were strongly correlated among breast cancer cases, but weakly correlated among controls. Particularly, the relative abundance of oral Porphyromonas was strongly, inversely correlated with fecal Bacteroides among breast cancer cases (r = -.37, P ≤ .001). Many oral microbial metrics were strongly associated with breast cancer and nonmalignant breast disease, and strongly correlated with fecal microbiome among breast cancer cases, but not controls

Associations of Circulating Estrogens and Estrogen Metabolites with Fecal and Oral Microbiome in Postmenopausal Women in the Ghana Breast Health Study

ABSTRACT The human fecal and oral microbiome may play a role in the etiology of breast cancer through modulation of endogenous estrogen metabolism. This study aimed to investigate associations of circulating estrogens and estrogen metabolites with the fecal and oral microbiome in postmenopausal African women. A total of 117 women with fecal (N = 110) and oral (N = 114) microbiome data measured by 16S rRNA gene sequencing, and estrogens and estrogen metabolites data measured by liquid chromatography tandem mass spectrometry were included. The outcomes were measures of the microbiome and the independent variables were the estrogens and estrogen metabolites. Estrogens and estrogen metabolites were associated with the fecal microbial Shannon index (global P < 0.01). In particular, higher levels of estrone (β = 0.36, P = 0.03), 2-hydroxyestradiol (β = 0.30, P = 0.02), 4-methoxyestrone (β = 0.51, P = 0.01), and estriol (β = 0.36, P = 0.04) were associated with higher levels of the Shannon index, while 16alpha-hydroxyestrone (β = −0.57, P < 0.01) was inversely associated with the Shannon index as indicated by linear regression. Conjugated 2-methoxyestrone was associated with oral microbial unweighted UniFrac as indicated by MiRKAT (P < 0.01) and PERMANOVA, where conjugated 2-methoxyestrone explained 2.67% of the oral microbial variability, but no other estrogens or estrogen metabolites were associated with any other beta diversity measures. The presence and abundance of multiple fecal and oral genera, such as fecal genera from families Lachnospiraceae and Ruminococcaceae, were associated with several estrogens and estrogen metabolites as indicated by zero-inflated negative binomial regression. Overall, we found several associations of specific estrogens and estrogen metabolites and the fecal and oral microbiome. IMPORTANCE Several epidemiologic studies have found associations of urinary estrogens and estrogen metabolites with the fecal microbiome. However, urinary estrogen concentrations are not strongly correlated with serum estrogens, a known risk factor for breast cancer. To better understand whether the human fecal and oral microbiome were associated with breast cancer risk via the regulation of estrogen metabolism, we conducted this study to investigate the associations of circulating estrogens and estrogen metabolites with the fecal and oral microbiome in postmenopausal African women. We found several associations of parent estrogens and several estrogen metabolites with the microbial communities, and multiple individual associations of estrogens and estrogen metabolites with the presence and abundance of multiple fecal and oral genera, such as fecal genera from families Lachnospiraceae and Ruminococcaceae, which have estrogen metabolizing properties. Future large, longitudinal studies to investigate the dynamic changes of the fecal and oral microbiome and estrogen relationship are needed

Pathways from research to sustainable development: insights from ten research projects in sustainability and resilience

Drawing on collective experience from ten collaborative research projects focused on the Global South, we identify three major challenges that impede the translation of research on sustainability and resilience into better-informed choices by individuals and policy-makers that in turn can support transformation to a sustainable future. The three challenges comprise: (i) converting knowledge produced during research projects into successful knowledge application; (ii) scaling up knowledge in time when research projects are short-term and potential impacts are long-term; and (iii) scaling up knowledge across space, from local research sites to larger-scale or even global impact. Some potential pathways for funding agencies to overcome these challenges include providing targeted prolonged funding for dissemination and outreach, and facilitating collaboration and coordination across different sites, research teams, and partner organizations. By systematically documenting these challenges, we hope to pave the way for further innovations in the research cycle

NALP3 inflammasome upregulation and CASP1 cleavage of the glucocorticoid receptor cause glucocorticoid resistance in leukemia cells

Glucocorticoids are universally used in the treatment of acute lymphoblastic leukemia (ALL), and resistance to glucocorticoids in leukemia cells confers poor prognosis. To elucidate mechanisms of glucocorticoid resistance, we determined the prednisolone sensitivity of primary leukemia cells from 444 patients newly diagnosed with ALL and found significantly higher expression of CASP1 (encoding caspase 1) and its activator NLRP3 in glucocorticoid-resistant leukemia cells, resulting from significantly lower somatic methylation of the CASP1 and NLRP3 promoters. Overexpression of CASP1 resulted in cleavage of the glucocorticoid receptor, diminished the glucocorticoid-induced transcriptional response and increased glucocorticoid resistance. Knockdown or inhibition of CASP1 significantly increased glucocorticoid receptor levels and mitigated glucocorticoid resistance in CASP1-overexpressing ALL. Our findings establish a new mechanism by which the NLRP3-CASP1 inflammasome modulates cellular levels of the glucocorticoid receptor and diminishes cell sensitivity to glucocorticoids. The broad impact on the glucocorticoid transcriptional response suggests that this mechanism could also modify glucocorticoid effects in other diseases

The genetic architecture of type 2 diabetes

The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of heritability. To test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole genome sequencing in 2,657 Europeans with and without diabetes, and exome sequencing in a total of 12,940 subjects from five ancestral groups. To increase statistical power, we expanded sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support a major role for lower-frequency variants in predisposition to type 2 diabetes