COMPETITIVENESS AND COMPARATIVE ADVANTAGE OF U.S. AGRICULTURE

Agribusiness,

Exploring Linkages Among Agriculture, Trade, and the Environment: Issues for the Next Century

Many trade and environment issues will confront agriculture over the next several years. This report provides an economic framework to better understand these issues and discusses prior empirical inquiries and findings. Four primary issues are addressed: (1) how will environmental policies affect agricultural trade?; (2) how will agricultural trade liberalization affect environmental quality?; (3) to what extent should there be international harmonization of environmental policies and product standards?; and (4) is there economic justification for using trade measures to protect the environment? This report demonstrates that basic economic paradigms can provide a basis for understanding how trade and the environment interact. The few empirical studies based on these concepts have found many of the linkages between trade and the environment to be weak or the effects small. Trade and environment issues remain important to monitor, however, because economic and environmental relationships and domestic and international policies are continually evolving, and decision-makers need good information to confirm or disprove the numerous hypotheses that have surfaced in international discussions.environmental policy, agricultural policy, trade policy, trade, environment, harmonization, Agricultural and Food Policy, Environmental Economics and Policy, International Relations/Trade,

3D vessel-wall virtual histology of whole-body perfused mice using a novel heavy element stain

© 2019, The Author(s). Virtual histology – utilizing high-resolution three-dimensional imaging – is becoming readily available. Micro-computed tomography (micro-CT) is widely available and is often coupled with x-ray attenuating histological stains that mark specific tissue components for 3D virtual histology. In this study we describe a new tri-element x-ray attenuating stain and perfusion protocol that provides micro-CT contrast of the entire vasculature of an intact mouse. The stain – derived from an established histology stain (Verhoeff’s) – is modified to enable perfusion through the vasculature; the attenuating elements of the stain are iodine, aluminum, and iron. After a 30-minute perfusion through the vasculature (10-minute flushing with detergent-containing saline followed by 15-minute perfusion with the stain and a final 5-minute saline flush), animals are scanned using micro-CT. We demonstrate that the new staining protocol enables sharp delineation of the vessel walls in three dimensions over the whole body; corresponding histological analysis verified that the CT stain is localized primarily in the endothelial cells and media of large arteries and the endothelium of smaller vessels, such as the coronaries. The rapid perfusion and scanning protocol ensured that all tissues are available for further analysis via higher resolution CT of smaller sections or traditional histological sectioning

Transcript analysis reveals a specific HOX signature associated with positional identity of human endothelial cells.

The endothelial cell has a remarkable ability for sub-specialisation, adapted to the needs of a variety of vascular beds. The role of developmental programming versus the tissue contextual environment for this specialization is not well understood. Here we describe a hierarchy of expression of HOX genes associated with endothelial cell origin and location. In initial microarray studies, differential gene expression was examined in two endothelial cell lines: blood derived outgrowth endothelial cells (BOECs) and pulmonary artery endothelial cells. This suggested shared and differential patterns of HOX gene expression between the two endothelial lines. For example, this included a cluster on chromosome 2 of HOXD1, HOXD3, HOXD4, HOXD8 and HOXD9 that was expressed at a higher level in BOECs. Quantative PCR confirmed the higher expression of these HOXs in BOECs, a pattern that was shared by a variety of microvascular endothelial cell lines. Subsequently, we analysed publically available microarrays from a variety of adult cell and tissue types using the whole "HOX transcriptome" of all 39 HOX genes. Using hierarchical clustering analysis the HOX transcriptome was able to discriminate endothelial cells from 61 diverse human cell lines of various origins. In a separate publically available microarray dataset of 53 human endothelial cell lines, the HOX transcriptome additionally organized endothelial cells related to their organ or tissue of origin. Human tissue staining for HOXD8 and HOXD9 confirmed endothelial expression and also supported increased microvascular expression of these HOXs. Together these observations suggest a significant involvement of HOX genes in endothelial cell positional identity

Combined point of care nucleic acid and antibody testing for SARS-CoV-2 following emergence of D614G Spike Variant

Rapid COVID-19 diagnosis in hospital is essential, though complicated by 30-50% of nose/throat swabs being negative by SARS-CoV-2 nucleic acid amplification testing (NAAT). Furthermore, the D614G spike mutant now dominates the pandemic and it is unclear how serological tests designed to detect anti-Spike antibodies perform against this variant. We assess the diagnostic accuracy of combined rapid antibody point of care (POC) and nucleic acid assays for suspected COVID-19 disease due to either wild type or the D614G spike mutant SARS-CoV-2. The overall detection rate for COVID-19 is 79.2% (95CI 57.8-92.9%) by rapid NAAT alone. Combined point of care antibody test and rapid NAAT is not impacted by D614G and results in very high sensitivity for COVID-19 diagnosis with very high specificity

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management. © 2021, The Author(s)