Sun Grant Initiative: The First Fifteen Years

Executive SummeryThe Vision: Building a Biobased Economy [Page] 4 The Sun Grant Initiative [Page] 5 Renewing the Mission of Land-Grant Universities [Page] 5 A Regional Approach [Page] 5 Reaching National Goals Through Regional Leadership [Page] 6 Integrating National and Regional Priorities [Page] 6 Comprehensive and Integrated Systems Approach [Page] 7 Cultivating Communication Federal Partnerships [Page] 8 U.S. Department of Agriculture [Page] 8 U.S. Department of Energy [Page] 9 U.S. Department of Transportation National Impacts [Page] 11 Overview [Page] 12 Sustainable Feedstock Development [Page] 13 Herbaceous Biomass Crops [Page] 14 Woody Biomass [Page] 15 Logistics [Page] 16 Conversion Technologies [Page] 19 Education and Outreach Regional Impacts [Page] 21 Southeast Region [Page] 22 South Central Region [Page] 23 North Central Region [Page] 25 Northeast Region [Page] 27 Western Region [Page] 30 The Path Forward Appendices [Page] 31 References [Page] 31 Regional Feedstock Partnership National Yield Potential Mapshttps://openprairie.sdstate.edu/ncrsgc_pubs/1000/thumbnail.jp

Sensitivity of L-band vegetation optical depth to carbon stocks in tropical forests: a comparison to higher frequencies and optical indices

Supplementary data to this article can be found online at https://doi.org/10.1016/j.rse.2019.111303.Monitoring vegetation carbon in tropical regions is essential to the global carbon assessment and to evaluate the actions oriented to the reduction of forest degradation. Mainly, satellite optical vegetation indices and LiDAR data have been used to this purpose. These two techniques are limited by cloud cover and are sensitive only to the top of vegetation. In addition, the vegetation attenuation to the soil microwave emission, represented by the vegetation optical depth (VOD), has been applied for biomass estimation using frequencies ranging from 4 to 30¿GHz (C- to K-bands). Atmosphere is transparent to microwaves and their sensitivity to canopy layers depends on the frequency, with lower frequencies having greater penetration depths. In this regard, L-band VOD (1.4¿GHz) is expected to enhance the ability to estimate carbon stocks. This study compares the sensitivity of different VOD products (from L, C, and X-bands) and an optical vegetation index (EVI) to the above-ground carbon density (ACD). It quantifies the contribution of ACD and forest cover proportion to the VOD/EVI signals. The study is conducted in Peru, southern Colombia and Panama, where ACD maps have been derived from airborne LiDAR. Results confirm the enhanced sensitivity of L-band VOD to ACD when compared to higher frequency bands, and show that the sensitivity of all VOD bands decreases in the densest forests. ACD explains 34% and forest cover 30% of L-band VOD variance, and these proportions gradually decrease for EVI, C-, and X-band VOD, respectively. Results are consistent through different categories of altitude and carbon density. This pattern is found in most of the studied regions and in flooded forests. Results also show that C-, X-band VOD and EVI provide complementary information to L-band VOD, especially in flooded forests and in mountains, indicating that synergistic approaches could lead to improved retrievals in these regions. Although the assessment of vegetation carbon in the densest forests requires further research, results from this study support the use of new L-band VOD estimates for mapping the carbon of tropical forests.Peer ReviewedPostprint (author's final draft

A saturated map of common genetic variants associated with human height.

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries

American Society for Enhanced Recovery and Perioperative Quality Initiative Joint Consensus Statement on Perioperative Opioid Minimization in Opioid-Naïve Patients

Surgical care episodes place opioid-naïve patients at risk for transitioning to new persistent postoperative opioid use. With one of the central principles being the application of multimodal pain interventions to reduce the reliance on opioid-based medications, enhanced recovery pathways provide a framework that decreases perioperative opioid use. The fourth Perioperative Quality Initiative brought together a group of international experts representing anesthesiology, surgery, and nursing with the objective of providing consensus recommendations on this important topic. Fourth Perioperative Quality Initiative was a consensus-building conference designed around a modified Delphi process in which the group alternately convened for plenary discussion sessions in between small group discussions. The process included several iterative steps including a literature review of the topics, building consensus around the important questions related to the topic, and sequential steps of content building and refinement until agreement was achieved and a consensus document was produced. During the fourth Perioperative Quality Initiative conference and thereafter as a writing group, reference applicability to the topic was discussed in any area where there was disagreement. For this manuscript, the questions answered included (1) What are the potential strategies for preventing persistent postoperative opioid use? (2) Is opioid-free anesthesia and analgesia feasible and appropriate for routine operations? and (3) Is opioid-free (intraoperative) anesthesia associated with equivalent or superior outcomes compared to an opioid minimization in the perioperative period? We will discuss the relevant literature for each questions, emphasize what we do not know, and prioritize the areas for future research.</p

Replication of genetic variants from genome-wide association studies with metabolic traits in an island population of the Adriatic coast of Croatia

Twenty-two single-nucleotide polymorphisms (SNPs) in 10 gene regions previously identified in obesity and type 2 diabetes (T2D) genome-wide association studies (GWAS) were evaluated for association with metabolic traits in a sample from an island population of European descent. We performed a population-based study using 18 anthropometric and biochemical traits considered as continuous variables in a sample of 843 unrelated subjects (360 men and 483 women) aged 18–80 years old from the island of Hvar on the eastern Adriatic coast of Croatia. All eight GWAS SNPs in FTO were significantly associated with weight, body mass index, waist circumference and hip circumference; 20 of the 32 nominal P-values remained significant after permutation testing for multiple corrections. The strongest associations were found between the two TCF7L2 GWAS SNPs with fasting plasma glucose and HbA1c levels, all four P-values remained significant after permutation tests. Nominally significant associations were found between several SNPs and other metabolic traits; however, the significance did not hold after permutation tests. Although the sample size was modest, our study strongly replicated the association of FTO variants with obesity-related measures and TCF7L2 variants with T2D-related traits. The estimated effect sizes of these variants were larger or comparable to published studies. This is likely attributable to the homogenous genetic background of the relatively isolated study population

Actionable druggable genome-wide Mendelian randomization identifies repurposing opportunities for COVID-19.

Drug repurposing provides a rapid approach to meet the urgent need for therapeutics to address COVID-19. To identify therapeutic targets relevant to COVID-19, we conducted Mendelian randomization analyses, deriving genetic instruments based on transcriptomic and proteomic data for 1,263 actionable proteins that are targeted by approved drugs or in clinical phase of drug development. Using summary statistics from the Host Genetics Initiative and the Million Veteran Program, we studied 7,554 patients hospitalized with COVID-19 and >1 million controls. We found significant Mendelian randomization results for three proteins (ACE2, P = 1.6 × 10-6; IFNAR2, P = 9.8 × 10-11 and IL-10RB, P = 2.3 × 10-14) using cis-expression quantitative trait loci genetic instruments that also had strong evidence for colocalization with COVID-19 hospitalization. To disentangle the shared expression quantitative trait loci signal for IL10RB and IFNAR2, we conducted phenome-wide association scans and pathway enrichment analysis, which suggested that IFNAR2 is more likely to play a role in COVID-19 hospitalization. Our findings prioritize trials of drugs targeting IFNAR2 and ACE2 for early management of COVID-19.We are grateful to the Host Genetic Initiative for making their data publicly available (full acknowledgements can be found here: https://www.covid19hg.org/acknowledgements/). This research is based on data from the Million Veteran Program, Office of Research and Development, Veterans Health Administration, and was supported by award #MVP035. This research was also supported by additional Department of Veterans Affairs awards grant #MVP001. This publication does not represent the views of the Department of Veteran Affairs or the United States Government. Full acknowledgements for the VA Million Veteran Program COVID-19 Science Initiative can be found in the supplementary methods. C.G. has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 754490 – MINDED project. A.G., P.B. and A.R.L. are funded by the Member States of the European Molecular Biology Laboratory (EMBL). I.B.- H. received funding from Open Targets (grant agreement OTAR-044). The Fenland Study (10.22025/2017.10.101.00001) is funded by the Medical Research Council (MC_UU_12015/1); we are grateful to all the volunteers and to the General Practitioners and practice staff for assistance with recruitment; we thank the Fenland Study Investigators, Fenland Study Co-ordination team and the Epidemiology Field, Data and Laboratory teams; we further acknowledge support for genomics from the Medical Research Council (MC_PC_13046); proteomic measurements were supported and governed by a collaboration agreement between the University of Cambridge and Somalogic. J.E.P. is supported by UKRI Innovation Fellowship at Health Data Research UK (MR/S004068/2). L.R., N.H. and C.L. are supported by the Swedish Research Council. E.A. was supported by the EU/EFPIA Innovative Medicines Initiative Joint Undertaking BigData@Heart grant n° 116074 and by the British Heart Foundation Programme Grant RG/18/13/33946. We thank Dr. Angela Wood for feedback on statistical analyses used in the paper. We thank the INTERVAL Study investigators, co-ordination team and the epidemiology field, data and laboratory teams, which were supported by core funding from the UK Medical Research Council (MR/L003120/1), the British Heart Foundation (RG/13/13/30194; RG/18/13/33946), the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014) [The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care], and the NIHR Blood and Transplant Research Unit in Donor Health and Genomics (NIHR BTRU-2014-10024). This work was also supported by Health Data Research UK, which is funded by the UK Medical Research Council, the Engineering and Physical Sciences Research Council, the Economic and Social Research Council, the Department of Health and Social Care (England), the Chief Scientist Office of the Scottish Government Health and Social Care Directorates, the Health and Social Care Research and Development Division (Welsh Government), the Public Health Agency (Northern Ireland), the British Heart Foundation, and Wellcome. J.D. holds a British Heart Foundation Professorship and a National Institute for Health Research Senior Investigator Awar