Cooperative Extension Service Online Registration Service

Prior to the development of this project, the South Dakota Cooperative Extension Service did not have an online registration system. Workshop participants registered manually; by telephone, by mail or at the door. This was an inefficient use of employee time and other resources. It was also inconvenient for participants. To answer this need, an online registration system was developed. ColdFusion was used as a database-to-web gateway. This database driven system allows the workshop manager to setup and manage the registration pages. The manager and the administrator pages are located on the Extension Services private intranet site. These include workshop management pages and participant management pages. The public pages are located the internet site and include the workshop search page and the individual registration pages

Intrinsic Variability of GM Density Maps and its Implications to VBM Studies

Voxel Based Morphometry (VBM) has been gaining popularity as an unbiased objective neuroimaging technique for identifying structural changes in the brain. VBM involves a voxel-wise comparison of the local concentration of gray matter (GM) in whole brain MRI scans. Although it was originally devised to examine structural abnormalities in patients, the technique has also been used successfully with healthy subjects. Recent VBM studies have investigated the impact of learning and practice on brain structure. Unlike certain medical conditions that may cause dramatic structural changes, effects observed in healthy subjects are expected to be small, therefore imposing stringent requirements on the sensitivity of the technique. The success of such studies depends on high quality imaging and subsequent accurate segmentation of GM. Segmentation results are inevitably affected by the presence of other tissues with similar intensity (dura matter, large blood vessels etc.), imaging artifacts (blood flow and eye movement, susceptibility artifacts etc.). Since these factors are non-homogeneous throughout the brain, segmentation is highly reproducible in some areas of cortex while it is less reliable in other areas. This non-homogeneity makes VBM sensitivity selective to areas where segmentation happens to be more robust. We studied the intrinsic variability of GM density maps derived from scans obtained under identical conditions, i.e. the same subject, scanner and protocol. The data was acquired on GE Signa 1.5, (SPGR) and Philips Achieva 3T (MPRAGE) scanners. A distinction should be made between variability observed among scans acquired within the same session and that observed for different sessions, since the latter will also be affected by such factors as different head positioning and the somewhat altered state of both the subject and the scanner. The figure summarizes within-session variability of GM density maps observed using the GE Signa. Six SPGR scans were obtained in each of four subjects in one session, and the scan sessions were repeated nine weeks later as a part of longitudinal VBM study. Variability for one subject/session was estimated by computing the standard deviation of six GM density maps obtained using SPM5 unified segmentation/normalization framework and VBM5 toolkit. These were normalized by applying a transformation estimated as follows: all six scans were coregistered and averaged to obtain a low noise structural image volume and a single normalization transformation was estimated from it. Eight variability maps in standard (MNI) space corresponding to session/subject pairs were averaged to produce a map shown in the Figure. The color coded variability map is superimposed onto the GM probability density map (only the right hemisphere is shown in the figure). We will present the findings of within and between session variability analyses derived from our data and from data obtained in other laboratories, and discuss implications and methodological considerations for planning and interpreting VBM studies of GM density. Preliminary results indicate that although different scanners and protocols produce varying patterns of GM variability maps, certain areas (e.g. tip of the temporal lobe) may consistently show increased variability

Patterns of Local-Regional Management Following Neoadjuvant Chemotherapy in Breast Cancer: Results From ACOSOG Z1071 (Alliance)

AXXXXX ZXXXX was a prospective trial evaluating the false negative rate of sentinel node (SLN) surgery after neoadjuvant chemotherapy (NAC) in breast cancer patients with initial node-positive disease. Radiation therapy (RT) decisions were at the discretion of treating physicians, providing an opportunity to evaluate variability in practice patterns following NAC

Effects of silver sulfide nanomaterials on mycorrhizal colonization of tomato plants and soil microbial communities in biosolid-amended soil

We investigated effects of Ag2S engineered nanomaterials (ENMs), polyvinylpyrrolidone (PVP) coated Ag ENMs (PVP-Ag), and Ag+ on arbuscular mycorrhizal fungi (AMF), their colonization of tomato (Solanum lycopersicum), and overall microbial community structure in biosolids-amended soil. Concentration-dependent uptake was measured in all treatments. Plants exposed to 100 mg kg−1 PVP-Ag ENMs and 100 mg kg−1 Ag+ exhibited reduced biomass and greatly reduced mycorrhizal colonization. Bacteria, actinomycetes and fungi were inhibited by all treatment classes, with the largest reductions measured in 100 mg kg−1 PVP-Ag ENMs and 100 mg kg−1 Ag+. Overall, Ag2S ENMs were less toxic to plants, less disruptive to plant-mycorrhizal symbiosis, and less inhibitory to the soil microbial community than PVP-Ag ENMs or Ag+. However, significant effects were observed at 1 mg kg−1 Ag2S ENMs, suggesting that the potential exists for microbial communities and the ecosystem services they provide to be disrupted by environmentally relevant concentrations of Ag2S ENMs.Jonathan D. Judy, Jason K. Kirby, Courtney Creamer, Mike J. McLaughlin, Cathy Fiebiger, Claire Wright, Timothy R. Cavagnaro, Paul M. Bertsc

Blueberry Advisory Committee Research Report

The 1984 edition of the Blueberry Progress Reports was prepared for the Maine Blueberry Commission and the University of Maine Blueberry Advisory Committee by researchers with the Maine Agricultural Experiment Station and Maine Cooperative Extension Service at the University of Maine, Orono. Projects in this report include: 1. Control, biology, and ecology of insects affecting lowbush blueberries . 2. Chemical control of mummyberry disease (Monilinia vaccinii-corymbosi) 3. New Fungicides for control of Botrytis blossom blight 4. Nutritional survey of selected lowbush blueberry fields 5. Interaction of fertility and pruning practices on soil characteristics and lowbush blueberry growth and yield 6. Long term effects of N and NPK fertilizer on plant growth and yield 7. The effect of N fertilization on clonal spread 8. Nutritional responses of the lowbush blueberry in new plantings as related to early establishment 9. The effect of several mulches on frost heaving, soil moisture, soil temperature and rhizome development 10. Effectiveness of mulches and planted lowbush blueberry seedlings for stabilizing soils and increasing plant cover 11. Effect of surface mulches on stabilizing lowbush blueberry soil in barren areas 12. Frequency of fertility application for establishment of lowbush blueberry seedlings 13. Slow release vs liquid fertilizer for establishment of lowbush blueberry seedlings 14. Comparison of rooted cuttings and tissue culture propagated lowbush blueberry plants 15. The effect of growth regulator formulations on growth and rhizome production of the lowbush blueberry 16. Unburned, mowed fields 17. Blueberry concentrate 18. Blueberry product development 19. Dehydrated blueberries 20. Low-calorie blueberry jellies 21. Hexazinone and terbacil mixture for weed control 22. Hexazinone and atrazine mixture for weed control 23. Effect of hexazinone and nitrogen or nitrogen-phosphorus fertilizer on lowbush blueberry plants 24. Hand-wiper applications of herbicides on birch, maple and willow 25. Glyphosate applied after leaf drop for bunchberry control 26. Napropamide for seedling weed control 27. PP333 plant growth regulator 28. Dichlobenil for bunchberry control 29. Effect of hexazinone on weed and blueberry populations 30. Fluazifop-butyl for grass control 31. Hand-wiping and cutting treatments for dogbane 32. Evaluation of airblast sprayer application of asulam for bracken fern control 33. Evaluation of spot treatment of woody weeds with 2,4-D in oil 34. Steam heat as a control of mummyberry diseas

Blueberry Progress Reports

The 1983 edition of the Blueberry Progress Reports was prepared for the Maine Blueberry Commission and the University of Maine Blueberry Advisory Committee by researchers with the Maine Agricultural Experiment Station and Maine Cooperative Extension Service at the University of Maine, Orono. Projects in this report include: 1. Introduction 2. Forest Tent Caterpillar in Blueberries 3. Control, Biology, and Ecology of Insects Affecting Lowbush Blueberries 4. Blueberry Diseases: Incidence and Control 5. Physiology and Culture of the Lowbush Blueberry 6. Weed Control in Lowbush Blueberry Fields 7. Product Development of Lowbush Blueberrie

2001 Wild Blueberry CSREES Project Reports

The 2001 edition of the Wild Blueberry CSREES Progress Reports was prepared for the Maine Wild Blueberry Commission and the University of Maine Wild Blueberry Advisory Committee by researchers at the University of Maine, Orono. Projects in this report include: 1. Effect of Wild Blueberry Products on Oxidation in Meat Based Food Systems 2. Factors Affecting the Microbial and Pesticide Residues Levels on Wild Blueberries 3. Determination of Pesticide Residue Levels in Fresh and Processed Wild Blueberries 4. Separation of Maggot-Infested Wild Blueberries in the IQF Processing Line 5. Water Use of Wild Blueberries and the Impact of Plant Water Stress on Yields 6. Survey of Stem Blight and Leaf Spot Diseases in Wild Blueberry Fields 7. IPM Strategies 8. Control Tactics for Wild Blueberry Pest Insects, 2001 9. Biology and Ecology of Blueberry Pest Insects 10. Diurnal Bee Activity and Measurement of Honeybee Field Strength 11. Effect of Foliar-applied Iron (Fe) Chelate Concentration on Leaf Iron Concentration, Wild Blueberry Growth and Yield 12. Effect of Boron Application Methods on Boron Uptake in Wild Blueberries 13. Effect of Foliar Iron and Copper Application on Growth and Yield of Wild Blueberries 14. Effect of Fertilizer Timing on Wild Blueberry Growth and Productivity 15. Effect of Foliar Copper Application on Growth and Yield of Wild Blueberries 16. Effect of Prune-year Applications of Nutri-Phitetm P or Nutri-Phitetm P+K on Growth and Yield of Wild Blueberry (Vaccinium angustifolium Ait.) 17. Effect of Soil pH on Nutrient Uptake 18. Assessment of Azafenidin for Weed Control in Wild Blueberries 19. Assessment of Rimsulfuron for Weed Control in Wild Blueberries 20. Assessment of Pendimethalin for Weed Control in Wild Blueberries 21. Evaluation and Demonstration of Techniques for Filling in Bare Spots in Wild Blueberry Fields 22. Assessment of Sprout-less Weeder for Hardwood Control in Wild Blueberries 23. Wild Blueberry Extension Education Program in 2001 24. Evaluation of Fungicide Efficacy in Wild Blueberry Fields 25. 2001 Pesticide Groundwater Survey 26. Cultural Weed Management Using Sulfur to Lower the pH 27. Wild Blueberry Web Sit

Identification of six new susceptibility loci for invasive epithelial ovarian cancer.

Genome-wide association studies (GWAS) have identified 12 epithelial ovarian cancer (EOC) susceptibility alleles. The pattern of association at these loci is consistent in BRCA1 and BRCA2 mutation carriers who are at high risk of EOC. After imputation to 1000 Genomes Project data, we assessed associations of 11 million genetic variants with EOC risk from 15,437 cases unselected for family history and 30,845 controls and from 15,252 BRCA1 mutation carriers and 8,211 BRCA2 mutation carriers (3,096 with ovarian cancer), and we combined the results in a meta-analysis. This new study design yielded increased statistical power, leading to the discovery of six new EOC susceptibility loci. Variants at 1p36 (nearest gene, WNT4), 4q26 (SYNPO2), 9q34.2 (ABO) and 17q11.2 (ATAD5) were associated with EOC risk, and at 1p34.3 (RSPO1) and 6p22.1 (GPX6) variants were specifically associated with the serous EOC subtype, all with P < 5 × 10(-8). Incorporating these variants into risk assessment tools will improve clinical risk predictions for BRCA1 and BRCA2 mutation carriers.COGS project is funded through a European Commission's Seventh Framework Programme grant (agreement number 223175 ] HEALTH ]F2 ]2009 ]223175). The CIMBA data management and data analysis were supported by Cancer Research.UK grants 12292/A11174 and C1287/A10118. The Ovarian Cancer Association Consortium is supported by a grant from the Ovarian Cancer Research Fund thanks to donations by the family and friends of Kathryn Sladek Smith (PPD/RPCI.07). The scientific development and funding for this project were in part supported by the US National Cancer Institute GAME ]ON Post ]GWAS Initiative (U19 ]CA148112). This study made use of data generated by the Wellcome Trust Case Control consortium. Funding for the project was provided by the Wellcome Trust under award 076113. The results published here are in part based upon data generated by The Cancer Genome Atlas Pilot Project established by the National Cancer Institute and National Human Genome Research Institute (dbGap accession number phs000178.v8.p7). The cBio portal is developed and maintained by the Computational Biology Center at Memorial Sloan ] Kettering Cancer Center. SH is supported by an NHMRC Program Grant to GCT. Details of the funding of individual investigators and studies are provided in the Supplementary Note. This study made use of data generated by the Wellcome Trust Case Control consortium, funding for which was provided by the Wellcome Trust under award 076113. The results published here are, in part, based upon data generated by The Cancer Genome Atlas Pilot Project established by the National Cancerhttp://dx.doi.org/10.1038/ng.3185This is the Author Accepted Manuscript of 'Identification of six new susceptibility loci for invasive epithelial ovarian cancer' which was published in Nature Genetics 47, 164–171 (2015) © Nature Publishing Group - content may only be used for academic research

Identification of 12 new susceptibility loci for different histotypes of epithelial ovarian cancer.

To identify common alleles associated with different histotypes of epithelial ovarian cancer (EOC), we pooled data from multiple genome-wide genotyping projects totaling 25,509 EOC cases and 40,941 controls. We identified nine new susceptibility loci for different EOC histotypes: six for serous EOC histotypes (3q28, 4q32.3, 8q21.11, 10q24.33, 18q11.2 and 22q12.1), two for mucinous EOC (3q22.3 and 9q31.1) and one for endometrioid EOC (5q12.3). We then performed meta-analysis on the results for high-grade serous ovarian cancer with the results from analysis of 31,448 BRCA1 and BRCA2 mutation carriers, including 3,887 mutation carriers with EOC. This identified three additional susceptibility loci at 2q13, 8q24.1 and 12q24.31. Integrated analyses of genes and regulatory biofeatures at each locus predicted candidate susceptibility genes, including OBFC1, a new candidate susceptibility gene for low-grade and borderline serous EOC

Polygenic Risk Modelling for Prediction of Epithelial Ovarian Cancer Risk

Funder: Funding details are provided in the Supplementary MaterialAbstractPolygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally-efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, “select and shrink for summary statistics” (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestry; 7,669 women of East Asian ancestry; 1,072 women of African ancestry, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestry. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38(95%CI:1.28–1.48,AUC:0.588) per unit standard deviation, in women of European ancestry; 1.14(95%CI:1.08–1.19,AUC:0.538) in women of East Asian ancestry; 1.38(95%CI:1.21-1.58,AUC:0.593) in women of African ancestry; hazard ratios of 1.37(95%CI:1.30–1.44,AUC:0.592) in BRCA1 pathogenic variant carriers and 1.51(95%CI:1.36-1.67,AUC:0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs.</jats:p