This research paper addresses the hypothesis that substituting soybean meal with locally produced yeast protein from Cyberlindnera jadinii in barley-based concentrates for Norwegian Red (NR) dairy cows does not have adverse effects on milk fatty acid (FA) composition, rumen microbiota and sensory quality of milk. As soybeans also represent valuable protein sources for human consumption, alternative protein sources need to be investigated for animal feed. A total of 48 NR dairy cows were allocated into three feeding treatments, with the same basal diet of grass silage, but different concentrates. The concentrates were all based on barley, but 7% of the barley in the barley-concentrate (BAR; negative control) was replaced by either soybean meal (SBM; conventional control) or yeast microbial protein (YEA). The experiment lasted for a total of 10 weeks, including 2 weeks of adaptation with the soybean meal concentrate. Analysis of the feed revealed some differences in the FA composition of the YEA concentrate compared to the SBM and BAR concentrates. In milk, only two FAs (C17:1n-8cis9 and an unidentified isomer of C18:3) were significantly different between the YEA- and SBM-group, while six FAs differed between the BAR- and SBM-group. However, the amount of these FAs was low compared to the entire FA profile (<0.7 g/100 g). The experimental diets did not affect rumen microbiota nor the milk sensory quality. This study shows that C. jadinii can replace soybean meal as a protein source in concentrates (7% inclusion) for NR dairy cows fed a diet composed of grass silage and concentrates without any effects on rumen microbiota, and without compromising the FA composition or sensory quality of milk.

Female fertility is a complex trait with age-specific changes in spontaneous dizygotic (DZ) twinning and fertility. To elucidate factors regulating female fertility and infertility, we conducted a genome-wide association study (GWAS) on mothers of spontaneous DZ twins (MoDZT) versus controls (3273 cases, 24,009 controls). This is a follow-up study to the Australia/New Zealand (ANZ) component of that previously reported (Mbarek et al., 2016), with a sample size almost twice that of the entire discovery sample meta-analysed in the previous article (and five times the ANZ contribution to that), resulting from newly available additional genotyping and representing a significant increase in power. We compare analyses with and without male controls and show unequivocally that it is better to include male controls who have been screened for recent family history, than to use only female controls. Results from the SNP based GWAS identified four genomewide significant signals, including one novel region, ZFPM1 (Zinc Finger Protein, FOG Family Member 1), on chromosome 16. Previous signals near FSHB (Follicle Stimulating Hormone beta subunit) and SMAD3 (SMAD Family Member 3) were also replicated (Mbarek et al., 2016). We also ran the GWAS with a dominance model that identified a further locus ADRB2 on chr 5. These results have been contributed to the International Twinning Genetics Consortium for inclusion in the next GWAS meta-analysis (Mbarek et al., in press).

The concentration of radiocarbon (14C) differs between ocean and atmosphere. Radiocarbon determinations from samples which obtained their 14C in the marine environment therefore need a marine-specific calibration curve and cannot be calibrated directly against the atmospheric-based IntCal20 curve. This paper presents Marine20, an update to the internationally agreed marine radiocarbon age calibration curve that provides a non-polar global-average marine record of radiocarbon from 0–55 cal kBP and serves as a baseline for regional oceanic variation. Marine20 is intended for calibration of marine radiocarbon samples from non-polar regions; it is not suitable for calibration in polar regions where variability in sea ice extent, ocean upwelling and air-sea gas exchange may have caused larger changes to concentrations of marine radiocarbon. The Marine20 curve is based upon 500 simulations with an ocean/atmosphere/biosphere box-model of the global carbon cycle that has been forced by posterior realizations of our Northern Hemispheric atmospheric IntCal20 14C curve and reconstructed changes in CO2 obtained from ice core data. These forcings enable us to incorporate carbon cycle dynamics and temporal changes in the atmospheric 14C level. The box-model simulations of the global-average marine radiocarbon reservoir age are similar to those of a more complex three-dimensional ocean general circulation model. However, simplicity and speed of the box model allow us to use a Monte Carlo approach to rigorously propagate the uncertainty in both the historic concentration of atmospheric 14C and other key parameters of the carbon cycle through to our final Marine20 calibration curve. This robust propagation of uncertainty is fundamental to providing reliable precision for the radiocarbon age calibration of marine based samples. We make a first step towards deconvolving the contributions of different processes to the total uncertainty; discuss the main differences of Marine20 from the previous age calibration curve Marine13; and identify the limitations of our approach together with key areas for further work. The updated values for ΔR, the regional marine radiocarbon reservoir age corrections required to calibrate against Marine20, can be found at the data base http://calib.org/marine/.

Radiocarbon (14C) ages cannot provide absolutely dated chronologies for archaeological or paleoenvironmental studies directly but must be converted to calendar age equivalents using a calibration curve compensating for fluctuations in atmospheric 14C concentration. Although calibration curves are constructed from independently dated archives, they invariably require revision as new data become available and our understanding of the Earth system improves. In this volume the international 14C calibration curves for both the Northern and Southern Hemispheres, as well as for the ocean surface layer, have been updated to include a wealth of new data and extended to 55,000 cal BP. Based on tree rings, IntCal20 now extends as a fully atmospheric record to ca. 13,900 cal BP. For the older part of the timescale, IntCal20 comprises statistically integrated evidence from floating tree-ring chronologies, lacustrine and marine sediments, speleothems, and corals. We utilized improved evaluation of the timescales and location variable 14C offsets from the atmosphere (reservoir age, dead carbon fraction) for each dataset. New statistical methods have refined the structure of the calibration curves while maintaining a robust treatment of uncertainties in the 14C ages, the calendar ages and other corrections. The inclusion of modeled marine reservoir ages derived from a three-dimensional ocean circulation model has allowed us to apply more appropriate reservoir corrections to the marine 14C data rather than the previous use of constant regional offsets from the atmosphere. Here we provide an overview of the new and revised datasets and the associated methods used for the construction of the IntCal20 curve and explore potential regional offsets for tree-ring data. We discuss the main differences with respect to the previous calibration curve, IntCal13, and some of the implications for archaeology and geosciences ranging from the recent past to the time of the extinction of the Neanderthals.

OBJECTIVES/SPECIFIC AIMS: We evaluated the accuracy of diagnosis and procedure codes to identify acute urinary retention (AUR) due to lack of existing validation studies. METHODS/STUDY POPULATION: We performed a cross-sectional validation study at a single medical institution in the emergency department (ED) and outpatient Urology Clinic in men ≥45 years. International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis codes 788.20, 788.21, 788.29 for urinary retention and Current Procedural Terminology, Fourth Edition (CPT-4) codes 51701, 51702, 51703 for urinary catheterization were used to identify men with potential AUR. Four algorithms using ICD-9-CM and CPT-4 codes were compared against medical record review. Sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve were calculated for both the ED and Urology Clinic. RESULTS/ANTICIPATED RESULTS: A total of 333 treated and released men in the ED were identified using facility billing data, and 245 men using physician billing data in the Urology Clinic were identified using the codes for urinary retention or urinary catheterization. Of the 4 algorithms, any ICD-9-CM diagnosis code for urinary retention was the preferred algorithm with a sensitivity and specificity of 0.95 and 0.91 using ED facility billing data and a sensitivity and specificity of 0.95 and 0.58 using Urology physician billing data. DISCUSSION/SIGNIFICANCE OF IMPACT: Use of the ICD-9-CM diagnosis codes for urinary retention performed well at identifying AUR in the ED. This study provides justification to use urinary retention diagnosis codes (specifically 788.20 and 788.29) in future studies to identify AUR using administrative data, especially in the ED.