The Subglacial Antarctic Lakes Scientific Access (SALSA) Project accessed Mercer Subglacial Lake using environmentally clean hot-water drilling to examine interactions among ice, water, sediment, rock, microbes and carbon reservoirs within the lake water column and underlying sediments. A ~0.4 m diameter borehole was melted through 1087 m of ice and maintained over ~10 days, allowing observation of ice properties and collection of water and sediment with various tools. Over this period, SALSA collected: 60 L of lake water and 10 L of deep borehole water; microbes >0.2 μm in diameter from in situ filtration of ~100 L of lake water; 10 multicores 0.32–0.49 m long; 1.0 and 1.76 m long gravity cores; three conductivity–temperature–depth profiles of borehole and lake water; five discrete depth current meter measurements in the lake and images of ice, the lake water–ice interface and lake sediments. Temperature and conductivity data showed the hydrodynamic character of water mixing between the borehole and lake after entry. Models simulating melting of the ~6 m thick basal accreted ice layer imply that debris fall-out through the ~15 m water column to the lake sediments from borehole melting had little effect on the stratigraphy of surficial sediment cores.

Rush skeletonweed is an aggressive perennial weed that establishes itself on land in the Conservation Reserve Program (CRP), and persists during cropping following contract expiration. It depletes critical soil moisture required for yield potential of winter wheat. In a winter wheat/fallow cropping system, weed control is maintained with glyphosate and tillage during conventional fallow, and with herbicides only in no-till fallow. Research was conducted for control of rush skeletonweed at two sites in eastern Washington, Lacrosse and Hay, to compare the effectiveness of a weed-sensing sprayer and broadcast applications of four herbicides (aminopyralid, chlorsulfuron + metsulfuron, clopyralid, and glyphosate). Experimental design was a split-plot with herbicide and application type as main and subplot factors, respectively. Herbicides were applied in the fall at either broadcast or spot-spraying rates depending on sprayer type. Rush skeletonweed density in May was reduced with use of aminopyralid (1.1 plants m−2), glyphosate (1.4 plants m−2), clopyralid (1.7 plants m−2), and chlorsulfuron + metsulfuron (1.8 plants m−2) compared with the nontreated check (2.6 plants m−2). No treatment differences were observed after May 2019. There was no interaction between herbicide and application system. Area covered using the weed-sensing sprayer was, on average, 52% (P < 0.001) less than the broadcast application at the Lacrosse location but only 20% (P = 0.01) at the Hay location. Spray reduction is dependent on foliar cover in relation to weed density and size. At Lacrosse, the weed-sensing sprayer reduced costs for all herbicide treatments except aminopyralid, with savings up to US$6.80 per hectare. At Hay, the weed-sensing sprayer resulted in economic loss for all products because of higher rush skeletonweed density. The weed-sensing sprayer is a viable fallow weed control tool when weed densities are low or patchy.

Over half of the Irish population is overweight or obese. The Obesity Policy and Action Plan 2016–2025 will set reformulation targets for fat, saturated fat and sugar in Ireland and review progress. In 2016, the Food Safety Authority of Ireland undertook a cross-sectional market scan of yoghurts to evaluate the energy, fat, saturated fat and sugar content based solely on declared nutrition labels. The aims of this 2018 study were to verify the accuracy of declared nutrition information on yoghurts and to confirm the suitability of declared nutrition labels for energy, fat, saturated fat and sugar reformulation monitoring.

Yoghurts identified in the 2016 market scan (n578) were weighted based on categorisation of manufacturer type (branded, own brand), product category (natural, flavoured and luxury) and declared nutrition content. Samples (n200) were randomly selected from these weighted groups and tested by a laboratory accredited for energy, fat, saturated fat, and sugar analysis. Data was analysed using IBM SPSS (version25). As data was not normally distributed, median values were investigated for declared and tested energy, fat, saturated fat and sugar content using Wilcoxon Signed-Rank Test and Spearman Rank-Order Correlation.

Of the tested yoghurts, 3% (n6), 5% (n9) and 19% (n31) were outside the recommended European Commission (EC) labelling tolerance for fat, saturated fat and sugar, respectively. Tested nutrient content was consistently lower than declared. There was a statistically significant difference in declared vs. tested energy (87kcal vs. 84kcal p = 0.03), fat (2.7 g vs. 2.5 g p < 0.001), and sugar (9.9 g vs. 8.7 g p < 0.001) content per 100 g yoghurt. Declared vs. tested sugar content per 100 g yoghurt was statistically significant across all yoghurt types, including natural (4.8 g vs. 3.4 g p < 0.001), flavoured (9.7 g vs. 8.6 g p < 0.001) and luxury (15 g vs. 13.6 g p = 0.002). There was a statistically significant difference between declared vs. tested fat (2.8 g vs. 2.5 g p < 0.001) and saturated fat (1.9 g vs.1.6 g p = 0.017) content of own brand yoghurts per 100 g. There was a positive correlation between energy content and portion size (r = .2,p < 0.01).

There was a high level of agreement between declared vs. tested fat and saturated fat content of yoghurts, but a lower level of agreement between declared vs. tested sugar content of yoghurts. This indicates that declared nutrition labels are suitable for reformulation monitoring of fat and saturated fat, but may not be suitable for sugar. This finding will be further investigated and tested in future work planned for nutrition label verification of other food categories.

The use of nutrition and health claims on food is legislated for in Commission Regulation 1924/2006 and SI No. 11 of 2014. This legislation ensures that any claim made on a food label is clear, accurate and substantiated, enabling consumers to make informed choices. A study undertaken by the Food Safety Authority of Ireland (FSAI) in 2009 found that yoghurts were the food category with the highest use of nutrition and health claims on the Irish market.

In 2018, the FSAI undertook a nutrition label verification study to verify the accuracy of declared nutrition information on yoghurts. The aims of this study were to measure the use of nutrition and health claims on a sample of yoghurts available on the Irish market in 2018 and assess their compliance with Regulation 1924/2006 Nutrition and Health Claims made on Food.

Yoghurts identified in a 2016 market scan (n578) were weighted based on categorisation of manufacturer type (branded, own brand), product category (natural, flavoured and luxury) and declared nutrition content. Samples (n200) were randomly selected from these weighted groups for the 2018 nutrition label verification study. A subsample (n100) was randomly sampled and checked for presence of nutrition and health claims. Presence of nutrition and health claims was recorded in Microsoft Excel and checked for compliance with Regulation 1924/2006.

Of the yoghurts reviewed, 67% (n67) made at least one nutrition claim and 34% (n34) made at least one health claim. Of these, 29% (n29) made a nutrition and a health claim. Branded yoghurts were more likely to make nutrition and health claims than own brand yoghurts (78% (n49) vs. 48% (n18)). Of yoghurts with a health claim, 88% (n30) were branded and 12% (n4) were own brand. Of yoghurts with a nutrition claim, 1.5% (n1) made a nutrition claim which was potentially non-compliant with Regulation 1924/2006. Of yoghurts with a health claim, 74% (n23) made a health claim which was potentially non-compliant with Regulation 1924/2006. The majority of potentially non-compliant health claims were in relation to probiotic strains and ‘live cultures’.

In conclusion, yoghurts continue to be a food category which often uses nutrition and health claims. Nutrition and health claims are more frequently used by branded than own brand products. Potentially non-compliant health claims are an issue amongst this food category which will be further investigated and followed up.

The COllaborative project of Development of Anthropometrical measures in Twins (CODATwins) project is a large international collaborative effort to analyze individual-level phenotype data from twins in multiple cohorts from different environments. The main objective is to study factors that modify genetic and environmental variation of height, body mass index (BMI, kg/m2) and size at birth, and additionally to address other research questions such as long-term consequences of birth size. The project started in 2013 and is open to all twin projects in the world having height and weight measures on twins with information on zygosity. Thus far, 54 twin projects from 24 countries have provided individual-level data. The CODATwins database includes 489,981 twin individuals (228,635 complete twin pairs). Since many twin cohorts have collected longitudinal data, there is a total of 1,049,785 height and weight observations. For many cohorts, we also have information on birth weight and length, own smoking behavior and own or parental education. We found that the heritability estimates of height and BMI systematically changed from infancy to old age. Remarkably, only minor differences in the heritability estimates were found across cultural–geographic regions, measurement time and birth cohort for height and BMI. In addition to genetic epidemiological studies, we looked at associations of height and BMI with education, birth weight and smoking status. Within-family analyses examined differences within same-sex and opposite-sex dizygotic twins in birth size and later development. The CODATwins project demonstrates the feasibility and value of international collaboration to address gene-by-exposure interactions that require large sample sizes and address the effects of different exposures across time, geographical regions and socioeconomic status.

Whether monozygotic (MZ) and dizygotic (DZ) twins differ from each other in a variety of phenotypes is important for genetic twin modeling and for inferences made from twin studies in general. We analyzed whether there were differences in individual, maternal and paternal education between MZ and DZ twins in a large pooled dataset. Information was gathered on individual education for 218,362 adult twins from 27 twin cohorts (53% females; 39% MZ twins), and on maternal and paternal education for 147,315 and 143,056 twins respectively, from 28 twin cohorts (52% females; 38% MZ twins). Together, we had information on individual or parental education from 42 twin cohorts representing 19 countries. The original education classifications were transformed to education years and analyzed using linear regression models. Overall, MZ males had 0.26 (95% CI [0.21, 0.31]) years and MZ females 0.17 (95% CI [0.12, 0.21]) years longer education than DZ twins. The zygosity difference became smaller in more recent birth cohorts for both males and females. Parental education was somewhat longer for fathers of DZ twins in cohorts born in 1990–1999 (0.16 years, 95% CI [0.08, 0.25]) and 2000 or later (0.11 years, 95% CI [0.00, 0.22]), compared with fathers of MZ twins. The results show that the years of both individual and parental education are largely similar in MZ and DZ twins. We suggest that the socio-economic differences between MZ and DZ twins are so small that inferences based upon genetic modeling of twin data are not affected.

The Square Kilometre Array will be an amazing instrument for pulsar astronomy. While the full SKA will be sensitive enough to detect all pulsars in the Galaxy visible from Earth, already with SKA1, pulsar searches will discover enough pulsars to increase the currently known population by a factor of four, no doubt including a range of amazing unknown sources. Real time processing is needed to deal with the 60 PB of pulsar search data collected per day, using a signal processing pipeline required to perform more than 10 POps. Here we present the suggested design of the pulsar search engine for the SKA and discuss challenges and solutions to the pulsar search venture.

Crop yield loss–weed density relationships critically influence calculation of economic thresholds and the resulting management recommendations made by a bioeconomic model. To examine site-to-site and year-to-year variation in winter Triticum aestivum L. (winter wheat)–Aegilops cylindrica Host. (jointed goatgrass) interference relationships, the rectangular hyperbolic yield loss function was fit to data sets from multiyear field experiments conducted at Colorado, Idaho, Kansas, Montana, Nebraska, Utah, Washington, and Wyoming. The model was fit to three measures of A. cylindrica density: fall seedling, spring seedling, and reproductive tiller densities. Two parameters: i, the slope of the yield loss curve as A. cylindrica density approaches zero, and a, the maximum percentage yield loss as A. cylindrica density becomes very large, were estimated for each data set using nonlinear regression. Fit of the model to the data was better using spring seedling densities than fall seedling densities, but it was similar for spring seedling and reproductive tiller densities based on the residual mean square (RMS) values. Yield loss functions were less variable among years within a site than among sites for all measures of weed density. For the one site where year-to-year variation was observed (Archer, WY), parameter a varied significantly among years, but parameter i did not. Yield loss functions differed significantly among sites for 7 of 10 comparisons. Site-to-site statistical differences were generally due to variation in estimates of parameter i. Site-to-site and year-to-year variation in winter T. aestivum–A. cylindrica yield loss parameter estimates indicated that management recommendations made by a bioeconomic model cannot be based on a single yield loss function with the same parameter values for the winter T. aestivum-producing region. The predictive ability of a bioeconomic model is likely to be improved when yield loss functions incorporating time of emergence and crop density are built into the model's structure.

Spray and granular forms of isopropyl N-(3-chlorophenyl)-carbamate (CIPC, ethyl N,N-dipropylthiocarbamate (EPTC), a,a,a-trifluoro-2,6-dinitro-N,N-dipropyl-p-toluidine (trifluralin) and 2,4-bis(isopropylamino) -6-methylmercapto-s-triazine (prometryne) were incorporated preemergence to barnyardgrass Echinochloa crusgalli (1.) Beauv.) and broadleaf weeds with a rotary tiller, wheel hoe, rotary spike, or row wheel into pre-irrigated or dry soil at three locations. The locations varied in type of soil, climatic conditions, and the species of broadleaf weeds. Rotary tiller incorporation generally resulted in best weed control with both spray and granular forms under all conditions. Herbicides usually performed best in pre-irrigated soil. Sprays and granules were equal in performance at coastal and intermediate locations; granules were more effective in the desert location. Herbicidal effectiveness was influenced by method of incorporation, form of treatment, soil moisture, environmental location, and weed type.

The surface waters of the Southern Ocean play a key role in the global climate and carbon cycles by promoting growth of some of the world’s largest phytoplankton blooms. Several studies have emphasized the importance of glacial and sediment inputs of Fe that fuel the primary production of the Fe-limited Southern Ocean. Although the fertile surface waters along the shelf of the western Antarctic Peninsula (WAP) are influenced by large inputs of freshwater, this freshwater may take multiple pathways (e.g. calving, streams, groundwater discharge) with different degrees of water-rock interactions leading to variable Fe flux to coastal waters. During the summers of 2012–13 and 2013–14, seawater samples were collected along the WAP, near Anvers Island, to observe water column dynamics in nearshore and offshore waters. Tracers (223,224Ra, 222Rn, 18O, 2H) were used to evaluate the source and transport of water and nutrients in coastal fjords and across the shelf. Coastal waters are compared across two field seasons, with increased freshwater observed during 2014. Horizontal mixing rates of water masses along the WAP ranged from 110–3600 m2 s-1. These mixing rates suggest a rapid transport mechanism for moving meltwater offshore.

Secale cereale is a serious weed problem in winter Triticum aestivum–producing regions. The interference relationships and economic thresholds of S. cereale in winter T. aestivum in Colorado, Kansas, Nebraska, and Wyoming were determined over 4 yr. Winter T. aestivum density was held constant at recommended planting densities for each site. Target S. cereale densities were 0, 5, 10, 25, 50, or 100 plants m−2. Secale cereale–winter T. aestivum interference relationships across locations and years were determined using a negative hyperbolic yield loss function. Two parameters—I, which represents the percent yield loss as S. cereale density approaches zero, and A, the maximum percent yield loss as S. cereale density increases—were estimated for each data set using nonlinear regression. Parameter I was more stable among years within locations than among locations within years, whereas maximum percentage yield loss was more stable across locations and years. Environmental conditions appeared to have a role in the stability of these relationships. Parameter estimates for I and A were incorporated into a second model to determine economic thresholds. On average, threshold values were between 4 and 5 S. cereale plants m−2; however, the large variation in these threshold values signifies considerable risk in making economic weed management decisions based upon these values.

A survey of 174 fields was conducted during August and September of 1998 to investigate effects of cultural and herbicide practices on postharvest weed control in winter wheat stubble fields across western and southern Nebraska. Seventy-four percent of the fields were seeded at rates of 67 to 100 kg/ha, with 60% of the wheat seeded in rows spaced 25 cm apart. Wheat seeded in east–west rows contained 98% more stinkgrass and 82% more tumble pigweed than wheat seeded in north–south rows. Sixty-nine percent of winter wheat stubble fields were rated excellent for weed control. Postharvest weed control with herbicides was not affected by row spacing. In western Nebraska, density of tumble pigweed and Russian thistle was greater when wheat seeding rate was 50 kg/ha than at higher seeding rates. Short-stature winter wheat cultivars had greater densities of Pennsylvania smartweed and toothed spurge than taller cultivars. The most common winter wheat cultivars were ‘Arapahoe’ (34%) and ‘Alliance’ (17%). Weed control was positively correlated with number of winter wheat stems per square meter (r = 0.22∗∗). Density of several weed species was greater in fields seeded with a disk than with a hoe drill. The most common crop rotations sampled were winter wheat–corn–fallow (50%), winter wheat–fallow (18%), and winter wheat–corn–soybean (13%). Winter wheat yields and wheat stem densities were greater and weed density was less when winter wheat was seeded after an 11- to 14-mo fallow period rather than a 0- to 5-mo period.

A survey of 174 fields was conducted to investigate performance of herbicides applied after winter wheat harvest on weeds across western and southern Nebraska during August and September 1998. Glyphosate plus 2,4-D plus atrazine was applied on 32%, glyphosate plus 2,4-D or dicamba on 24%, paraquat plus atrazine on 23%, glyphosate on 8%, ICIA0224 plus 2,4-D or atrazine on 10%, and atrazine plus 2,4-D on 3% of the fields. These treatments controlled 85 to 100% of the weeds except atrazine plus 2,4-D, which controlled 30%. The frequency of occurrence of the most prevalent summer annual grasses was as follows: green foxtail, 65%; barnyardgrass, 46%; stinkgrass, 41%; witchgrass, 39%; and longspine sandbur, 36%. The most common broadleaf weeds and their frequency were redroot pigweed, 32%; tumble pigweed, 30%; tall waterhemp, 28%; and kochia, 25%. Virginia groundcherry, 22%; common milkweed, 11%; yellow woodsorrel, 9%; and field bindweed, 6% were the most common perennial weeds. The five most difficult weeds to control were yellow nutsedge, spotted spurge, Virginia groundcherry, common milkweed, and toothed spurge, with control ratings of 0, 3, 17, 26, and 33%, respectively. These weeds were not controlled with glyphosate or mixtures containing glyphosate. Only 35% of the fields were treated before summer annual grasses had headed. Late applications required higher herbicides rates for effective control.

Three models that empirically predict crop yield from crop and weed density were evaluated for their fit to 30 data sets from multistate, multiyear winter wheat–jointed goatgrass interference experiments. The purpose of the evaluation was to identify which model would generally perform best for the prediction of yield (damage function) in a bioeconomic model and which model would best fulfill criteria for hypothesis testing with limited amounts of data. Seven criteria were used to assess the fit of the models to the data. Overall, Model 2 provided the best statistical description of the data. Model 2 regressions were most often statistically significant, as indicated by approximate F tests, explained the largest proportion of total variation about the mean, gave the smallest residual sum of squares, and returned residuals with random distribution more often than Models 1 and 3. Model 2 performed less well based on the remaining criteria. Model 3 outperformed Models 1 and 2 in the number of parameters estimated that were statistically significant. Model 1 outperformed Models 2 and 3 in the proportion of regressions that converged on a solution and more readily exhibited an asymptotic relationship between winter wheat yield and both winter wheat and jointed goatgrass density under the constraint of limited data. In contrast, Model 2 exhibited a relatively linear relationship between yield and crop density and little effect of increasing jointed goatgrass density on yield, thus overpredicting yield at high weed densities when data were scarce. Model 2 had statistical properties that made it superior for hypothesis testing; however, Model 1's properties were determined superior for the damage function in the winter wheat–jointed goatgrass bioeconomic model because it was less likely to cause bias in yield predictions based on data sets of minimum size.

An image analysis method is developed and applied to shadowgraph images of supersonic jet flow to measure shock front propagation angles at numerous interrogation points distributed throughout the quiescent region outside of the jet shear layer. These shock fronts manifest in acoustic measurements of jet noise as steepened temporal waveforms that have been linked to the perception of crackle. The analysis method uses the Radon transform to quantitatively determine a local shock front propagation angle at each point. The dataset of angles is subsequently used to determine the locations and convection velocities of the sources inside the jet shear layer. The results indicate that the shock-like waves emerge immediately from the jet shear layer and are created by the supersonic convection of coherent structures. The statistical distribution of convection velocities follows an extreme value distribution, indicating that the shock front emitting sources are maxima of the underlying turbulence. A noise reduction method known to reduce the convection velocities in the jet shear layer is applied to the jet to investigate the effect on the shock front emission. The shock front angles change in concert with the reduction in convection velocity giving further evidence that the source of crackle is a flow field event.

A trend toward greater body size in dizygotic (DZ) than in monozygotic (MZ) twins has been suggested by some but not all studies, and this difference may also vary by age. We analyzed zygosity differences in mean values and variances of height and body mass index (BMI) among male and female twins from infancy to old age. Data were derived from an international database of 54 twin cohorts participating in the COllaborative project of Development of Anthropometrical measures in Twins (CODATwins), and included 842,951 height and BMI measurements from twins aged 1 to 102 years. The results showed that DZ twins were consistently taller than MZ twins, with differences of up to 2.0 cm in childhood and adolescence and up to 0.9 cm in adulthood. Similarly, a greater mean BMI of up to 0.3 kg/m2 in childhood and adolescence and up to 0.2 kg/m2 in adulthood was observed in DZ twins, although the pattern was less consistent. DZ twins presented up to 1.7% greater height and 1.9% greater BMI than MZ twins; these percentage differences were largest in middle and late childhood and decreased with age in both sexes. The variance of height was similar in MZ and DZ twins at most ages. In contrast, the variance of BMI was significantly higher in DZ than in MZ twins, particularly in childhood. In conclusion, DZ twins were generally taller and had greater BMI than MZ twins, but the differences decreased with age in both sexes.