Product architecture decisions are made early in the product development process and have far-reaching effects. Unless anticipated through experience or intuition, many of these effects may not be apparent until much later in the development process, making changes to the architecture costly in time, effort and resources. Many researchers through the years have studied various elements of product architecture and their effects. By using a repeatable process for aggregating statements on the effects of architecture strategies from a selection of the literature on the topic and storing them in a systematic database, this information can then be recalled and presented in the form of a Product Architecture Strategy and Effect (PASE) matrix. PASE matrices allow for the identification, comparison, evaluation, and then selection of the most desirable product architecture strategies before expending resources along a specific development path. This paper introduces the PASE Database and matrix and describes their construction and use in guiding design decisions. This paper also provides metrics for understanding the robustness of this database.

To illustrate that different approaches lead to different costs a cost calculation on four different welfare assessment systems for four different animal species has been carried out; an integrated pig herd (450 sows), a dairy cattle herd with automatic milking (90 cows), an organic egg production system (3000 layers) and a mink farm (1000 mink). We calculated the cost to be: €375 per annum for the mink farm and €2205, €2430 and €2435 for the egg production system, the AMS dairy herd and the integrated pig farm, respectively. The costs can be reduced by: reducing the number of indicators and/or the recording frequency, reducing sample sizes, more intensive use of existing data and by exchanging external for internal recordings.

Iodine intake affects the occurrence of thyroid disorders. However, the association of iodine intake with longevity remains to be described. This led us to perform a 20 years’ follow-up on participants from the Randers–Skagen (RaSk) study. Residents in Randers born in 1920 (n 210) and Skagen born in 1918–1923 (n 218) were included in a clinical study in 1997–1998. Mean iodine content in drinking water was 2 µg/l in Randers and 139 µg/l in Skagen. We collected baseline data through questionnaires, performed physical examinations and measured iodine concentrations in spot urine samples. Income data were retrieved from Danish registries. We performed follow-up on mortality until 31 December 2017 using Danish registries. Complete follow-up data were available on 428 out of 430 of participants (99·5 %). At baseline, the median urinary iodine concentration was 55 µg/l in Randers and 160 µg/l in Skagen residents. Participants were long-term residents with 72·8 and 92·7 % residing for more than 25 years in Randers and Skagen, respectively. Cox regression showed that living in Skagen compared with Randers was associated with a lower hazard ratio (HR) of death in both age- and sex-adjusted analyses (HR 0·60, 95 % CI 0·41, 0·87, P = 0·006), but also after adjustment for age, sex, number of drugs, Charlson co-morbidity index, smoking, alcohol and income (HR 0·60, 95 % CI 0·41, 0·87, P = 0·008). Residing in iodine-replete Skagen was associated with increased longevity. This indicates that long-term residency in an iodine-replete environment may be associated with increased longevity compared with residency in an iodine-deficient environment.

Use of antimicrobials for food-producing animals is a major public concern due to the risk of antimicrobial resistance. Although dairy production has a relatively low usage of antimicrobials, the potential for further reduction should be explored. The objective of the study was to estimate the current differences in antimicrobial use in Danish organic and conventional dairy herds and to describe the differences between them. Based on data from three different sources, 2604 herds (306 organic and 2298 conventional) were identified for the study. These herds had been either organic or conventional for the entire period from 2015 to 2018. Antimicrobial use was calculated as the treatment incidence in Animal Daily Doses (ADDs)/100 animals/day for three age groups: adult cattle, young stock and calves. For adult cattle, the ratio of median treatment incidence between conventional and organic production ranged from 2.8 : 1 to 3.4 : 1, depending on the specific year. For cows, 25% of the organic herds had a higher treatment incidence than the 25% of conventional herds with the lowest treatment incidence. Antimicrobial use for young stock was low and at a similar level in both the organic and conventional production systems. For calves, the median treatment incidence was 1.2 times higher in conventional herds and 1.6 times higher for the 75th percentile. Analyses of treatment incidence in adult cattle showed an overall decrease from 2015 to 2018 in both organic and conventional herds. The decrease was greater for the conventional herds (0.12 ADD/100 animals/day) compared to the organic herds (0.04 ADD/100 animals/day) over the 4-year period. In addition, herd size was an important risk factor for treatment incidence in conventional herds, increasing by 0.07 ADD/100 animals/day per 100 cows, whereas herd size had a minor influence on the treatment incidence in organic herds. The results of this study demonstrate the large variation in antimicrobial use within both organic and conventional herds, suggesting that further reduction is possible. Furthermore, herd size appears to be a risk factor in conventional herds but not in organic herds – an aspect that should be studied in more detail.

The Square Kilometre Array (SKA) is a planned large radio interferometer designed to operate over a wide range of frequencies, and with an order of magnitude greater sensitivity and survey speed than any current radio telescope. The SKA will address many important topics in astronomy, ranging from planet formation to distant galaxies. However, in this work, we consider the perspective of the SKA as a facility for studying physics. We review four areas in which the SKA is expected to make major contributions to our understanding of fundamental physics: cosmic dawn and reionisation; gravity and gravitational radiation; cosmology and dark energy; and dark matter and astroparticle physics. These discussions demonstrate that the SKA will be a spectacular physics machine, which will provide many new breakthroughs and novel insights on matter, energy, and spacetime.

Both blood- and milk-based biomarkers have been analysed for decades in research settings, although often only in one herd, and without focus on the variation in the biomarkers that are specifically related to herd or diet. Biomarkers can be used to detect physiological imbalance and disease risk and may have a role in precision livestock farming (PLF). For use in PLF, it is important to quantify normal variation in specific biomarkers and the source of this variation. The objective of this study was to estimate the between- and within-herd variation in a number of blood metabolites (β-hydroxybutyrate (BHB), non-esterified fatty acids, glucose and serum IGF-1), milk metabolites (free glucose, glucose-6-phosphate, urea, isocitrate, BHB and uric acid), milk enzymes (lactate dehydrogenase and N-acetyl-β-D-glucosaminidase (NAGase)) and composite indicators for metabolic imbalances (Physiological Imbalance-index and energy balance), to help facilitate their adoption within PLF. Blood and milk were sampled from 234 Holstein dairy cows from 6 experimental herds, each in a different European country, and offered a total of 10 different diets. Blood was sampled on 2 occasions at approximately 14 days-in-milk (DIM) and 35 DIM. Milk samples were collected twice weekly (in total 2750 samples) from DIM 1 to 50. Multilevel random regression models were used to estimate the variance components and to calculate the intraclass correlations (ICCs). The ICCs for the milk metabolites, when adjusted for parity and DIM at sampling, demonstrated that between 12% (glucose-6-phosphate) and 46% (urea) of the variation in the metabolites’ levels could be associated with the herd-diet combination. Intraclass Correlations related to the herd-diet combination were generally higher for blood metabolites, from 17% (cholesterol) to approximately 46% (BHB and urea). The high ICCs for urea suggest that this biomarker can be used for monitoring on herd level. The low variance within cow for NAGase indicates that few samples would be needed to describe the status and potentially a general reference value could be used. The low ICC for most of the biomarkers and larger within cow variation emphasises that multiple samples would be needed - most likely on the individual cows - for making the biomarkers useful for monitoring. The majority of biomarkers were influenced by parity and DIM which indicate that these should be accounted for if the biomarker should be used for monitoring.

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.

Unbalanced metabolic status in the weeks after calving predisposes dairy cows to metabolic and infectious diseases. Blood glucose, IGF-I, non-esterified fatty acids (NEFA) and β-hydroxybutyrate (BHB) are used as indicators of the metabolic status of cows. This work aims to (1) evaluate the potential of milk mid-IR spectra to predict these blood components individually and (2) to evaluate the possibility of predicting the metabolic status of cows based on the clustering of these blood components. Blood samples were collected from 241 Holstein cows on six experimental farms, at days 14 and 35 after calving. Blood samples were analyzed by reference analysis and metabolic status was defined by k-means clustering (k=3) based on the four blood components. Milk mid-IR analyses were undertaken on different instruments and the spectra were harmonized into a common standardized format. Quantitative models predicting blood components were developed using partial least squares regression and discriminant models aiming to differentiate the metabolic status were developed with partial least squares discriminant analysis. Cross-validations were performed for both quantitative and discriminant models using four subsets randomly constituted. Blood glucose, IGF-I, NEFA and BHB were predicted with respective R2 of calibration of 0.55, 0.69, 0.49 and 0.77, and R2 of cross-validation of 0.44, 0.61, 0.39 and 0.70. Although these models were not able to provide precise quantitative values, they allow for screening of individual milk samples for high or low values. The clustering methodology led to the sharing out of the data set into three groups of cows representing healthy, moderately impacted and imbalanced metabolic status. The discriminant models allow to fairly classify the three groups, with a global percentage of correct classification up to 74%. When discriminating the cows with imbalanced metabolic status from cows with healthy and moderately impacted metabolic status, the models were able to distinguish imbalanced group with a global percentage of correct classification up to 92%. The performances were satisfactory considering the variables are not present in milk, and consequently predicted indirectly. This work showed the potential of milk mid-IR analysis to provide new metabolic status indicators based on individual blood components or a combination of these variables into a global status. Models have been developed within a standardized spectral format, and although robustness should preferably be improved with additional data integrating different geographic regions, diets and breeds, they constitute rapid, cost-effective and large-scale tools for management and breeding of dairy cows.

We present an indentation-scope that interfaces with confocal microscopy, enabling direct observation of the three-dimensional (3D) microstructural response of coatings on substrates. Using this method, we compared microns-thick polymer coatings on glass with and without silica nanoparticle filler. Bulk force data confirmed the >30% modulus difference, while microstructural data further revealed slip at the glass-coating interface. Filled coatings slipped more and about two times faster, as reflected in 3D displacement and von Mises strain fields. Overall, these data indicate that silica-doping of coatings can dramatically alter adhesion. Moreover, this method compliments existing theoretical and modeling approaches for studying indentation in layered systems.

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.

Piglet mortality is a major problem in organic pig production affecting both farm economy and animal welfare. Knowledge is scarce on the risk factors of piglet mortality in Danish commercial organic pig production. The objectives of this study were to evaluate season, litter size, parity, sow body condition and stillborn littermates as risk factors for early piglet mortality and crushing of liveborn piglets from parturition until castration at day 3 to 5 postpartum (pp). The study was conducted over a 1-year period in nine commercial Danish organic pig herds practicing outdoor farrowing all year round. Data included recordings on 3393 farrowings with 50 284 liveborn piglets of which 14.8% died before castration. A subset of the dead piglets were collected and necropsied to identify crushed piglets. The average number of liveborn piglets per litter was 14.8 (SD=3.7) and the average time from parturition until castration was 4.1 (SD=1.7) days. A negative binomial regression analysis was used to model the effect of the predictive variables on the early piglet mortality accounting for different time periods from parturition to castration. An increase in maternal body condition score (BCS) and parity significantly increased the risk of dying between parturition and castration. Early mortality was found to be lowest during spring (March to May) and highest during summer (June to August). Being born into a litter with one or more stillborn littermates increased the risk of early mortality. The risk factors for crushing of piglets were evaluated using a logistic analysis. A significant effect of parity and litter size was found where the odds of at least one piglet in a litter with mortality was diagnosed as crushed increased with increasing parity and litter size. In conclusion, being born during summer (June to August), high parity and maternal BCS and stillborn littermates were found to be risk factors for piglet mortality between parturition and castration. In addition, parity and increasing litter size were found to be risk factors for crushing of piglets in litters with mortality.

In Danish organic pig production, one-third of total born piglets die before weaning, and stillbirth has previously crudely been estimated to account for 27% of the total preweaning mortality. The objective of this study was to evaluate season, litter size, parity and body condition of the sow as risk factors for stillbirth in nine commercial Danish organic pig herds. The study was conducted over a 1-year period, and the data included registrations on 5170 farrowings with 82 906 total born piglets. The average number of total born piglets per litter was 16.0, and the number of stillborn piglets per litter was 1.1. A significant effect of season was seen with an odds ratio for stillbirth of 1.15 during summer (May to August) compared with the remaining part of the year. A non-linear effect of litter size was seen where an increase in litter size from 11 to 16 resulted in an odds ratio of stillbirth of 1.11. An increase in litter size from 16 to 21 resulted in an odds ratio of stillbirth of 1.45. A significant interaction between body condition and parity was present. In first parity sows, an increase in body condition score from 2 (thin) to 3 (moderate) and from 3 to 4 (fat) increased the probability of stillbirth with an odds ratio of 1.23 and 1.36, respectively. In sows with parity above 4, an increase in body condition score from 2 to 3 and from 3 to 4 decreases the probability of stillbirth with an odds ratio of 0.68 and 0.79, respectively. In conclusion, increasing litter size and being born during the summer months of May to August were found to be risk factors for stillbirth. Furthermore, an interaction between body condition and parity showed that thin sows with parity above 4 had a substantially increased risk of stillbirth compared with normal and fat sows with parity above 4. In contrast, for parity 1 sows risk of stillbirth was increased in fat sows.