Search

The design of artificial, synthetic cells is a fundamentally important and fast-developing field of science. Of the diverse attributes of cellular life, artificial transmembrane signaling across the biomolecular barriers remains a high challenge with only a few documented successes. Herein, the study achieves signaling across lipid bilayers and connects an exofacial enzymatic receptor activation to an intracellular biochemical catalytic response using an artificial receptor. The mechanism of signal transduction for the artificial receptor relies on the triggered decomposition of a self-immolative linker. Receptor activation ensues its head-to-tail decomposition and the release of a secondary messenger molecule into the internal volume of the synthetic cell. Transmembrane signaling is demonstrated in synthetic cells based on liposomes and mammalian cell-sized giant unilamellar vesicles and illustrates receptor performance in cell mimics with a diverse size and composition of the lipid bilayer. In giant unilamellar vesicles, transmembrane signaling connects exofacial receptor activation with intracellular activation of proteolysis. Taken together, the results of this study take a step toward engineering receptor-mediated, responsive behavior in synthetic cells., (© 2024 The Authors. Advanced Biology published by Wiley‐VCH GmbH.)

Signal transduction across biological membranes is among the most important evolutionary achievements. Herein, for the design of artificial cells, we engineer fully synthetic receptors with the capacity of transmembrane signaling, using tools of chemistry. Our receptors exhibit similarity with their natural counterparts in having an exofacial ligand for signal capture, being membrane anchored, and featuring a releasable messenger molecule that performs enzyme activation as a downstream signaling event. The main difference from natural receptors is the mechanism of signal transduction, which is achieved using a self-immolative linker. The receptor scaffold is modular and can readily be re-designed to respond to diverse activation signals including biological or chemical stimuli. We demonstrate an artificial signaling cascade that achieves transmembrane enzyme activation, a hallmark of natural signaling receptors. Results of this work are relevant for engineering responsive artificial cells and interfacing them and/or biological counterparts in co-cultures., (© 2023. The Author(s).)

The goal of this study was to assess the feasibility of across-country genomic predictions in Norwegian White Sheep (NWS) and New Zealand Composite (NZC) sheep populations with similar development history. Different training populations were evaluated (i.e., including only NWS or NZC, or combining both populations). Predictions were performed using the actual phenotypes (normalized) and the single-step GBLUP via Bayesian inference. Genotyped NWS animals born in 2016 (N = 267) were used to assess the accuracy and bias of genomic estimated breeding values (GEBVs) predicted for birth weight (BW), weaning weight (WW), carcass weight (CW), EUROP carcass classification (EUC), and EUROP fat grading (EUF). The accuracy and bias of GEBVs differed across traits and training population used. For instance, the GEBV accuracies ranged from 0.13 (BW) to 0.44 (EUC) for GEBVs predicted including only NWS, from 0.06 (BW) to 0.15 (CW) when including only NZC, and from 0.10 (BW) to 0.41 (EUC) when including both NWS and NZC animals in the training population. The regression coefficients used to assess the spread of GEBVs (bias) ranged from 0.26 (BW) to 0.64 (EUF) for only NWS, 0.10 (EUC) to 0.52 (CW) for only NZC, and from 0.42 (WW) to 2.23 (EUC) for both NWS and NZC in the training population. Our findings suggest that across-country genomic predictions based on ssGBLUP might be possible for NWS and NZC, especially for novel traits., (© 2021 Wiley-VCH GmbH.)

The aim of this study was to investigate the effect of including milk yield data in the international genetic evaluation of female fertility traits to reduce or eliminate a possible bias because of across-country selection for milk yield. Data included two female fertility traits from Great Britain, Italy and the Netherlands, together with milk yield data from the same countries and from the United States, because the genetic trends in other countries may be influenced by selection decisions on bulls in the United States. Potentially, female fertility data had been corrected nationally for within-country selection and management biases for milk yield. Using a multiple-trait multiple across-country evaluation (MT-MACE) for the analysis of female fertility traits with milk yield, across-country selection patterns both for female fertility and milk yield can be considered simultaneously. Four analyses were performed; one single-trait multiple across-country evaluation analysis including only milk yield data, one MT-MACE analysis including only female fertility traits, and one MT-MACE analysis including both female fertility and milk yield traits. An additional MT-MACE analysis was performed including both female fertility and milk yield traits, but excluding the United States. By including milk yield traits to the analysis, female fertility reliabilities increased, but not for all bulls in all the countries by trait combinations. The presence of milk yield traits in the analysis did not considerably change the genetic correlations, genetic trends or bull rankings of female fertility traits. Even though the predicted genetic merits of female fertility traits hardly changed by including milk yield traits to the analysis, the change was not equally distributed to the whole data. The number of bulls in common between the two sets of Top 100 bulls for each trait in the two analyses of female fertility traits, with and without the four milk yield traits and their rank correlations were low, not necessarily because of the absence of the US milk yield data. The joint international genetic evaluation of female fertility traits with milk yield is recommended to make use of information on several female fertility traits from different countries simultaneously, to consider selection decisions for milk yield in the genetic evaluation of female fertility traits for obtaining more accurate estimating breeding values (EBV) and to acquire female fertility EBV for bulls evaluated for milk yield, but not for female fertility.

The study documents the procedures used to estimate genetic correlations among countries for overall conformation (OCS), overall udder (OUS), overall feet and legs (OFL), and body condition score (BCS) of Holstein sires. Major differences in traits definition are discussed, in addition to the use of international breeding values (IBV) among countries involved in international genetic evaluations, and similarities among countries through hierarchical clustering. Data were available for populations from 20 countries for OCS and OUS, 18 populations for OFL, and 11 populations for BCS. The IBV for overall traits and BCS were calculated using a multi-trait across-country evaluation model. Distance measures, obtained from genetic correlations, were used as input values in the cluster analysis. Results from surveys sent to countries participating in international genetic evaluation for conformation traits showed that different ways of defining traits are used: the overall traits were either computed from linear or composite traits or defined as general characteristics. For BCS, populations were divided into 2 groups: one scored and evaluated BCS, and one used a best predictor. In general, populations were well connected except for Estonia and French Red Holstein. The average number of common bulls for the overall traits ranged from 19 (OCS and OUS of French Red Holstein) to 514 (OFL of United States), and for BCS from 17 (French Red Holstein) to 413 (the Netherlands). The average genetic correlation (range) across countries was 0.75 (0.35 to 0.95), 0.80 (0.41 to 0.95), and 0.68 (0.12 to 0.89) for OCS, OUS, and OFL, respectively. Genetic correlations among countries that used angularity as best predictor for BCS and countries that scored BCS were negative. The cluster analysis provided a clear picture of the countries distances; differences were due to trait definition, trait composition, and weights in overall traits, genetic ties, and genotype by environment interactions. Harmonization of trait definition and increasing genetic ties could improve genetic correlations across countries and reduce the distances. In each national selection index, all countries, except Estonia and New Zealand, included at least one overall trait, whereas none included BCS. Out of 18 countries, 9 have started genomic evaluation of conformation traits. The first were Canada, France, New Zealand, and United States in 2009, followed by Switzerland, Germany, and the Netherlands in 2010, and Australia and Denmark-Finland-Sweden (joint evaluation) in 2011. Six countries are planning to start soon., (Copyright © 2012 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Genetic parameters were estimated for protein yield (PY), clinical mastitis (CM), somatic cell score, number of inseminations (NI) and days from calving to first insemination (CFI) in first-parity Swedish Red cows by series of tri-variate linear animal models. The heritability of PY was moderate (0.34 ± 0.004), and the heritabilities of the functional traits were all low (0.014 ± 0.001-0.14 ± 0.004). The genetic correlation between CM and CFI (0.38 ± 0.05) was stronger than the correlation between CM and NI (0.05 ± 0.06), perhaps because CM and CFI usually are observed in early lactation when the cow is likely to be in negative energy balance, whereas NI generally is recorded when the cow is not in negative energy balance any more. The genetic correlation between NI and CFI was very close to zero (-0.002 ± 0.05), indicating that these two fertility traits have different genetic backgrounds. All genetic correlations between PY and the functional traits were moderate and unfavourable, ranging from 0.22 ± 0.02 to 0.47 ± 0.03. In addition, the effect of including genetic and phenotypic correlations between the trait groups milk production, udder health and female fertility on the accuracy of the selection index was quantified for a heifer, a cow and a proven bull. The difference between the accuracy obtained by multi-trait and single-trait evaluations was largest for the cow (0.012) and small for the heifer and the bull (0.006 and 0.004) because the phenotype of the cow for one trait could assist in predicting the Mendelian sampling term for a correlated trait., (© 2010 Blackwell Verlag GmbH.)

The current method in use for international genetic evaluations, called single-trait multiple across-country evaluation (ST-MACE), does not consider residual covariances among traits, making possible only the inclusion of one trait per country in an analysis. The aim of this study was to quantify the effect of bias resulting from treating traits from the same country as nationally independent in an international genetic evaluation. Data from the September 2007 Interbull test evaluation for Holstein female fertility traits were used. Data included were 1 trait from Belgium, Switzerland, Spain, and the United States of America, and 2 traits from Canada, Germany-Austria, and Denmark-Finland-Sweden. The biased results were obtained from a 10-variate ST-MACE analysis including all country traits. The unbiased results were obtained from 8 different 7-variate ST-MACE analyses, each including only 1 trait per country. Average absolute bias in the genetic correlations among 2-trait countries (0.11) was higher than for between 1-trait countries and 2-trait countries (0.07) and for among 1-trait countries (0.03). The results of the biased and the unbiased analyses were different, not only due to bias, but also because of different number of traits involved in the analyses. Differences were considerable (on average, 0.08 to 6.91) for reliabilities, which were higher for traits with lower heritability. Average differences were minor (-0.04 to 0.03 standard deviations) for predicted genetic merits. However, for the top 100 bulls in each country trait, these differences were important (on average, -0.26 to 0.11 standard deviation of predicted genetic merit), which caused considerable changes in bull rankings. The results of this study showed that the effect of bias, caused by ignoring covariances from multiple-trait national models in an ST-MACE analysis, is of such a magnitude that necessitates the use of another method such as multiple-trait multiple across-country evaluation., (Copyright © 2011 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Hoof diseases are a problem in many dairy herds. To study one aspect of the problem, genetic correlations between 4 hoof diseases, protein yield, clinical mastitis, number of inseminations, and days from calving to first insemination were estimated in first-parity Swedish Red cows using trivariate linear animal models. Occurrence of dermatitis, heel horn erosion, sole hemorrhage, and sole ulcer were reported by hoof trimmers. The data set contained about 314,000 animals with records on at least one of the traits; among these, about 64,000 animals had records on hoof diseases. Heritabilities were low for all hoof diseases (0.03 to 0.05). The hoof diseases fell into 2 groups: (1) dermatitis and heel horn erosion (i.e., diseases related to hygiene) and (2) sole hemorrhage and sole ulcer (i.e., diseases related to feeding). The genetic correlations between traits within the 2 groups were high (0.87 and 0.73, respectively), whereas the genetic correlations between traits in different groups were low (≤0.23). These results indicate that the 2 groups of hoof diseases are partly influenced by the same genes. All genetic correlations between hoof diseases and protein yield were low to moderate and unfavorable. Moderate and favorable genetic correlations were found between the feed-related hoof diseases and clinical mastitis (0.35 and 0.32), whereas the genetic correlations between the hygiene-related hoof diseases and clinical mastitis were low and not significantly different from zero. The genetic correlations between the hygiene-related hoof diseases and number of inseminations were low to moderate and favorable (0.32 and 0.22), and the genetic correlations between the feed-related hoof diseases and number of inseminations were low and not significantly different from zero. A moderate genetic correlation was found between sole ulcer and days from calving to first insemination (0.33), whereas the genetic correlations between days from calving to first insemination and sole hemorrhage and the hygiene-related hoof diseases were low and not significantly different from zero. In general, the 2 groups of hoof diseases showed different patterns of genetic correlations to the other functional traits, but both were unfavorably correlated to protein yield. A simulation study showed that inclusion of hoof diseases in the selection index will not only reduce the genetic decline in resistance to hoof diseases but also be favorable for other functional traits and improve overall genetic merit., (Copyright © 2011 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The need to implement a method that can handle multiple traits per country in international genetic evaluations is evident. Today, many countries have implemented multiple-trait national genetic evaluations and they may expect to have their traits simultaneously analyzed in international genetic evaluations. Traits from the same country are residually correlated and the method currently in use, single-trait multiple across-country evaluation (ST-MACE), cannot handle nonzero residual correlations. Therefore, multiple-trait, multiple across-country evaluation (MT-MACE) was proposed to handle several traits from the same country simultaneously. To test the robustness of MT-MACE on real data, female fertility was chosen as a complex trait with low heritability. Data from 7 Holstein populations, 3 with 2 traits and 4 with 1 trait, were used. The differences in the estimated genetic correlations by MT-MACE and the single ST-MACE analysis (average absolute deviation of 0.064) were due to the bias of considering several traits from the same country in the ST-MACE analysis. However, the differences between the estimated genetic correlations by MT-MACE and multiple ST-MACE analyses avoiding more than one trait per country in each analysis (average absolute deviation of 0.066) were due to the lack of analysis of the correlated traits from the same country together and using the reported within-country genetic correlations. Applying MT-MACE resulted in reliability gain in international genetic evaluations, which was different from trait to trait and from bull to bull. The average reliability gain by MT-MACE over ST-MACE was 3.0 points for domestic bulls and 6.3 points for foreign bulls. Even countries with 1 trait benefited from the joint analysis of traits from the 2-trait countries. Another superiority of MT-MACE over ST-MACE is that the bulls that do not have national genetic evaluation for some traits from multiple trait countries will receive international genetic evaluations for those traits. Rank correlations were high between ST-MACE and MT-MACE when considering all bulls. However, the situation was different for the top 100 bulls. Simultaneous analysis of traits from the same country affected bull ranks, especially for top 100 bulls. Multi-trait MACE is a recommendable and robust method for international genetic evaluations and is appropriate for handling multiple traits per country, which can increase the reliability of international genetic evaluations., (Copyright © 2010 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The aims of this study were to document, present, and discuss the procedure used to calculate the international estimated breeding value (EBV) for longevity for Brown Swiss, Guernsey, Holstein, Jersey, Red Dairy Cattle, and Simmental breeds. Data from 19 countries and 123,833 national sires' breeding value were used for this purpose. Trait definitions and national genetic evaluation procedures were first summarized; and this showed that differences among countries existed. International breeding values for direct longevity were calculated using a multi-trait across-country evaluation model. The data editing method was identical to the one used for the February 2007 routine international genetic evaluation. Estimated genetic correlations presented in this study were similar to those presented in the literature and, in general, differed from unity because of differences in trait definitions, culling reasons, data included, evaluation procedures, genotype-environment interactions, and weak genetic ties among countries. The average genetic correlations for Holstein ranged from 0.49 to 0.76. The genetic correlations for Brown Swiss and Guernsey ranged from 0.29 to 0.95 and from 0.30 to 0.89, respectively. For Jersey and Red Dairy Cattle the genetic correlations ranged from 0.39 to 0.61 and from 0.30 to 0.96, respectively. For Simmental the genetic correlation was 0.59. Different predictors were used at national levels to define combined longevity. These predictors were combined using economic and empirical weights. Three out of 15 countries published international EBV of direct longevity only and 12 out of 15 countries combined direct longevity with predictors (combined longevity). International breeding values for longevity were combined into the total merit index by most of the member organizations and made available to breeders across the world through magazines and Web sites. Even if some breeders are not familiar with longevity EBV, they will select for this trait automatically if they use the published total merit indexes.

In the present work, covariance components for milk yield and disease liability were estimated with bivariate random regression test-day sire models using a Bayesian approach and implemented via the Gibbs sampler. The data consist of 8075 first-parity Danish Holstein (DH) cows, from 1259 sires, performing in 57 herds from 1992 to 1997. Treatments associated with five different type of diseases were pooled into a single general disease liability for each cow. Two models were fitted to the data. First, using a bivariate model, milk yield is modeled via a random regression, and disease liability via a repeatablility model. Second, using a bivariate model, both milk yield and disease liability are modeled using random regressions. A comparison based on a Bayes factor provides very strong support for the bivariate random regression model. Posterior means of heritabilities for each of the traits were estimated for five different points in time throughout lactation. Across models, heritabilities for milk yield are lowest in the beginning of the lactation (0.19) and highest at the end of the lactation (0.35). Posterior means of heritabilities of disease liability range from 0.04 to 0.10 for test days, and is equal to 0.20 for the whole lactation. Heritability of persistency measures estimated from the two models are 0.20 and 0.21. Estimates of posterior means of genetic correlations between single test-day milk yield and single test-day disease liability are in the range of 0.31 to 0.57. The estimates of posterior mean and of the 95% posterior interval of the genetic correlation between persistency and (total) disease liability using the model with the highest posterior probability are -0.12 and (-0.44; 0.20), respectively. Even though the largest proportion of the posterior probability mass is spread along negative values of the correlation (indicating that individuals with a flatter lactation curve tend to have lower disease liability), a value of zero of the genetic correlation falls comfortably within the 95% posterior interval. Thus the prospects of reducing incidence of disease by manipulating persistency as defined in this work remain inconclusive.

(Co)variance components for milk, fat, and protein yield of 8075 first-parity Danish Holsteins (DH) were estimated in random regression models by REML. For all analyses, the fixed part of the model was held constant, whereas four different functions were applied to model the additive genetic effect and the permanent environment effect. Homogeneous residual variance was assumed throughout lactation. Univariate models were compared using a minimum of -2 ln(restricted likelihood) as the criterion for best fit. Heritabilities as a function of time were calculated from the estimated curve parameters from univariate analyses. Independent of the function applied and the trait in question, heritabilities were lowest in the beginning of the lactation. Heritabilities for persistency of fat yield were slightly higher than heritabilities for persistency of milk and protein yield. Genetic correlations between persistency and 305-d production were higher for protein and milk yield than for fat yield. Bivariate analyses between the production traits were carried out in sire models using the models with the best 3-parameter curve fit in the univariate analyses. Correlations between traits were calculated from covariance components for curve parameters estimated in bivariate analyses. Genetic correlations between milk and protein yield were higher than between milk and fat yield.

On the background of increasing international interest for early diagnosis, pathogenesis and treatment of cholesteatoma in children, a sub-group is described and termed primary cholesteatoma, congenital cholesteatoma in the middle ear or cholesteatoma in the middle ear behind an intact ear-drum. This review is based on a retrospective review of cholesteatoma in children submitted to operation in Hjørring Hospital during a period of five years from 1986 to 1991. All of the children were under the age of 16 years. Out of 22 cholesteatoma treated operatively in children, the authors found six cases (27%) of congenital cholesteatoma. The symptomless congenital cholesteatoma in the middle ear is recognized clinically by otoscopy where it appears as a white shadow, most frequently in the anterior upper quadrant, behind an intact ear-drum. Early diagnosis is important and may be established by general practitioners, paediatricians and otologists at routine otoscopy.

During a period of 12 years, a total of 203 patients with lymph node metastases in the neck were referred. At the conclusion of the programme of investigation, the primary tumours remained undiagnosed in 37 patients. The primary tumours were diagnosed in 18 patients, ten of these were alive. The primary tumours were most commonly localized in the head and neck region with the possibility for curative treatment. Patients with small metastases experienced good effects from regional treatment in the form of cervical dissection and radiotherapy with a five-year crude survival of 53%