Nonparallel genome changes within subpopulations over time contributed to genetic diversity within the US Holstein population.

Maintaining genetic variation in a population is important for long-term genetic gain. The existence of subpopulations within a breed helps maintain genetic variation and diversity. The 20,990 genotyped animals, representing the breeding animals in the year 2014, were identified as the sires of animals born after 2010 with at least 25 progenies, and females measured for type traits within the last 2 yr of data. K-means clustering with 5 clusters (C1, C2, C3, C4, and C5) was applied to the genomic relationship matrix based on 58,990 SNP markers to stratify the selected candidates into subpopulations. The general higher inbreeding resulting from within-cluster mating than across-cluster mating suggests the successful stratification into genetically different groups. The largest cluster (C4) contained animals that were less related to each animal within and across clusters. The average fixation index was 0.03, indicating that the populations were differentiated, and allele differences across the subpopulations were not due to drift alone. Starting with the selected candidates within each cluster, a family unit was identified by tracing back through the pedigree, identifying the genotyped ancestors, and assigning them to a pseudogeneration. Each of the 5 families (F1, F2, F3, F4, and F5) was traced back for 10 generations, allowing for changes in frequency of individual SNPs over time to be observed, which we call allele frequencies change. Alternative procedures were used to identify SNPs changing in a parallel or nonparallel way across families. For example, markers that have changed the most in the whole population, markers that have changed differently across families, and genes previously identified as those that have changed in allele frequency. The genomic trajectory taken by each family involves selective sweeps, polygenic changes, hitchhiking, and epistasis. The replicate frequency spectrum was used to measure the similarity of change across families and showed that populations have changed differently. The proportion of markers that reversed direction in allele frequency change varied from 0.00 to 0.02 if the rate of change was greater than 0.02 per generation, or from 0.14 to 0.24 if the rate of change was greater than 0.005 per generation within each family. Cluster-specific SNP effects for stature were estimated using only females and applied to obtain indirect genomic predictions for males. Reranking occurs depending on SNP effects used. Additive genetic correlations between clusters show possible differences in populations. Further research is required to determine how this knowledge can be applied to maintain diversity and optimize selection decisions in the future.

Identifying influential sires and distinct clusters of selection candidates based on genomic relationships to reduce inbreeding in the US Holstein.

High relatedness in the US Holstein breed can be attributed to the increased rate of inbreeding that resulted from strong selection and the extensive use of a few bulls via reproductive biotechnology. The objectives of this study were to determine whether clustering could separate selected candidates into genetically different groups and whether such clustering could reduce the expected inbreeding of the next generation. A genomic relationship matrix composed of 1,145 sires with the most registered progeny in the breed born after 1985 was used for principal component analysis and k-means clustering. The 5 clusters reduced the variance by 25% and contained 171 (C1), 252 (C2), 200 (C3), 244 (C4), and 278 (C5) animals, respectively. The 2 most predominant families were C1 and C2, while C4 contained the most international animals. On average, C1 and C5 contained older animals; the average birth year per cluster was 1988 (C1), 1996 (C2 and C3), 1999 (C4), and 1990 (C5). Increasing to 10 clusters allowed the separation of the predominant sons. Statistically significant differences were observed for indices (net merit index, cheese merit index, and fluid merit index), daughter pregnancy rate, and production traits (milk, fat, and protein), with older clusters having lower merit for production but higher for reproduction. K-means clustering was also used for 20,099 animals considered as selection candidates. Based on the reduction in variance achieved by clustering, 5 to 7 clusters were appropriate. The number of animals in each cluster was 3,577 (C1), 3,073 (C2), 3,302 (C3), 5,931 (C4), and 4,216 (C5). The expected inbreeding from within or across cluster mating was calculated using the complete pedigree, assuming the mean inbreeding of animals born in the same year when parents are unknown. Generally, inbreeding was highest within cluster mating and lowest across cluster mating. Even when 10 clusters were used, one cluster always gave low inbreeding in all scenarios. This suggests that this cluster contains animals that differ from all other groups but still contains enough diversity within itself. Based on lower across cluster inbreeding, up to 7 clusters were appropriate. Statistically significant differences in genomic estimated breeding values were found between clusters. The rankings of clusters for different traits were mostly the same except for reproduction and fat. Results show that diversity within the population exists and clustering of selection candidates can reduce the expected inbreeding of the next generations.

Genomic prediction of milk-production traits and somatic cell score using single-step genomic best linear unbiased predictor with random regression test-day model in Thai dairy cattle.

Cow genotypes are expected to improve the accuracy of genomic estimated breeding values (GEBV) for young bulls in relatively small populations such as Thai Holstein-Friesian crossbred dairy cattle in Thailand. The objective of this study was to investigate the effect of cow genotypes on the predictive ability and individual accuracies of GEBV for young dairy bulls in Thailand. Test-day data included milk yield (n = 170,666), milk component traits (fat yield, protein yield, total solids yield, fat percentage, protein percentage, and total solids percentage; n = 160,526), and somatic cell score (n = 82,378) from 23,201, 82,378, and 13,737 (for milk yield, milk component traits, and SCS, respectively) cows calving between 1993 and 2017, respectively. Pedigree information included 51,128; 48,834; and 32,743 animals for milk yield, milk component traits, and somatic cell score, respectively. Additionally, 876, 868, and 632 pedigreed animals (for milk yield, milk component traits, and SCS, respectively) were genotyped (152 bulls and 724 cows), respectively, using Illumina Bovine SNP50 BeadChip. We cut off the data in the last 6 yr, and the validation animals were defined as genotyped bulls with no daughters in the truncated set. We calculated GEBV using a single-step random regression test-day model (SS-RR-TDM), in comparison with estimated breed value (EBV) based on the pedigree-based model used as the official method in Thailand (RR-TDM). Individual accuracies of GEBV were obtained by inverting the coefficient matrix of the mixed model equations, whereas validation accuracies were measured by the Pearson correlation between deregressed EBV from the full data set and (G)EBV predicted with the reduced data set. When only bull genotypes were used, on average, SS-RR-TDM increased individual accuracies by 0.22 and validation accuracies by 0.07, compared with RR-TDM. With cow genotypes, the additional increase was 0.02 for individual accuracies and 0.06 for validation accuracies. The inflation of GEBV tended to be reduced using cow genotypes. Genomic evaluation by SS-RR-TDM is feasible to select young bulls for the longitudinal traits in Thai dairy cattle, and the accuracy of selection is expected to be increased with more genotypes. Genomic selection using the SS-RR-TDM should be implemented in the routine genetic evaluation of the Thai dairy cattle population. The genetic evaluation should consider including genotypes of both sires and cows.

Genomic predictions for yield traits in US Holsteins with unknown parent groups.

The objective of this study was to assess the reliability and bias of estimated breeding values (EBV) from traditional BLUP with unknown parent groups (UPG), genomic EBV (GEBV) from single-step genomic BLUP (ssGBLUP) with UPG for the pedigree relationship matrix (A) only (SS_UPG), and GEBV from ssGBLUP with UPG for both A and the relationship matrix among genotyped animals (A22; SS_UPG2) using 6 large phenotype-pedigree truncated Holstein data sets. The complete data included 80 million records for milk, fat, and protein yields from 31 million cows recorded since 1980. Phenotype-pedigree truncation scenarios included truncation of phenotypes for cows recorded before 1990 and 2000 combined with truncation of pedigree information after 2 or 3 ancestral generations. A total of 861,525 genotyped bulls with progeny and cows with phenotypic records were used in the analyses. Reliability and bias (inflation/deflation) of GEBV were obtained for 2,710 bulls based on deregressed proofs, and on 381,779 cows born after 2014 based on predictivity (adjusted cow phenotypes). The BLUP reliabilities for young bulls varied from 0.29 to 0.30 across traits and were unaffected by data truncation and number of generations in the pedigree. Reliabilities ranged from 0.54 to 0.69 for SS_UPG and were slightly affected by phenotype-pedigree truncation. Reliabilities ranged from 0.69 to 0.73 for SS_UPG2 and were unaffected by phenotype-pedigree truncation. The regression coefficient of bull deregressed proofs on (G)EBV (i.e., GEBV and EBV) ranged from 0.86 to 0.90 for BLUP, from 0.77 to 0.94 for SS_UPG, and was 1.00 ± 0.03 for SS_UPG2. Cow predictivity ranged from 0.22 to 0.28 for BLUP, 0.48 to 0.51 for SS_UPG, and 0.51 to 0.54 for SS_UPG2. The highest cow predictivities for BLUP were obtained with the most extreme truncation, whereas for SS_UPG2, cow predictivities were also unaffected by phenotype-pedigree truncations. The regression coefficient of cow predictivities on (G)EBV was 1.02 ± 0.02 for SS_UPG2 with the most extreme truncation, which indicated the least biased predictions. Computations with the complete data set took 17 h with BLUP, 58 h with SS_UPG, and 23 h with SS_UPG2. The same computations with the most extreme phenotype-pedigree truncation took 7, 36, and 15 h, respectively. The SS_UPG2 converged in fewer rounds than BLUP, whereas SS_UPG took up to twice as many rounds. Thus, the ssGBLUP with UPG assigned to both A and A22 provided accurate and unbiased evaluations, regardless of phenotype-pedigree truncation scenario. Old phenotypes (before 2000 in this data set) did not affect the reliability of predictions for young selection candidates, especially in SS_UPG2.

Mechanism for antibacterial action of epigallocatechin gallate and theaflavin-3,3'-digallate on Clostridium perfringens.

AIM: The purpose of this study was to clarify the mechanism of the antibacterial action of two high potential and natural food additives, epigallocatechin gallate (EGCg) and theaflavin-3,3'-digallate (TF3), on Clostridium perfringens. METHODS AND RESULTS: Minimal inhibitory concentrations were determined by the serial dilution method. Afterwards, the cells were treated with 250 or 1000 mg l-1 of EGCg and 125 or 500 mg l-1 of TF3 and morphological changes were observed and cell sizes were also measured under fluorescence microscopy. Our results showed that TF3 had a twice stronger antibacterial activity than EGCg against C. perfringens. Phase-contrast and fluorescence microscopy confirmed that the bacterial cells elongated without DNA segregation and septum formation in the presence of 250 mg l-1 EGCg. While in the higher concentration of EGCg and TF3, cell growth was suppressed. Bacterial cells reached to around 12 µm after the 24 h incubation with 250 mg l-1 EGCg, but the cells were shorter than the control at 1000 mg l-1 of EGCg. After washing and incubating the elongated cells in fresh medium, DNA segregated at 2 h of incubation. The average cell length decreased gradually and reached the normal size at 8 h. CONCLUSION: It seems that EGCg at a low concentration affected the proteins involved in the septum formation, DNA segregation and cell division. Furthermore, the high concentration of EGCg and TF3 seemed to cause stronger cellular damage to C. perfringens. SIGNIFICANCE AND IMPACT OF THE STUDY: These polyphenols are widely distributed in all higher plants especially in tea plants, and people tend to use natural food additives rather than synthetic ones. EGCg and TF3, as natural food additives, can prevent C. perfringens food poisoning along with other potential health benefits.

Controlling bias in genomic breeding values for young genotyped bulls.

The objectives of this study were to investigate bias in genomic predictions for dairy cattle and to find a practical approach to reduce the bias. The simulated data included phenotypes, pedigrees, and genotypes, mimicking a dairy cattle population (i.e., cows with phenotypes and bulls with no phenotypes) and assuming selection by breeding values or no selection. With the simulated data, genomic estimated breeding values (GEBV) were calculated with a single-step genomic BLUP and compared with true breeding values. Phenotypes and genotypes were simulated in 10 generations and in the last 4 generations, respectively. Phenotypes in the last generation were removed to predict breeding values for those individuals using only genomic and pedigree information. Complete pedigrees and incomplete pedigrees with 50% missing dams were created to construct the pedigree-based relationship matrix with and without inbreeding. With missing dams, unknown parent groups (UPG) were assigned in relationship matrices. Regression coefficients (b1) and coefficients of determination (R2) of true breeding values on (G)EBV were calculated to investigate inflation and accuracy in GEBV for genotyped animals, respectively. In addition to the simulation study, 18 linear type traits of US Holsteins were examined. For the 18 type traits, b1 and R2 of GEBV with full data sets on GEBV with partial data sets for young genotyped bulls were calculated. The results from the simulation study indicated inflation in GEBV for genotyped males that were evaluated with only pedigree and genomic information under BLUP selection. However, when UPG for only pedigree-based relationships were included, the inflation was reduced, accuracy was highest, and genetic trends had no bias. For the linear type traits, when UPG for only pedigree-based relationships were included, the results were generally in agreement with those from the simulation study, implying less bias in genetic trends. However, when including no UPG, UPG in pedigree-based relationships, or UPG in genomic relationships, inflation and accuracy in GEBV were similar. The results from the simulation and type traits suggest that UPG must be defined accurately to be estimable and inbreeding should be included in pedigree-based relationships. In dairy cattle, known pedigree information with inbreeding and estimable UPG plays an important role in improving compatibility between pedigree-based and genomic relationship matrices, resulting in more reliable genomic predictions.

Modeling missing pedigree in single-step genomic BLUP.

The objective was to compare methods of modeling missing pedigree in single-step genomic BLUP (ssGBLUP). Options for modeling missing pedigree included ignoring the missing pedigree, unknown parent groups (UPG) based on A (the numerator relationship matrix) or H (the unified pedigree and genomic relationship matrix), and metafounders. The assumptions for the distribution of estimated breeding values changed with the different models. We simulated data with heritabilities of 0.3 and 0.1 for dairy cattle populations that had more missing pedigrees for animals of lesser genetic merit. Predictions for the youngest generation and UPG solutions were compared with the true values for validation. For both traits, ssGBLUP with metafounders provided accurate and unbiased predictions for young animals while also appropriately accounting for genetic trend. Accuracy was least and bias was greatest for ssGBLUP with UPG for H for the trait with heritability of 0.3 and with UPG for A for the trait with heritability of 0.1. For the trait with heritability of 0.1 and UPG for H, the UPG accuracy (SD) was -0.49 (0.12), suggesting poor estimates of genetic trend despite having little bias for validations on young, genotyped animals. Problems with UPG estimates were likely caused by the lesser amount of information available for the lower heritability trait. Hence, UPG need to be defined differently based on the trait and amount of information. More research is needed to investigate accounting for UPG in A22 to better account for missing pedigrees for genotyped animals.

International bull evaluations by genomic BLUP with a prediction population.

The purpose of this study was to determine whether multi-country genomic evaluation can be accomplished by multiple-trait genomic best linear unbiased predictor (GBLUP) without sharing genotypes of important animals. Phenotypes and genotypes with 40k SNP were simulated for 25,000 animals, each with 4 traits assuming the same genetic variance and 0.8 genetic correlations. The population was split into 4 subpopulations corresponding to 4 countries, one for each trait. Additionally, a prediction population was created from genotyped animals that were not present in the individual countries but were related to each country's population. Genomic estimated breeding values were computed for each country and subsequently converted to SNP effects. Phenotypes were reconstructed for the prediction population based on the SNP effects of a country and the prediction animals' genotypes. The prediction population was used as the basis for the international evaluation, enabling bull comparisons without sharing genotypes and only sharing SNP effects. The computations were such that SNP effects computed within-country or in the prediction population were the same. Genomic estimated breeding values were calculated by single-trait GBLUP for within-country and multiple-trait GBLUP for multi-country predictions. The true accuracy for the prediction population with reconstructed phenotypes was at most 0.02 less than the accuracy with the original data. The differences increased when countries were assumed unequally sized. However, accuracies by multiple-trait GBLUP with the prediction population were always greater than accuracies from any single within-country prediction. Multi-country genomic evaluations by multiple-trait GBLUP are possible without using original genotypes at a cost of lower accuracy compared with explicitly combining countries' data.

Modeling pedigree accuracy and uncertain parentage in single-step genomic evaluations of simulated and US Holstein datasets.

The objective of this study was to model differences in pedigree accuracy caused by selective genotyping. As genotypes are used to correct pedigree errors, some pedigree relationships are more accurate than others. These accuracy differences can be modeled with uncertain parentage models that distribute the paternal (maternal) contribution across multiple sires (dams). In our case, the parents were the parent on record and an unknown parent group to account for pedigree relationships that were not confirmed through genotypes. Pedigree accuracy was addressed through simulation and through North American Holstein data. Data were simulated to be representative of the dairy industry with heterogeneous pedigree depth, pedigree accuracy, and genotyping. Holstein data were obtained from the official evaluation for milk, fat, and protein. Two models were compared: the traditional approach, assuming accurate pedigrees, and uncertain parentage, assuming variable pedigree accuracy. The uncertain parentage model was used to add pedigree relationships for alternative parents when pedigree relationships were not certain. The uncertain parentage model included 2 possible sires (dams) when the sire (dam) could not be confirmed with genotypes. The 2 sires (dams) were the sire (dam) on record with probability 0.90 (0.95) and the unknown parent group for the birth year of the sire (dam) with probability 0.10 (0.05). An additional set of assumptions was tested in simulation to mimic an extensive dairy production system by using a sire probability of 0.75, a dam probability of 0.85, and the remainder attributed to the unknown parent groups. In the simulation, small bias differences occurred between models based on pedigree accuracy and genotype status. Rank correlations were strong between traditional and uncertain parentage models in simulation (≥0.99) and in Holstein (≥0.99). For Holsteins, the estimated breeding value differences between models were small for most animals. Thus, traditional models can continue to be used for dairy genomic prediction despite using genotypes to improve pedigree accuracy. Those genotypes can also be used to discover maternal parentage, specifically maternal grandsires and great grandsires when the dam is not known. More research is needed to understand how to use discovered maternal pedigrees in genetic prediction.

Single-step genome-wide association for longitudinal traits of Canadian Ayrshire, Holstein, and Jersey dairy cattle.

Estimating single nucleotide polymorphism (SNP) effects over time is essential to identify and validate candidate genes (or quantitative trait loci) associated with time-dependent variation of economically important traits and to better understand the underlying mechanisms of lactation biology. Therefore, in this study, we aimed to estimate time-dependent effects of SNP and identifying candidate genes associated with milk (MY), fat (FY), and protein (PY) yields, and somatic cell score (SCS) in the first 3 lactations of Canadian Ayrshire, Holstein, and Jersey breeds, as well as suggest their potential pattern of phenotypic effect over time. Random regression coefficients for the additive direct genetic effect were estimated for each animal using single-step genomic BLUP, based on 2 random regression models: one considering MY, FY, and PY in the first 3 lactations and the other considering SCS in the first 3 lactations. Thereafter, SNP solutions were obtained for random regression coefficients, which were used to estimate the SNP effects over time (from 5 to 305 d in lactation). The top 1% of SNP that showed a high magnitude of SNP effect in at least 1 d in lactation were selected as relevant SNP for further analyses of candidate genes, and clustered according to the trajectory of their SNP effects over time. The majority of SNP selected for MY, FY, and PY increased the magnitude of their effects over time, for all breeds. In contrast, for SCS, most selected SNP decreased the magnitude of their effects over time, especially for the Holstein and Jersey breeds. In general, we identified a different set of candidate genes for each breed, and similar genes were found across different lactations for the same trait in the same breed. For some of the candidate genes, the suggested pattern of phenotypic effect changed among lactations. Among the lactations, candidate genes (and their suggested phenotypic effect over time) identified for the second and third lactations were more similar to each other than for the first lactation. Well-known candidate genes with major effects on milk production traits presented different suggested patterns of phenotypic effect across breeds, traits, and lactations in which they were identified. The candidate genes identified in this study can be used as target genes in studies of gene expression.

Application of single-step genomic evaluation using multiple-trait random regression test-day models in dairy cattle.

Test-day traits are important for genetic evaluation in dairy cattle and are better modeled by multiple-trait random regression models (RRM). The reliability and bias of genomic estimated breeding values (GEBV) predicted using multiple-trait RRM via single-step genomic best linear unbiased prediction (ssGBLUP) were investigated in the 3 major dairy cattle breeds in Canada (i.e., Ayrshire, Holstein, and Jersey). Individual additive genomic random regression coefficients for the test-day traits were predicted using 2 multiple-trait RRM: (1) one for milk, fat, and protein yields in the first, second, and third lactations, and (2) one for somatic cell score in the first, second, and third lactations. The predicted coefficients were used to derive GEBV for each lactation day and, subsequently, the daily GEBV were compared with traditional daily parent averages obtained by BLUP. To ensure compatibility between pedigree and genomic information for genotyped animals, different scaling factors for combining the inverse of genomic (G-1) and pedigree (A-122) relationship matrices were tested. In addition, the inclusion of only genotypes from animals with accurate breeding values (defined in preliminary analysis) was compared with the inclusion of all available genotypes in the analyzes. The ssGBLUP model led to considerably larger validation reliabilities than the BLUP model without genomic information. In general, scaling factors used to combine the G-1 and A-122 matrices had small influence on the validation reliabilities. However, a greater effect was observed in the inflation of GEBV. Less inflated GEBV were obtained by the ssGBLUP compared with the parent average from traditional BLUP when using optimal scaling factors to combine the G-1 and A-122 matrices. Similar results were observed when including either all available genotypes or only genotypes from animals with accurate breeding values. These findings indicate that ssGBLUP using multiple-trait RRM increases reliability and reduces bias of breeding values of young animals when compared with parent average from traditional BLUP in the Canadian Ayrshire, Holstein, and Jersey breeds.

First Observation of P-odd γ Asymmetry in Polarized Neutron Capture on Hydrogen.

We report the first observation of the parity-violating gamma-ray asymmetry A_{γ}^{np} in neutron-proton capture using polarized cold neutrons incident on a liquid parahydrogen target at the Spallation Neutron Source at Oak Ridge National Laboratory. A_{γ}^{np} isolates the ΔI=1, ^{3}S_{1}→^{3}P_{1} component of the weak nucleon-nucleon interaction, which is dominated by pion exchange and can be directly related to a single coupling constant in either the DDH meson exchange model or pionless effective field theory. We measured A_{γ}^{np}=[-3.0±1.4(stat)±0.2(syst)]×10^{-8}, which implies a DDH weak πNN coupling of h_{π}^{1}=[2.6±1.2(stat)±0.2(syst)]×10^{-7} and a pionless EFT constant of C^{^{3}S_{1}→^{3}P_{1}}/C_{0}=[-7.4±3.5(stat)±0.5(syst)]×10^{-11} MeV^{-1}. We describe the experiment, data analysis, systematic uncertainties, and implications of the result.

Analysis of the influence of collagen fibres in the dermis on skin optical reflectance by Monte Carlo simulation in a nine-layered skin model.

BACKGROUND: Collagen fibres in the dermis play an important structural role in the skin. Age-related changes to these fibres cause wrinkles and slackness of facial skin. However, it is not clear how dermal collagen fibres affect skin colour. The purpose of this study was to clarify the influence of altered collagen fibres on skin colour, using both experimental measurement of fibre density and Monte Carlo simulations in an optical model of skin. METHODS: Reflection spectra were measured from the cheeks of 12 Japanese women (22-65 years old) by spectral colorimeter. Two-dimensional autocorrelation functions were calculated from second harmonics generation (SHG) images acquired from the same locations and used to calculate collagen density indices. Monte Carlo simulations of light reflectance by skin were performed using a nine-layered model that precisely imitates skin structure. The relationship between dermal collagen fibre density and skin reflection spectra was analysed. RESULTS: A positive correlation was found between collagen density and skin brightness, as measured by the colour value, L* (using the L*a*b* colour space). In addition, collagen density showed a strong inverse correlation with age and with the optical absorption of dermis. The Monte Carlo simulations showed that the reflection spectrum of skin changes when the scattering coefficient of the dermis is altered. These changes were the same for simulated and experimentally measured reflection spectra. CONCLUSION: When collagen fibre density in the upper dermis is decreased with age, skin colour becomes less bright because light scattering in the skin is decreased.

Mogamulizumab-induced photosensitivity in patients with mycosis fungoides and other T-cell neoplasms.

BACKGROUND: Mogamulizumab (Mog) is a defucosylated, therapeutic monoclonal antibody, targeting CCR4 and was first approved in Japan for the treatment of adult T-cell leukaemia/lymphoma (ATLL), followed by cutaneous T-cell lymphoma and peripheral T-cell lymphoma. OBJECTIVE: To retrospectively investigate development of photosensitivity in patients with mycosis fungoides and other T-cell neoplasms after treatment with Mog. METHODS: We treated seven cutaneous lymphoma patients with Mog. Upon combination treatment with narrow-band UVB, we noticed that four patients developed photosensitivity dermatitis following Mog therapy, including two cases of mycosis fungoides, one case of adult T-cell leukaemia/lymphoma and one case of EB virus-associated T-cell lymphoproliferative disorder. Phototest was performed with UVA and UVB, and immunohistochemical staining for CD4, CD8 and Foxp3 was conducted in both photosensitivity and lymphoma lesions. RESULTS: Phototest revealed that the action spectrum of the photosensitivity was UVB in three cases and both UVB and UVA in one case. Histopathologically, the photosensitive lesions were characterized by a lichenoid tissue reaction with a CD8+ T cell-dominant infiltrate, sharing the feature with chronic actinic dermatitis, an autoreactive photodermatosis with a cytotoxic T-cell response. Foxp3+ regulatory T cells (Tregs) were decreased in the photosensitivity lesions compared with the lymphoma lesions. CONCLUSION: Increased incidence of photosensitivity reaction was observed during Mog treatment. Decreased number of Tregs in the lesional skin suggests that this reaction is possibly induced by autoreactive cytotoxic T cells.

Differing genetic trend estimates from traditional and genomic evaluations of genotyped animals as evidence of preselection bias in US Holsteins.

The objective of this study was to compare genetic trends from single-step genomic BLUP (ssGBLUP) and traditional BLUP models for milk production traits of US Holsteins. Phenotypes were 305-d milk, fat, and protein yields from 21,527,040 cows recorded between January 1990 and August 2015. The pedigree file included 29,651,623 animals and was limited to 3 generations back from recorded or genotyped animals. Genotypes for 764,029 animals were used, and analyses were by a 3-trait repeatability model as used in the US official genetic evaluation. Unknown-parent groups were incorporated into the inverse of a relationship matrix (H-1 in ssGBLUP and A-1 in BLUP) with the QP transformation. For ssGBLUP, 18,359 genotyped animals were randomly chosen as core animals to calculate the inverse of the genomic relationship matrix with the APY algorithm. Computations took 6.5 h and 1.4 GB of memory for BLUP, and 13 h and 115 GB of memory for ssGBLUP. For genotyped sires with at least 10 daughters, the average genetic levels for predicted transmitting ability (PTA) and genomic PTA were similar up to 2008, with a higher level for ssGBLUP later (approximately by 36 kg for milk, 2.1 kg for fat, and 1.1 kg for protein for bulls born in 2010). For genotyped cows, the average genetic levels were similar up to 2006, with a higher level for ssGBLUP (approximately by 91 kg for milk, 3.6 kg for fat, and 2.7 kg for protein for cows born in 2012). For all cows, the average levels were slightly higher for ssGBLUP, with much smaller differences than for genotyped cows. Trends for BLUP indicate bias due to genomic preselection for genotyped sires and cows. For official evaluations released in December 2016, traditional PTA had the same trend as multiple-step genomic PTA for both genotyped bulls and cows except for the youngest bulls, who had traditional PTA slightly lower than genomic PTA. For genotyped bulls born in recent years, genetic gain for official traditional and genomic evaluations was similar in contrast to ssGBLUP and BLUP differences. Official PTA for cows were adjusted so that the Mendelian sampling variance was comparable with that for bulls, and those adjustments likely removed bias due to genomic preselection from traditional PTA, especially for genotyped cows. The ssGBLUP method seems to account partially for that bias and is computationally suitable for national evaluations.

Location, size and age at onset of metamorphosis in the Japanese eel Anguilla japonica.

This study clarifies the location, size and age at the onset of metamorphosis in Japanese eels Anguilla japonica through oceanic surveys, rearing experiments and analyses of the morphology and otoliths of leptocephali and glass eels. Twenty-eight metamorphosing leptocephali were collected in the mesoscale eddy region to the east of Taiwan during research expeditions in 2004. Rearing experiments showed that the total length (LT ) of leptocephali decreased by an average of 12·5% during metamorphosis and 13·9% during the 2-12 h after death. Thus, the mean back-calculated LT at the onset of metamorphosis for 630 glass eels from Taiwan and Japan was estimated at 67·8 ± 2·7 mm (mean ± S.D.). The estimated mean ante-mortem size of the fully grown pre-metamorphic leptocephali collected in 2004 was 64·6 ± 3·4 mm, which was consistent with the LT estimate for glass eels. Otolith analysis showed that the mean age at the onset of metamorphosis was 137 ± 15 days and indicated that Japanese eels may have a recruitment route through the mesoscale eddies to the east of Taiwan in addition to the direct transfer route from the North Equatorial Current to the Kuroshio Current.

Accuracy of breeding values in small genotyped populations using different sources of external information-A simulation study.

Genetically linked small and large dairy cattle populations were simulated to test the effect of different sources of information from foreign populations on the accuracy of predicting breeding values for young animals in a small population. A large dairy cattle population (PL) with >20 generations was simulated, and a small subpopulation (PS) with 3 generations was formed as a related population, including phenotypes and genomic information. Predicted breeding values for young animals in the small population were calculated using BLUP and single-step genomic BLUP (ssGBLUP) in 4 different scenarios: (S1) 3,166 phenotypes, 22,855 pedigree animals, and 1,000 to 6,000 genotypes for PS; (S2) S1 plus genomic estimated breeding value (GEBV) for 4,475 sires from PL as external information; (S3) S1 plus 221,580 phenotypes, 402,829 pedigree animals, and 53,558 genotypes for PL; and (S4) single nucleotide polymorphism (SNP) effects calculated based on PL data. The ability to predict true breeding value was assessed in the youngest third of the genotyped animals in the small population. When data only from the small population were used and 1,000 animals were genotyped, the accuracy of GEBV was only 1 point greater than the estimated breeding value accuracy (0.32 vs. 0.31). Adding external GEBV for sires from PL did not considerably increase accuracy (0.33 vs. 0.32 in S1). Combining phenotypes, pedigree, and genotypes for PS and PL was beneficial for predicting accuracy of GEBV in the small population, and the prediction accuracy of GEBV in this scenario was 0.38 compared with 0.31 from estimated breeding values. When SNP effects from PL were used to predict GEBV for young genotyped animals from PS, accuracy was greatest (0.56). With 6,000 genotyped animal in PS, accuracy was greatest (0.61) with the combined populations. In a small population with few genotypes, the highest accuracy of evaluation may be obtained by using SNP effects derived from a related large population.

The relationship between lip-closing force and dental arch morphology in patient with Angle Class I malocclusion.

Dental arch morphology and tooth position are affected by lip-closing force (LCF). This study aimed to quantitatively evaluate the relationships between the horizontal or vertical balance of the LCF generated during maximum voluntary pursing-like movements and dental arch length (DAL) or width (DAW) or the lingual inclination of the upper or lower 1st molars (LIUM, LILM) in patients with Angle Class I malocclusion. Sixteen subjects with Angle Class I malocclusion (median age: 23·4 ± 5·9 years) who had never undergone orthodontic treatment were randomly selected. LCF was measured in eight directions during maximum voluntary pursing-like lip-closing movements. Dental arch models were scanned and analysed to obtain DAW, DAL, LIUM and LILM measurements. Mandibular deviation was measured on posteroanterior cephalograms. A significant negative correlation was detected between maxillary DAL and upper LCF. Maxillary DAL, DAW and the DAL/DAW ratio displayed significant negative correlations with total LCF and upper LCF. However, no significant correlations were detected between any mandibular dental arch morphological parameter and LCF. The difference in the LIUM between the deviation and non-deviation sides exhibited a significant positive correlation with the difference in upper LCF between the deviation and non-deviation sides and was significantly negatively correlated with the difference in lower LCF between the deviation and non-deviation sides. These results suggest that upper LCF is related to maxillary DAL, and the horizontal balance of the LCF of the upper and lower lips is related to the LIUM during pursing-like lip-closing movements in patients with Angle Class I malocclusion.

Nutrition-adaptive control of multiple-bacteriocin production by Weissella hellenica QU 13.

AIM: To analyse nutrition-adaptive multiple-bacteriocin production by Weissella hellenica QU 13. METHODS AND RESULTS: Weissella hellenica QU 13 produces two leaderless bacteriocins, weissellicins Y and M. Their production was studied in MRS and APT media by quantification analyses with liquid chromatography mass spectrometry (LC/MS), while transcriptional analysis of biosynthetic genes was performed by real-time reverse transcription (RT)-PCR. Weissellicin Y production was higher in MRS culture than in APT culture, while weissellicin M production was higher in APT culture than in MRS culture. APT medium contains a higher amount of thiamine than MRS medium, to enhance the growth of heterofermentative lactic acid bacteria. Therefore, thiamine addition to MRS culture enhanced the growth of W. hellenica QU 13; consequently, weissellicin Y production was decreased, while weissellicin M production was not affected. Furthermore, real-time RT-PCR analyses indicated that the transcriptional trends of their respective structural genes, welY and welM, were different from each other, and that these two genes' transcriptions responded to nutrition conditions. CONCLUSION: Weissella hellenica QU 13 was demonstrated to control weissellicins Y and M production based on nutrition conditions. In addition, differential expression behaviour of weissellicins Y and M indicates that each of them would have separate roles to adapt to different environmental situations. SIGNIFICANCE AND IMPACT OF THE STUDY: This is the first report that describes nutrition-adaptive multiple-bacteriocin production, in which thiamine inhibits bacteriocin production while it enhances the growth of the producer strain.

A new quantitative evaluation method for age-related changes of individual pigmented spots in facial skin.

BACKGROUND: Facial skin pigmentation is one of the most prominent visible features of skin aging and often affects perception of health and beauty. To date, facial pigmentation has been evaluated using various image analysis methods developed for the cosmetic and esthetic fields. However, existing methods cannot provide precise information on pigmented spots, such as variations in size, color shade, and distribution pattern. The purpose of this study is the development of image evaluation methods to analyze individual pigmented spots and acquire detailed information on their age-related changes. METHODS: To characterize the individual pigmented spots within a cheek image, we established a simple object-counting algorithm. First, we captured cheek images using an original imaging system equipped with an illumination unit and a high-resolution digital camera. The acquired images were converted into melanin concentration images using compensation formulae. Next, the melanin images were converted into binary images. The binary images were then subjected to noise reduction. Finally, we calculated parameters such as the melanin concentration, quantity, and size of individual pigmented spots using a connected-components labeling algorithm, which assigns a unique label to each separate group of connected pixels. RESULTS: The cheek image analysis was evaluated on 643 female Japanese subjects. We confirmed that the proposed method was sufficiently sensitive to measure the melanin concentration, and the numbers and sizes of individual pigmented spots through manual evaluation of the cheek images. The image analysis results for the 643 Japanese women indicated clear relationships between age and the changes in the pigmented spots. CONCLUSION: We developed a new quantitative evaluation method for individual pigmented spots in facial skin. This method facilitates the analysis of the characteristics of various pigmented facial spots and is directly applicable to the fields of dermatology, pharmacology, and esthetic cosmetology.