Genomic analysis of genotype-environment interaction in age at first calving of Murrah buffaloes.

Age at first calving (AFC) is a measure of sexual maturity associated with the start of productive life of dairy animals. Additionally, a lower AFC reduces the generation interval and early culling of females. However, AFC has low heritability, making it a trait highly influenced by environmental factors. In this scenario, one way to improve the reproductive performance of buffalo cows is to select robust animals according to estimated breeding value (EBV) using models that include genotype-environment interaction (GEI) with the application of reaction norm models (RNMs). This can be achieved by understanding the genomic basis related to GEI of AFC. Thus, in this study, we aimed to predict EBV considering GEI via the RNM and identify candidate genes related to this component in dairy buffaloes through genome-wide association studies (GWAS). We used 1795 AFC records from three Murrah buffalo herds and formed environmental gradients (EGs) from contemporary group solutions obtained from genetic analysis of 270-day cumulative milk yield. Heritability estimates ranged from 0.15 to 0.39 along the EG. GWAS of the RNM slope parameter identified important genomic regions. The genomic window that explained the highest percentage of genetic variance of the slope (0.67%) was located on BBU1. After functional analysis, five candidate genes were detected, involved in two biological processes. The results suggested the existence of a GEI for AFC in Murrah buffaloes, with reclassification of animals when different environmental conditions were considered. The inclusion of genomic information increased the accuracy of breeding values for the intercept and slope of the reaction norm. GWAS analysis suggested that important genes associated with the AFC reaction norm slope were possibly also involved in biological processes related to lipid metabolism and immunity.

Screening Iranian endemic coriander genebank germplasm for drought tolerance using a new multivariate statistical method.

Drought is one of the serious abiotic factors influencing crop production such as coriander. Development of tolerant genotypes is prevented by the lack of effective selection criterion. Objectives of this study were evaluation of coriander accessions for water deficit stress and introduce a new multivariate method to select drought tolerant genotypes. For investigation of 19 traits, 16 Iranian endemic coriander genotypes were grown in a glasshouse under control and water deficit stress conditions. Shoot dry weight (SDW), fruit weight per plant (FWPP), fruit number per plant (FNPP) and umbel number per plant (UNPP) were decreased (Susceptibility Index>38%) under water deficit stress condition compared with the control condition. While the mean values of root dry weight (RDW) and root to shoot ratio (RTSR) were increased 1.49% and 97.33% under water stress condition, respectively. Because of high inheritance, high expected genetic gain, high genotypic correlation with together, well response to drought stress and high explanation of FWPP variation in regression model, the FWPP, branch number per plant (BNPP), FNPP and SPAD chlorophyll content in grain filing stage (SCCIGFS) traits were selected to screen coriander genotypes for drought tolerance in coriander. The principal component analysis mediated method (PCAMM) indicated as comprehensive criterion to screen drought tolerant genotypes. This method was highly heritabl, able to separate the Fernandez described A, B, C and D groups, no multicollinear and using multiple drought tolerance related traits. The PCAMM results showed that G13, G16, G2 and G12 genotypes belonged to Fernandez described A, B, C and D groups, respectively.

Evaluation of models for estimation of genetic parameters for post-weaning body measurements and their association with yearling weight in Nellore sheep.

OBJECTIVE: The objective of this study was to obtain (co) variance components and genetic parameter estimates for post-weaning body measurements such as body length (BL), height at withers (HW), and chest girth (HG) recorded at six (SBL, SHW, and SHG), nine (NBL, NHW, and NHG) and twelve (YBL, YHW, and YHG) months of age along with yearling weight (YW) in Nellore sheep maintained at livestock research station, Palamaner, Andhra Pradesh, India and also the association among body measurements with YW was studied. METHODS: Data on 2,076 Nellore sheep (descended from 75 sires and 522 dams) recorded between 2007 and 2016 (10 years) were utilized in the study. Lambing year, sex of lamb, season of lambing and parity of dam were included in the model as fixed effects and ewe weight was kept as a covariate. Analyses were conducted with six animal models with different combinations of direct and maternal genetic effects using restricted maximum likelihood procedure. Best model for each trait was determined based on Akaike's information criterion. RESULTS: Moderate estimates of direct heritability were obtained for the studied traits viz., BL (0.02 to 0.24), HW (0.31 to 0.49), and CG (0.08 to 0.35) and their corresponding maternal heritability estimates were in the range of 0.00 to 0.07 (BL), 0.13 to 0.17 (HW), and 0.07 to 0.13 (CG), respectively. Positive direct genetic and phenotypic correlations among the traits and they ranged from 0.07 (YBL-YW) to 0.99 (SBL-SHG, SHG-YW, and NBL-YBL) and 0.01 (SBL-YBL) to 0.99 (NBL-NHG), respectively. Further, the genetic correlations among all the body measurements and YW were positive and ranged from 0.07 (YW and YBL) to 0.99 (YW and SHG). CONCLUSION: There was a strong association of chest girth at six months with YW. Further, it is indicated that moderate improvement of post-weaning body measurements in Nellore sheep would be possible through selection.

Enhancing radiological monitoring of 137Cs in coastal environments using taxonomic signals in brown seaweeds.

With the rapidly expanding global nuclear industry, more efficient and direct radiological monitoring approaches are needed to ensure the associated environmental health impacts and risk remain fully assessed and undertaken as robustly as possible. Conventionally, radiological monitoring in the environment consists of measuring a wide range of anthropogenically enhanced radionuclides present in selected environmental matrices and using generic transfer values for modelling and prediction that are not necessarily suitable in some situations. Previous studies have found links between taxonomy and radionuclide uptake in terrestrial plants and freshwater fish, but the marine context remains relatively unexplored. This preliminary study was aimed at investigating a similar relationship between brown seaweed, an important indicator in radiological monitoring programmes in the marine environment, and Caesium-137, an important radionuclide discharged to the marine environment. A linear mixed model was fitted using REsidual Maximum Likelihood (REML) to activity concentration data collected from literature published worldwide and other databases. The output from REML modelling was adjusted to the International Atomic Energy Agency (IAEA) quoted transfer value for all seaweed taxa in order to produce mean estimate transfer value for each species, which were then analysed by hierarchical ANalysis Of VAriance (ANOVA) based on the taxonomy of brown seaweeds. Transfer value was found to vary between taxa with increasing significance up the taxonomic hierarchy, suggesting a link to evolutionary history. This novel approach enables contextualisation of activity concentration measurements of important marine indicator species in relation to the wider community, allows prediction of unknown transfer values without the need to sample specific species and could, therefore, enhance radiological monitoring by providing accurate, taxon specific transfer values for use in dose assessments and models of radionuclide transfer in the environment.

Genetic analysis of growth curve in Moghani Sheep using Bayesian and restricted maximum likelihood.

This study was conducted to predict the genetic (co)variance components of growth curve parameters of Moghani sheep breed using the following information: birth weight (N = 7278), 3-mo-old weight (N = 5881), 6-mo-old weight (N = 5013), 9-mo-old weigh (N = 2819], and 12-mo-old weight (N = 2883). The growth parameters (A: maturity weight, B: growth rate, and K: maturity rate) were calculated using Gompertz, Logistic, Brody, and Von Bertalanffy nonlinear models via NLIN procedure of SAS software. The aforementioned models were compared using Akaike information criterion, root mean square error, adjusted co-efficient of determination. Also, both Bayesian (using MTGSAM) and RMEL (using WOMBAT) paradigms were adapted to predict the genetic (co)variance components of growth parameters (A, B, K) due to the best fitted growth models. It was turned out that Von Bertalanffy best fitted to the data in this study. The year of birth and lamb gender had a significant effect on maturity rate (P < 0.01). Also it turned out that within the growth parameter, with increasing (co)variance matrix complexity, the Bayesian paradigm fitted well to the data than the restricted maximum likelihood (REML) one. However, for simple animal model and across all growth parameters, REML outperformed Bayesian. In this way, the h2a predicted (0.15 ± 0.05), (0.11±.05), and (0.04 ± 0.03) for A, B, and K parameters, respectively. Practically, in terms of breeding plan, we could see that genetic improvement of growth parameters in this study is not a tractable strategy to follow up and improvement of the management and environment should be thoroughly considered. In terms of paradigm comparison, REML's bias correction bears up an advantageous approach as far as we are concerned with small sample size. To this end, REML predictions are fairly accurate but the mode of posterior distributions could be overestimated. Finally, the differences between REML and Bayesian estimates were found for all parameter data in this study. We conclude that simulation studies are necessary in order to trade off these parading in the complex random effects scenarios of genetic individual model.

The Iran plateau is known to be the origin of many sheep species nowadays. In Iran, different production systems are operated ranging from intensive to lower-input/extensive ones. However, the majority of these sheep breeds are extensively managed, where lambs are born outside and with little intervention and generally they experience frequent drought and shortage of nutritional value of forages. Meanwhile, the weight of lambs as a whole play a major decision role in rearing or culling them. However, investigations involving the possible genetic improvement of lamb weights over different periods of time have found low genetic variations. This study serves to be comprehensive in addressing this issue in Moghani sheep breed. Fitting many different genetics models over both restricted maximum likelihood and Bayesian paradigms indicated that heritability of weight spanned 0.03 to 0.23. The low genetic variation would lead to recommendations that the improvement of Moghani lamb weights should rather be based upon modification of the environment to create conditions suitable for weight of lambs. This reflects that breeders of Moghani sheep breed have less options to tackle Iran harsh conditions using Moghani sheep genetic potentials.

Identification of Superior Barley Genotypes Using Selection Index of Ideal Genotype (SIIG).

The main objective of the study was to evaluate and select the superior barley genotypes based on grain yield and some pheno-morphological traits using a new proposed selection index (SIIG). For this purpose, one-hundred-eight pure and four local cultivars (Norouz, Auxin, Nobahar, and WB-97-11) were evaluated as reference genotypes in four warm regions of Iran, including Ahvaz, Darab, Zabol, and Gonbad, during the 2020-2021 cropping seasons. The results of REML analysis showed that the heritability of all traits (except plant height) was higher in Gonbad than in other environments, while the lowest values were estimated in Ahvaz and Zabol environments. In addition, among the measured traits, the thousand kernel weight and grain filling period showed the highest and lowest values of heritability (0.83 and 0.01, respectively). The results showed that the seed yield of genotypes 1, 108, 3, 86, 5, 87, 19, 16, 15, 56, and 18 was higher than the four reference genotypes, and, on the other hand, the SIIG index of these genotypes was greater than or equal to 0.60. Based on the SIIG discriminator index, 4, 8, 31, and 28 genotypes with values greater than or equal to 0.60 were identified as superior for Darab, Ahvaz, Zabol, and Gonbad environments, respectively. As a conclusion, our results revealed that the SIIG index has ideal potential to identify genotypes with high yield and desirable traits. Therefore, the use of this index can be beneficial in screening better genotypes in the early stages of any breeding program for any crop.

Estimating population size: The importance of model and estimator choice.

We consider estimator and model choice when estimating abundance from capture-recapture data. Our work is motivated by a mark-recapture distance sampling example, where model and estimator choice led to unexpectedly large disparities in the estimates. To understand these differences, we look at three estimation strategies (maximum likelihood estimation, conditional maximum likelihood estimation, and Bayesian estimation) for both binomial and Poisson models. We show that assuming the data have a binomial or multinomial distribution introduces implicit and unnoticed assumptions that are not addressed when fitting with maximum likelihood estimation. This can have an important effect in finite samples, particularly if our data arise from multiple populations. We relate these results to those of restricted maximum likelihood in linear mixed effects models.

A comparison of design-based and model-based approaches for finite population spatial sampling and inference.

The design-based and model-based approaches to frequentist statistical inference rest on fundamentally different foundations. In the design-based approach, inference relies on random sampling. In the model-based approach, inference relies on distributional assumptions. We compare the approaches in a finite population spatial context.We provide relevant background for the design-based and model-based approaches and then study their performance using simulated data and real data. The real data is from the United States Environmental Protection Agency's 2012 National Lakes Assessment. A variety of sample sizes, location layouts, dependence structures, and response types are considered. The population mean is the parameter of interest, and performance is measured using statistics like bias, squared error, and interval coverage.When studying the simulated and real data, we found that regardless of the strength of spatial dependence in the data, the generalized random tessellation stratified (GRTS) algorithm, which explicitly incorporates spatial locations into sampling, tends to outperform the simple random sampling (SRS) algorithm, which does not explicitly incorporate spatial locations into sampling. We also found that model-based inference tends to outperform design-based inference, even for skewed data where the model-based distributional assumptions are violated. The performance gap between design-based inference and model-based inference is small when GRTS samples are used but large when SRS samples are used, suggesting that the sampling choice (whether to use GRTS or SRS) is most important when performing design-based inference.There are many benefits and drawbacks to the design-based and model-based approaches for finite population spatial sampling and inference that practitioners must consider when choosing between them. We provide relevant background contextualizing each approach and study their properties in a variety of scenarios, making recommendations for use based on the practitioner's goals.

Evaluation of internal margins for prostate for step and shoot intensity-modulated radiation therapy and volumetric modulated arc therapy using different margin formulas.

PURPOSE: This feasibility study evaluated the intra-fractional prostate motion using an ultrasound image-guided system during step and shoot intensity-modulated radiation therapy (SS-IMRT) and volumetric modulated arc therapy (VMAT). Moreover, the internal margins (IMs) using different margin formulas were calculated. METHODS: Fourteen consecutive patients with prostate cancer who underwent SS-IMRT (n = 5) or VMAT (n = 9) between March 2019 and April 2020 were considered. The intra-fractional prostate motion was observed in the superior-inferior (SI), anterior-posterior (AP), and left-right (LR) directions. The displacement of the prostate was defined as the displacement from the initial position at the scanning start time, which was evaluated using the mean ± standard deviation (SD). IMs were calculated using the van Herk and restricted maximum likelihood (REML) formulas for SS-IMRT and VMAT. RESULTS: For SS-IMRT, the maximum displacements of the prostate motion were 0.17 ± 0.18, 0.56 ± 0.86, and 0.18 ± 0.59 mm in the SI, AP, and LR directions, respectively. For VMAT, the maximum displacements of the prostate motion were 0.19 ± 0.64, 0.22 ± 0.35, and 0.14 ± 0.37 mm in the SI, AP, and LR directions, respectively. The IMs obtained for SS-IMRT and VMAT were within 2.3 mm and 1.2 mm using the van Herk formula and within 1.2 mm and 0.8 mm using the REML formula. CONCLUSIONS: This feasibility study confirmed that intra-fractional prostate motion was observed with SS-IMRT and VMAT using different margin formulas. The IMs should be determined according to each irradiation technique using the REML margin.

Genetic parameters and selection in full-sib families of tall fescue using best linear unbiased prediction (BLUP) analysis.

BACKGROUND: Better understanding of genetic structure of economic traits is crucial for identification and selection of superior genotypes in specific breeding programs. Best linear unbiased prediction (BLUP) is the most efficient method in this regard, which is poorly used in forage plant breeding. The present study aimed to assess genetic variation, estimate genetic parameters, and predict breeding values of five essential traits in full sib families (recognized by EST-SSR markers) of tall fescue using REML/BLUP procedure. METHOD: Forty-two full-sib families of tall fescue (included of 120 individual genotypes), recognized by EST-SSR markers along with twenty-one their corresponding parental genotypes were assessed for biomass production and agro-morphological traits at three harvests (spring, summer, and autumn) in the field during 4 years (2017-2020). RESULTS: Considerable genotypic variability was observed for all traits. Low narrow-sense heritability (h2n) for dry forage yield (DFY) at three harvest indicates that non-additive gene actions may play an important role in the inheritance of this trait. Higher h2n of yield related traits and flowering time and also significant genetic correlation of these traits with forage yield, suggests that selection based on these traits may lead to indirect genetic improvement of DFY. CONCLUSION: Our results showed the adequacy of REML/BLUP procedure for identification and selection of preferable parental genotypes and progenies with higher breeding values for future breeding programs such as variety development in tall fescue. Parental genotypes 21 M, 1 M, and 20 L were identified as superior and stable genotypes and could also produce the best hybrid combinations when they were mostly used as maternal parent.

Overview of Major Computer Packages for Genomic Prediction of Complex Traits.

Genomic prediction models are showing their power to increase the rate of genetic gain by boosting all the elements of the breeder's equation. Insight into the factors associated with the successful implementation of this prediction model is increasing with time but the technology has reached a stage of acceptance. Most genomic prediction models require specialized computer packages based mainly on linear models and related methods. The number of computer packages has exploded in recent years given the interest in this technology. In this chapter, we explore the main computer packages available to fit these models; we also review the special features, strengths, and weaknesses of the methods behind the most popular computer packages.

Inclusion of sire by herd interaction effect in the genomic evaluation for weaning weight of American Angus.

A spurious negative genetic correlation between direct and maternal effects of weaning weight (WW) in beef cattle has historically been problematic for researchers and industry. Previous research has suggested the covariance between sires and herds may be contributing to this relationship. The objective of this study was to estimate the variance components (VC) for WW in American Angus with and without sire by herd (S×H) interaction effect when genomic information is used or not. Five subsets of ~100k animals for each subset were used. When genomic information was included, genotypes were added for 15,637 animals. Five replicates were performed. Four different models were tested, namely, M1: without S×H interaction effect and with covariance between direct and maternal effect (σam) ≠ 0; M2: with S×H interaction effect and σam ≠ 0; M3: without S×H interaction effect and with σam = 0; M4: with S×H interaction effect and σam = 0. VC were estimated using the restricted maximum likelihood (REML) and single-step genomic REML (ssGREML) with the average information algorithm. Breeding values were computed using single-step genomic BLUP for the models above and one additional model, which had the covariance zeroed after the estimation of VC (M5). The ability of each model to predict future breeding values was investigated with the linear regression method. Under REML, when the S×H interaction effect was added to the model, both direct and maternal genetic variances were greatly reduced, and the negative covariance became positive (i.e., when moving from M1 to M2). Similar patterns were observed under ssGREML, but with less reduction in the direct and maternal genetic variances and still a negative covariance. Models with the S×H interaction effect (M2 and M4) had a better fit according to the Akaike information criteria. Breeding values from those models were more accurate and had less bias than the other three models. The rankings and breeding values of artificial insemination sires (N = 1,977) greatly changed when the S×H interaction effect was fit in the model. Although the S×H interaction effect accounted for 3% to 5% of the total phenotypic variance and improved the model fit, this change in the evaluation model will cause severe reranking among animals.

A spurious negative genetic correlation between direct and maternal effects of weaning weight (WW) in beef cattle has been problematic for researchers and industry. Previous research suggested the covariance between sires and herds may contribute to this relationship. The objective of this study was to estimate the variance components (VC) for WW in American Angus with and without sire by herd (S×H) interaction effect when genomic information is used or not. Four models were designed to investigate the S×H effect. The restricted maximum likelihood (REML) and single-step genomic REML (ssGREML) were used to estimate VC. Breeding values were computed using single-step genomic BLUP and the validation was done through the linear regression method. Under REML, when the S×H was added to the model, both direct and maternal genetic variances were greatly reduced, and the negative covariance became positive. Similar patterns were observed under ssGREML, but with less reduction in the direct and maternal genetic variances and still a negative covariance. Breeding values from models with S×H were more accurate and had less bias than the other models. Although the S×H improved the model, this change in the evaluation model will cause severe reranking among key animals.

Genomic Selection for Milk Production Traits in Xinjiang Brown Cattle.

One-step genomic selection is a method for improving the reliability of the breeding value estimation. This study aimed to compare the reliability of pedigree-based best linear unbiased prediction (PBLUP) and single-step genomic best linear unbiased prediction (ssGBLUP), single-trait and multitrait models, and the restricted maximum likelihood (REML) and Bayesian methods. Data were collected from the production performance records of 2207 Xinjiang Brown cattle in Xinjiang from 1983 to 2018. A cross test was designed to calculate the genetic parameters and reliability of the breeding value of 305 daily milk yield (305 dMY), milk fat yield (MFY), milk protein yield (MPY), and somatic cell score (SCS) of Xinjiang Brown cattle. The heritability of 305 dMY, MFY, MPY, and SCS estimated using the REML and Bayesian multitrait models was approximately 0.39 (0.02), 0.40 (0.03), 0.49 (0.02), and 0.07 (0.02), respectively. The heritability and estimated breeding value (EBV) and the reliability of milk production traits of these cattle calculated based on PBLUP and ssGBLUP using the multitrait model REML and Bayesian methods were higher than those of the single-trait model REML method; the ssGBLUP method was significantly better than the PBLUP method. The reliability of the estimated breeding value can be improved from 0.9% to 3.6%, and the reliability of the genomic estimated breeding value (GEBV) for the genotyped population can reach 83%. Therefore, the genetic evaluation of the multitrait model is better than that of the single-trait model. Thus, genomic selection can be applied to small population varieties such as Xinjiang Brown cattle, in improving the reliability of the genomic estimated breeding value.

Influence of model selection and data structure on the estimation of genetic parameters in honeybee populations.

Estimating genetic parameters of quantitative traits is a prerequisite for animal breeding. In honeybees, the genetic variance separates into queen and worker effects. However, under data paucity, parameter estimations that account for this peculiarity often yield implausible results. Consequently, simplified models that attribute all genetic contributions to either the queen (queen model) or the workers (worker model) are often used to estimate variance components in honeybees. However, the causes for estimations with the complete model (colony model) to fail and the consequences of simplified models for variance estimates are little understood. We newly developed the necessary theory to compare parameter estimates that were achieved by the colony model with those of the queen and worker models. Furthermore, we performed computer simulations to quantify the influence of model choice, estimation algorithm, true genetic parameters, rates of controlled mating, apiary sizes, and phenotype data completeness on the success of genetic parameter estimations. We found that successful estimations with the colony model were only possible if at least some of the queens mated controlled on mating stations. In that case, estimates were largely unbiased if more than 20% of the colonies had phenotype records. The simplified queen and worker models proved more stable and yielded plausible parameter estimates for almost all settings. Results obtained from these models were unbiased when mating was uncontrolled, but with controlled mating, the simplified models consistently overestimated heritabilities. This study elucidates the requirements for variance component estimation in honeybees and provides the theoretical groundwork for simplified honeybee models.

Assessing Heterogeneity in Random-Effects Meta-analysis.

The random-effects model allows for the possibility that studies in a meta-analysis have heterogeneous effects. That is, observed study estimates vary not only due to random sampling error but also due to inherent differences in the way studies have been designed and conducted. In this chapter, we consider methods to estimate the heterogeneity variance parameter in a random-effects model, consider in more detail what this parameter represents and how the possible explanations for heterogeneity can be explored through statistical methods. Toward the end of this chapter, publication bias is discussed as an alternative explanation for why observed effect estimates might form some distribution other than what we might come to expect.

Model Selection and Model Averaging for Mixed-Effects Models with Crossed Random Effects for Subjects and Items.

A good deal of experimental research is characterized by the presence of random effects on subjects and items. A standard modeling approach that includes such sources of variability is the mixed-effects models (MEMs) with crossed random effects. However, under-parameterizing or over-parameterizing the random structure of MEMs bias the estimations of the Standard Errors (SEs) of fixed effects. In this simulation study, we examined two different but complementary perspectives: model selection with likelihood-ratio tests, AIC, and BIC; and model averaging with Akaike weights. Results showed that true model selection was constant across the different strategies examined (including ML and REML estimators). However, sample size and variance of random slopes were found to explain true model selection and SE bias of fixed effects. No relevant differences in SE bias were found for model selection and model averaging. Sample size and variance of random slopes interacted with the estimator to explain SE bias. Only the within-subjects effect showed significant underestimation of SEs with smaller number of items and larger item random slopes. SE bias was higher for ML than REML, but the variability of SE bias was the opposite. Such variability can be translated into high rates of unacceptable bias in many replications.

Internet-based cognitive behavioral therapy for psychological distress in older adults without cognitive impairment living in nursing homes during the COVID-19 pandemic: A feasibility study.

BACKGROUND: The COVID-19 pandemic has had a detrimental effect on the mental health of older adults living in nursing homes. Very few studies have examined the effects of Internet-based Cognitive Behavioral Therapy (ICBT) on older adults living in nursing homes during the pandemic. We conducted a feasibility study using a single-group design, to explore the effectiveness of ICBT on psychological distress in 137 older adults (without cognitive impairment) from 8 nursing homes in 4 southeast cities in China, between January and March 2020. METHODS: Symptoms of depression, anxiety, general psychological distress, and functional disability were measured at baseline, post-treatment (5 weeks) and at a 1-month follow-up. Mixed-effects model was used to assess the effects of ICBT. RESULTS: Statistically significant changes with large effect sizes were observed from pre- to post-treatment on the PHQ-9 (p < .001, Cohen's d = 1.74), GAD-7 (p < .001, d = 1.71), GDS (p < .001, d = 1.30), K-10 (p < .001, d = 1.93), and SDS (p < .001, d = 2.03). Furthermore, improvements in treatment outcomes were sustained at 1-month follow-up, and high levels of adherence and satisfaction were indicated. CONCLUSION: ICBT was effective in reducing psychological distress in older adults without cognitive impairments living in nursing homes during the COVID-19 pandemic. Thus, it could be applied in improving the mental health of this vulnerable group during the pandemic.

Changes in genetic parameters for milk yield and heat tolerance in the Thai Holstein crossbred dairy population under different heat stress levels and over time.

The objectives of this study were to investigate changes in genetic parameters for milk yield (MY) and heat tolerance of the crossbred Thai Holstein Friesian population under different heat stress levels over time, and to investigate the threshold point of heat stress manifestation on milk production. Genetic parameters were estimated using single-step genomic REML (ssGREML) and traditional REML models. Data included 58,965 test-day MY records from 1999 to 2008 (old data) and 105,485 test-day MY records from 2009 to 2018 (recent data) from the first parity of 24,520 cows. The pedigree included 55,168 animals, of which 882 animals had genotypes. Variance components were estimated with the REMLF90 program using a repeatability model with random regressions on a function of temperature-humidity index (THI) for additive genetic and permanent environmental effects. Fixed effects included farm-calving season combination, breed group-months in milk combination, and age at first calving. Random effects included additive genetic (intercept and slope) effects, permanent environmental (intercept and slope) effects, and herd-month-year of test. The phenotypic mean for MY was 13.33 ± 4.39 kg/d in the old data, and 14.48 ± 4.40 kg/d in the recent data. Estimates over different THI levels for the intercept additive genetic variance using old data ranged from 2.61 to 2.77 and from 5.02 to 5.38 using recent data with the REML method. In ssGREML analyses (performed with recent data only) the estimates for the intercept additive genetic variance ranged from 4.71 to 5.05. Estimates for the slope additive genetic variance were close to zero in all cases, with the largest values (0.024-0.030) at the most extreme THI value (80). Using REML, the covariance between the intercept and the slope additive genetic effects (THI from 72 to 80) ranged from -0.001 to 0.019 with old data and from 0.027 to 0.060 with recent data. The same covariance ranged from 0.026 to 0.057 in ssGREML analyses. The covariance between the intercept and the slope permanent environmental effects ranged from -0.42 to -0.67 for all data and THI levels. Across THI levels, the genetic correlation between MY and heat tolerance varied from -0.06 to 0.13 with old data, from 0.16 to 0.30 with recent data in REML analyses, and from 0.15 to 0.30 in ssGREML analyses, suggesting that in the current population the top animals for MY are more resistant to heat stress. This was expected, because of the introduction of Bos indicus genes in the last years. Heritability estimates for MY ranged from 0.19 to 0.21 (old data) and from 0.33 to 0.40 (recent data) for REML analyses. Heritability estimates for MY using ssGREML ranged from 0.31 to 0.38. A decline in MY was found when the animals' breed composition had more than 97.3% of Holstein genetics, and it was greatest at THI 80. The heritability and genetic correlations observed in this study show that selection for MY is possible without a negative correlated response for heat tolerance. Although the inclusion of genomic information is expected to increase the accuracy of selection, more genotypes must be collected for successful application. Future research should address other production and fitness traits within the Thai Holstein population.

Is Brief Exposure to Green Space in School the Best Option to Improve Attention in Children?

The positive effects of Green Spaces on health are thought to be achieved through the mechanisms of mitigation, instoration and restoration. One of the benefits of Green Spaces may be the restoration of attention and so the objective of this research is testing empirically whether exposure to a green environment improves attention in school children. For so doing, we first used a split-unit statistical design in each of four schools, then combined the primary results via meta-analysis. The Attention Network Test (ANT) was used to measure attention before and after exposure and a total of 167 seven-year-old students participated in the experiments. Overall, our experimental results do not support the hypothesis that students' exposure to activities in green vs. grey spaces affected their performance in ANT. This was so despite the fact that neither age nor gender biases have been detected and despite that our experiments have been proved to be sufficiently statistically powerful. It would be advisable to consider air pollution and noise. We also recommend that participants attend the experiment with mental exhaustion to maximize the ability to detect significant changes.

Mitigation of the convergence issues associated with semi-replicated bioequivalence data.

Semi-replicated designs for investigation of bioequivalence constitute a challenge when mixed models are applied. With the commonly available packages and regardless of choice of covariance structure the software may force variance components into the covariance matrix that render it over-specified. This may give rise to arbitrary estimates of certain variance components, lack of convergence or warnings. Classically the covariance matrix is decomposed as V = ZGZt  + R, with G containing the between-subject variance components, Z being the design matrix for the random effects and R containing the within-subject variance components. By abandoning the definitions of G and R, and instead working directly in V, it is possible to specify a correct model with only the variance components of interest. Proof-of-concept for this idea is delivered with a script in the statistical language R. The script is available as supplementary material (Data S1).