Fusarium head blight (FHB) is a disease in wheat (Triticum aestivum L.) caused by the fungal pathogen Fusarium graminearum Schwabe. Fusarium head blight poses potential economic losses and health risks due to the accumulation of the mycotoxin deoxynivalenol (DON) on infected seed heads. The objectives of this study were to identify novel FHB resistance loci using a genome‐wide association study (GWAS) approach and to evaluate two genomic selection (GS) approaches to improve prediction accuracies for FHB traits in a population of 354 soft red winter wheat (SRWW) genotypes. The GS approaches included GS+GWAS, where markers associated with a trait were used as fixed effects, and multivariate GS (MVGS), where correlated traits were used as covariates. The population was evaluated in FHB nurseries in Fayetteville and Newport, AR, and Winnsboro, LA, from 2014 to 2017. Genotypes were phenotyped for DON, Fusarium‐damaged kernels (FDK), incidence (INC), and severity (SEV). Forty‐two single nucleotide polymorphism (SNP) markers were significantly (false discovery rate, q [FDRq] ≤ .10) associated with resistance traits across 17 chromosomes. Ten significant SNPs were identified for DON, notably on chromosomes 2BL and 3BL. Eleven were identified for FDK, notably on chromosomes 4BL, 3AL, 1BL, 5BL, and 5DL. Nine were identified for INC, notably on chromosomes 2BS, 2BL, 7BL, 5DL, 6AS, and 5DS. Twelve were identified for SEV, notably on chromosomes 3BL, 4AL, and 4BL. The naïve GS models significantly outperformed the GS+GWAS model for all traits, whereas MVGS models significantly outperformed the naïve GS models for all traits. Results from this study will facilitate the development of SRWW cultivars with improved FHB resistance.

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Many studies have evaluated the effectiveness of genomic selection (GS) using cross-validation within training populations; however, few have looked at its performance for forward prediction within a breeding program. The objectives for this study were to compare the performance of naïve GS (NGS) models without covariates and multi-trait GS (MTGS) models by predicting two years of F4:7 advanced breeding lines for three Fusarium head blight (FHB) resistance traits, deoxynivalenol (DON) accumulation, Fusarium damaged kernels (FDK), and severity (SEV) in soft red winter wheat and comparing predictions with phenotypic performance over two years of selection based on selection accuracy and response to selection. On average, for DON, the NGS model correctly selected 69.2% of elite genotypes, while the MTGS model correctly selected 70.1% of elite genotypes compared with 33.0% based on phenotypic selection from the advanced generation. During the 2018 breeding cycle, GS models had the greatest response to selection for DON, FDK, and SEV compared with phenotypic selection. The MTGS model performed better than NGS during the 2019 breeding cycle for all three traits, whereas NGS outperformed MTGS during the 2018 breeding cycle for all traits except for SEV. Overall, GS models were comparable, if not better than phenotypic selection for FHB resistance traits. This is particularly helpful when adverse environmental conditions prohibit accurate phenotyping. This study also shows that MTGS models can be effective for forward prediction when there are strong correlations between traits of interest and covariates in both training and validation populations.

Siblings of hospitalized newborns in neonatal intensive care units (NICU) experience unique thoughts and feelings in response to this situational crisis. Providing an opportunity for siblings and their parents to address both of their concerns can improve sibling adjustment to the NICU, and is also consistent with a family-centered care philosophy. This article traces the development and evolution of sibling policy and program changes at Helen DeVos Children's Hospital (HDVCH) NICU, and describes the current comprehensive model for inclusion of siblings. Particular emphasis will be given to the cornerstone program "Celebrating Siblings Pizza Party." Infection control considerations and the importance of an interdisciplinary team approach to enhance an array of sibling services are also highlighted.

The objective of this study was to examine the effects of diet energy density on ranking for dry matter intake (DMI), residual feed intake (RFI) and greenhouse gas emissions. Forty-two mature, gestating Angus cows (600 ± 69 kg BW; BCS 5.3 ± 1.1) with a wide range in DMI EPD (-1.38 to 2.91) were randomly assigned to 2 diet sequences; forage then concentrate (FC) or concentrate then forage (CF). The forage diet consisted of long-stem native grass hay plus protein supplement (HAY; 1.96 Mcal ME/kg DM). The concentrate diet consisted of 35% chopped grass hay and 65% concentrate feeds on a dry matter basis (MIX; 2.5 Mcal ME/kg DM). The GreenFeed Emission Monitoring system was used to determine CO2, O2, and CH4 flux. Cow performance traits, ultrasound back fat and rump fat, feed DMI, and gas flux data were analyzed in a crossover design using a mixed model including diet, period, and sequence as fixed effects and pen and cow within sequence as random effects. For all measured traits excluding DMI, there was a diet × sequence interaction (P < 0.05). The correlation between MIX and HAY DMI was 0.41 (P = 0.067) and 0.47 (P = 0.03) for FC and CF sequences, respectively. There was no relationship (P > 0.66) between HAY and MIX average daily gain, regardless of sequence. Fifty seven percent of the variation in DMI was explained by metabolic BW, average daily gain (ADG), and body condition score for both diets during the first period. During the second period, the same three explanatory variables accounted for 38 and 37 percent of the variation in DMI for MIX and HAY diets, respectively. The negative relationship between body condition score and DMI was more pronounced when cows consumed the MIX diet. The was no relationship between MIX and HAY RFI, regardless of sequence (P > 0.18). During the first period, correlations for CO2, CH4, and O2 with MIX DMI were 0.69, 0.81 and 0.56 (P ≤ 0.015), respectively and 0.76, 0.74 and 0.64 (P < 0.01) with HAY DMI. During the second period, correlations for CO2, CH4, and O2 with MIX DMI were 0.62, 0.47 and 0.56 (P ≤ 0.11), respectively. However, HAY DMI during the second period was not related to gas flux (P > 0.47). Results from this experiment indicate that feed intake of two energy-diverse diets is moderately correlated while ADG while consuming the two diets is not related. Further experimentation is necessary to determine if gas flux data can be used to predict feed intake in beef cows.

The objectives of these experiments were to determine the relationship between maintenance requirements and energy partitioned to maternal tissue or milk production in limit-fed Angus cows and to determine the relationship between retained energy during the lactation period to dry-period voluntary forage intake (VDMI). Twenty-four mature fall-calving Angus cows were used in a 79-d study during late lactation to establish daily metabolizable energy required for maintenance (MEm). Cows were individually fed daily a mixed diet (2.62 Mcal MEl/kg, 18.2% crude protein) to meet energy and protein requirements of 505 kg beef cows producing 8.2 kg milk daily. If cow BW changed by ±9 kg from initial BW, daily feed intake was adjusted to slow BW loss or reduce BW gain. Milk yield and composition were determined on 3 occasions throughout the study. Maintenance was computed as metabolizable energy intake minus retained energy assigned to average daily maternal tissue energy change, average daily milk energy yield, and average daily energy required for pregnancy. After calves were weaned, cows were fed a low-quality grass hay diet (8.2% crude protein, 65% NDF) and VDMI was measured for 21 d. Lactation maintenance energy was 83% the default value recommended by NASEM (2016) for lactating Angus cows. Increasing lactation-period retained energy (decreasing BW loss and increasing milk energy yield) was associated with lower maintenance energy requirements (P < 0.01; R 2 = 0.92). Increased residual daily gain during lactation was associated with lower lactation maintenance energy requirements (P = 0.05; R 2 = 0.17). Post-weaning VDMI was not related to late-lactation milk energy production, although sensitive to lactation period BCS and BW loss. These results contradict previous reports suggesting that maintenance requirements increase with increasing milk yield.

This study’s objective was to determine the relationship between retained energy, lactation maintenance energy requirement (MER), and dry period voluntary feed intake (VOL) in beef cows. Twenty-four mature fall-calving Angus cows were used in an 82-d study during lactation to establish maintenance energy requirements followed by a voluntary feed intake study after weaning. During the lactation MER experiment, cows were housed in 2 drylot pens and limit-fed a mixed hay/concentrate diet (17.8% CP, 2.6 Mcal/kg ME, DM basis) individually once per d in a stall barn. Cows were adapted to the diet and feeding management for the first 16 d. Subsequently, cows were weighed and feed allowance adjusted at 14-d intervals to achieve BW and BCS stasis. Milk yield and composition were determined on d – 10, 49, and 77 using a milking machine. Retained energy was calculated as average daily maternal tissue energy change plus average daily milk energy yield. During the post-weaning VOL experiment, cows were provided ad libitum access to a grass hay diet for 41 d (8.15% CP, 1.8 Mcal/kg ME, DM basis) using five individual feed intake monitoring units (SmartFeed, C-Lock, Inc). Each one unit increase in metabolizable energy intake, kcal/kg BW0.75 was associated with a 0.86 ± 0.28 kcal/kg BW0.75 increase in total retained energy (P = 0.005). Using this partial efficiency coefficient, ME required for maintenance declined by 0.80 ± 0.11 kcal ME/kg BW0.75 for each additional kcal net energy retained/kg BW0.75 (P < 0.0001). There was no relationship between lactation-period retained energy and post-weaning VOL forage dry matter intake. The present study results contradict previous reports suggesting that maintenance requirements increase with increasing productivity.