Comparison of three reproductive management strategies for lactating dairy cows using combination of estrus detection or ovulation synchronization and Fixed-Timed Artificial Insemination.

The objective of this study was to compare the reproductive performance of lactating dairy cows submitted to first AI after combination of estrus detection and fixed timed AI (FTAI) and FTAI only. Cows were randomly assigned to receive AI at detected estrus between 50 and 70 d in milk (DIM), if not detected in estrus, were enrolled in either Ovsynch (ED-Ov, n = 485) or PRIDsynch (ED-PR, n = 505) protocols; or received FTAI at 80 DIM after Double-Ovsynch protocol (DO, n = 501). Cows were body condition scored (BCS) at calving and at 43 DIM; and evaluated for postpartum disorders within 7 d postpartum; clinical mastitis, lameness and bovine respiratory disease were recorded until first AI. Ovarian cyclicity was monitored at 43 and 50 DIM, and at 70 and 77 DIM. Pregnancy diagnoses (PD) were performed at 32 and 63 d after AI. Overall prevalence of postpartum anovulation was 7.8%. Pregnancy per AI (P/AI) did not differ between reproductive strategies at 32 d PD (ED-Ov = 43.2%; ED-PR = 41.7%; DO= 45.3%). Primiparous cows had greater P/AI than multiparous cows (53.7% vs 36.8%). Cows on farm 1 had lower P/AI compared with their counterparts on farm 2 (42.1% vs 45.4%). Cows with BCS > 2.5 at 43 DIM had greater P/AI compared with cows with BCS ≤ 2.5 (44.5% vs 34.7%). Similar P/AI for cow's receiving AI at detected estrus and FTAI, low prevalence of disease anovulation may have contributed to the similar performance of ED-Ov, ED-PR and DO.

Association of days in close up, gestation length, and rumination around time of calving with disease and pregnancy outcomes in multiparous dairy cows.

The purpose of this study was to evaluate the effect of rumination times and days spent in a close-up group before calving (DCU) on early-lactation health and reproductive outcomes in dairy cows. Data were gathered for 719 cows located in a single herd. Herd management and reproductive records were analyzed for cows receiving treatment in the first 30 d of lactation (days in milk; DIM) for clinical mastitis, reproductive tract disease, ketosis, milk fever, and displaced abomasum. Rumination times for each cow were downloaded daily from the herd's automated collar system used to generate heat and health alerts for each cow beginning at 21 d precalving until 14 d postcalving. During the first 30 DIM, 121 cows (18%) developed at least 1 disease-any combination of ketosis (40 cows, 5.9% of total), mastitis (17 cows, 2.5%), metritis (75 cows, 11%), milk fever (17 cows, 2.5%), or displaced abomasum (28 cows, 4.1%); 305 cows (45%) were pregnant again at 100 DIM, and an additional 139 cows (20%) were pregnant at 150 DIM. Principal component analysis was used to determine the relationship between gestation length and DCU and their association with the odds of developing disease in early lactation. We did not find any significant association between precalving rumination time and disease within the first 30 DIM. Higher rumination time in the week before calving was shown to be strongly linked to a shorter time to subsequent pregnancy, whereas rumination times postcalving were not associated with changes in the time to pregnancy. Principal component analysis showed that a curvilinear combination of gestation length and DCU (principal component 1) was significantly associated with changes in disease incidence in the first 30 DIM. Gestation length and time spent in close up are important management factors in reducing the incidence of disease in early lactation, and rumination times around calving may help predict future reproductive outcomes.

Relative quantitation goes viral: An RT-qPCR assay for a grapevine virus.

Accurate detection and quantitation of viruses can be beneficial to plant-virus interaction studies. In this study, three SYBR green real-time RT-PCR assays were developed to quantitate grapevine leafroll-associated virus 3 (GLRaV-3) in infected vines. Three genomic regions (ORF1a, coat protein and 3'UTR) were targeted to quantitate GLRaV-3 relative to three stably expressed reference genes (actin, GAPDH and α-tubulin). These assays were able to detect all known variant groups of GLRaV-3, including the divergent group VI, with equal efficiency. No link could be established between the concentration ratios of the different genomic regions and subgenomic RNA (sgRNA) expression. However, a significant lower virus concentration ratio for plants infected with variant group VI compared to variant group II was observed for the ORF1a, coat protein and the 3'UTR. Significant higher accumulation of the virus in the growth tip was also detected for both variant groups. The quantitation of viral genomic regions under different conditions can contribute to elucidating disease aetiology and enhance knowledge about virus ecology.

Predicting mortality and length-of-stay for neonatal admissions to private hospital neonatal intensive care units: a Southern African retrospective study.

OBJECTIVES: To predict neonatal mortality and length of stay (LOS) from readily available perinatal data for neonatal intensive care unit (NICU) admissions in Southern African private hospitals. METHODS: Retrospective observational study using perinatal data from a large multicentre sample. Fifteen participating NICU centres in the Medi-Clinic private hospital group in Southern Africa. We used 2376 infants born between 1 January - 31 December 2008 to build the regression models, and a further 1 578 infants born between 1 January - 31 December 2007 to test the models. Outcome measures were mortality and length of hospital stay for NICU admissions. RESULTS: Of the infants included in the 2008 dataset, ninety-one (3.8%) died after being admitted to NICU centres. The median LOS for non-transferred survivors was 11 days. An analysis of the structural peculiarities of the data showed high correlations between groups of the perinatal variables pertaining to the size and Apgar scores of the newborn infants, respectively. The logistic regression model to predict neonatal mortality had a good fit (AUC: 0.8507, misclassification rate: 13.6%), but the low positive predictive value of this model reduces its usefulness. The poisson log-linear model to predict LOS had a good fit (predicted R(2): 0.7027). CONCLUSIONS: Apgar score at one minute, birth weight, and delivery mode significantly influence the odds of neonatal death and are associated with significant effects on LOS.