Evaluating equations estimating change in swine feed intake during heat and cold stress

The objectives of this study were to evaluate heat stress feed intake models for growing swine using a data set assembled from the literature and to develop a series of new equations modeling the influence of the thermal environment and interactions between the thermal environmental and other factors on feed intake. A literature survey was conducted to identify studies assessing intake responses to temperature. The resulting data set comprised 35 studies containing 120 comparisons to thermoneutral intake. Intake as a fraction of thermoneutral intake (FFI) was the primary response variable, where a value of 1 represented no change from thermoneutral intake. The FFI predicted by NRC and a recent model from a meta-analysis (Renaudeau et al.,) were compared to observed values. New parameters for the NRC equation (NRCmod) were derived, and a series of new equations incorporating duration of exposure (TD), temperature cycling (TC), and floor type (TH) were also derived. Root-mean-square prediction error (RMSPE) and concordance correlation coefficients were used to evaluate all models. The RMSPE for the NRC model was 23.6 with mean and slope bias accounting for 12.6% and 51.1% of prediction error, respectively. The TD, TC, and TH models had reduced RMSPE compared with NRC: 12.9 for TD, 12.6 for TC, and 12.9 for TS. Substantial improvements were also made by refitting parameters (NRCmod; RMSPE 13.0%). In NRCmod, TD, TC, and TH, random error was the predominant source, accounting for over 97% of prediction error. The Renaudeau et al. model was also evaluated. Renaudeau et al. had relatively low RMSPE (22.3) for intake but higher RMSPE for FFI (22.6) than NRC, NRCmod, TD, TC, or TH. Additional parameters were derived for the Renaudeau et al. equation to account for housing system and diet characteristics. This adjustment reduced RMSPE of predicting feed intake (16.0) and FFI (16.3) and reduced systematic bias in the equation. This evaluation of equations highlights the effects of novel explanatory variables on feed intake during heat stress, and the comparison can be useful when selecting a model that best explains variability in feed intake responses to heat stress given available input data

Suicide and drought in New South Wales, Australia, 1970–2007

There is concern in Australia that droughts substantially increase the incidence of suicide in rural populations, particularly among male farmers and their families. We investigated this possibility for the state of New South Wales (NSW), Australia between 1970 and 2007, analyzing data on suicides with a previously established climatic drought index. Using a generalized additive model that controlled for season, region, and long-term suicide trends, we found an increased relative risk of suicide of 15% (95% confidence interval, 8%–22%) for rural males aged 30–49 y when the drought index rose from the first quartile to the third quartile. In contrast, the risk of suicide for rural females aged >30 y declined with increased values of the drought index. We also observed an increased risk of suicide in spring and early summer. In addition there was a smaller association during unusually warm months at any time of year. The spring suicide increase is well documented in nontropical locations, although its cause is unknown. The possible increased risk of suicide during drought in rural Australia warrants public health focus and concern, as does the annual, predictable increase seen each spring and early summer. Suicide is a complex phenomenon with many interacting social, environmental, and biological causal factors. The relationship between drought and suicide is best understood using a holistic framework. Climate change projections suggest increased frequency and severity of droughts in NSW, accompanied and exacerbated by rising temperatures. Elucidating the relationships between drought and mental health will help facilitate adaptation to climate change

Depth video data-enabled predictions of longitudinal dairy cow body weight using thresholding and Mask R-CNN algorithms

Monitoring cow body weight is crucial to support farm management decisions due to its direct relationship with the growth, nutritional status, and health of dairy cows. Cow body weight is a repeated trait, however, the majority of previous body weight prediction research only used data collected at a single point in time. Furthermore, the utility of deep learning-based segmentation for body weight prediction using videos remains unanswered. Therefore, the objectives of this study were to predict cow body weight from repeatedly measured video data, to compare the performance of the thresholding and Mask R-CNN deep learning approaches, to evaluate the predictive ability of body weight regression models, and to promote open science in the animal science community by releasing the source code for video-based body weight prediction. A total of 40,405 depth images and depth map files were obtained from 10 lactating Holstein cows and 2 non-lactating Jersey cows. Three approaches were investigated to segment the cow's body from the background, including single thresholding, adaptive thresholding, and Mask R-CNN. Four image-derived biometric features, such as dorsal length, abdominal width, height, and volume, were estimated from the segmented images. On average, the Mask-RCNN approach combined with a linear mixed model resulted in the best prediction coefficient of determination and mean absolute percentage error of 0.98 and 2.03%, respectively, in the forecasting cross-validation. The Mask-RCNN approach was also the best in the leave-three-cows-out cross-validation. The prediction coefficients of determination and mean absolute percentage error of the Mask-RCNN coupled with the linear mixed model were 0.90 and 4.70%, respectively. Our results suggest that deep learning-based segmentation improves the prediction performance of cow body weight from longitudinal depth video data

Prediction of portal and hepatic blood flow from intake level data in cattle

There is growing interest in developing integrated post-absorptive metabolism models for dairy 30 cattle. An integral part of linking a multi-organ post-absorptive model is the prediction of nutrient 31 fluxes between organs, and thus blood flow. It was the purpose of this paper to use a multivariate 32 meta-analysis approach to model portal blood flow (PORBF) and hepatic venous blood flow 33 (HEPBF) simultaneously, with evaluation of hepatic arterial blood flow (ARTBF; ARTBF = 34 HEPBF – PORBF) and PORBF/HEPBF (%) as calculated values. The database used to develop 35 equations consisted of 296 individual animal observations (lactating and dry dairy cows and beef 36 cattle) and 55 treatments from 17 studies, and a separate evaluation database consisted of 34 37 treatment means (lactating dairy cows and beef cattle) from 9 studies obtained from the literature. 38 Both databases had information on DMI, MEI, body weight and a basic description of the diet 39 including crude protein intake and forage proportion of the diet (FP; %). Blood flow (L/h or L/kg 40 BW0.75/h) and either DMI or MEI (g or MJ/d or g or MJ/kg BW0.75/d) with linear and quadratic 41 fits were examined. Equations were developed using cow within experiment and experiment as 42 random effects, and blood flow location as a repeated effect. Upon evaluation with the evaluation 43 database, equations based on DMI typically resulted in lower root mean square prediction errors, 44 expressed as a % of the observed mean (rMSPE%) and higher concordance correlation coefficient 45 (CCC) values than equations based on MEI. Quadratic equation terms were frequently non-46 significant, and the quadratic equations did not out-perform their linear counterparts. The best 47 performing blood flow equations were: PORBF (L/h) = 202 (± 45.6) + 83.6 (± 3.11) × DMI (kg/d) and HEPBF (L/h) = 186 (± 45.4) + 103.8 (± 3.10) × DMI (kg/d), with rMSPE% values of 17.5 and 49 16.6 and CCC values of 0.93 and 0.94, respectively. The residuals (predicted – observed) for 50 PORBF/HEPBF were significantly related to the forage % of the diet, and thus equations for 51 3 PORBF and HEPBF based on forage and concentrate DMI were developed: PORBF (L/h) = 210 52 (± 51.0) + 82.9 (± 6.43) × Forage (kg DM/d) + 82.9 (± 6.04) × Concentrate (kg DM/d), and 53 HEPBF (L/h) = 184 (± 50.6) + 92.6 (± 6.28) × Forage (kg DM/d) + 114.2 (± 5.88) × Concentrate 54 (kg DM/d), where rMSPE% values were 17.5 and 17.6 and CCC values were 0.93 and 0.94, 55 respectively. Division of DMI into forage and concentrate fractions improved the joint Bayesian 56 Information Criterion (BIC) value for PORBF and HEPBF (BIC = 6512 vs. 7303), as well as 57 slightly improved the rMSPE and CCC for ARTBF and PORBF/HEPBF. This was despite 58 minimal changes in PORBF and HEPBF predictions. Developed equations predicted blood flow 59 well, and could easily be used within a post absorptive model of nutrient metabolism. Results also 60 suggest different sensitivity of PORBF and HEPBF to the composition of DMI, and accounting 61 for this difference resulted in improved ARTBF predictions

Regulation of protein synthesis in mammary glands of lactating dairy cows by starch and amino acids

The objective of this study was to evaluate local molecular adaptations proposed to regulate protein synthesis in the mammary glands. It was hypothesized that AA and energy-yielding substrates independently regulate AA metabolism and protein synthesis in mammary glands by a combination of systemic and local mechanisms. Six primiparous mid-lactation Holstein cows with ruminal cannulas were randomly assigned to 4 treatment sequences in a replicated incomplete 4 x 4 Latin square design experiment. Treatments were abomasal infusions of casein and starch in a 2 x 2 factorial arrangement. All animals received the same basal diet (17.6% crude protein and 6.61 MJ of net energy for lactation/kg of DM) throughout the study. Cows were restricted to 70% of ad libitum intake and abomasally infused for 36 h with water, casein (0.86 kg/d), starch (2 kg/d), or a combination (2 kg/d starch + 0.86 kg/d casein) using peristaltic pumps. Milk yields and composition were assessed throughout the study. Arterial and venous plasma samples were collected every 20 min during the last 8 h of infusion to assess mammary uptake. Mammary biopsy samples were collected at the end of each infusion and assessed for the phosphorylation state of selected intracellular signaling molecules that regulate protein synthesis. Animals infused with casein had increased arterial concentrations of AA, increased mammary extraction of AA from plasma, either no change or a trend for reduced mammary AA clearance rates, and no change in milk protein yield. Animals infused with starch had increased milk and milk protein yields, increased mammary plasma flow, reduced arterial concentrations of AA, and increased mammary clearance rates and net uptake of some AA. Infusions of starch increased plasma concentrations of glucose, insulin, and insulin-like growth factor-I. Starch infusions increased phosphorylation of ribosomal protein S6 and endothelial nitric oxide synthase, consistent with changes in milk protein yields and plasma flow, respectively. Phosphorylation of the mammalian target of rapamycin was increased in response to starch only when casein was also infused. Thus, cell signaling molecules involved in the regulation of protein synthesis differentially responded to these nutritional stimuli. The hypothesized independent effects of casein and starch on animal metabolism and cell signaling were not observed, presumably because of the lack of a milk protein response to infused casein

Evaluation of the National Research Council (2001) dairy model and derivation of new prediction equations. 1. Digestibility of fiber, fat, protein, and nonfiber carbohydrate

Evaluation of ration balancing systems such as the National Research Council (NRC) Nutrient Requirementsseries is important for improving predictions of animal nutrient requirements and advancing feeding strategies. This work used a literature data set (n = 550) to evaluate predictions of total-tract digested neutral detergent fiber (NDF), fatty acid (FA), crude protein (CP), and nonfiber carbohydrate (NFC) estimated by the NRC (2001) dairy model. Mean biases suggested that the NRC (2001) lactating cow model overestimated true FA and CP digestibility by 26 and 7%, respectively, and under-predicted NDF digestibility by 16%. All NRC (2001) estimates had notable mean and slope biases and large root mean squared prediction error (RMSPE), and concordance (CCC) ranged from poor to good. Predicting NDF digestibility with independent equations for legumes, corn silage, other forages, and nonforage feeds improved CCC (0.85 vs. 0.76) compared with the re-derived NRC (2001) equation form (NRC equation with parameter estimates re-derived against this data set). Separate FA digestion coefficients were derived for different fat supplements (animal fats, oils, and other fat types) and for the basal diet. This equation returned improved (from 0.76 to 0.94) CCC compared with the re-derived NRC (2001) equation form. Unique CP digestibility equations were derived for forages, animal protein feeds, plant protein feeds, and other feeds, which improved CCC compared with the re-derived NRC (2001) equation form (0.74 to 0.85). New NFC digestibility coefficients were derived for grain-specific starch digestibilities, with residual organic matter assumed to be 98% digestible. A Monte Carlo cross-validation was performed to evaluate repeatability of model fit. In this procedure, data were randomly subsetted 500 times into derivation (60%) and evaluation (40%) data sets, and equations were derived using the derivation data and then evaluated against the independent evaluation data. Models derived with random study effects demonstrated poor repeatability of fit in independent evaluation. Similar equations derived without random study effects showed improved fit against independent data and little evidence of biased parameter estimates associated with failure to include study effects. The equations derived in this analysis provide interesting insight into how NDF, starch, FA, and CP digestibilities are affected by intake, feed type, and diet composition

Bovine Mammary Gland Biopsy Techniques

Bovine mammary gland biopsies allow researchers to collect tissue samples to study cell biology including gene expression, histological analysis, signaling pathways, and protein translation. This article describes two techniques for biopsy of the bovine mammary gland (MG). Three healthy Holstein dairy cows were the subjects. Before biopsies, cows were milked and subsequently restrained in a cattle chute. An analgesic (flunixin meglumine, 1.1 to 2.2 mg/kg of body weight) was administered via jugular intravenous [IV] injection 15-20 min prior to biopsy. For standing sedation, xylazine hydrochloride (0.01-0.05 mg/kg of body weight) was injected via the coccygeal vessels 5-10 min before the procedure. Once adequately sedated, the biopsy site was aseptically prepared and locally anaesthetized with 6 mL of 2% lidocaine hydrochloride via subcutaneous injection. Using aseptic technique, a 2 to 3 cm vertical incision was made using a number 10 scalpel. Core and needle biopsy tools were used. The core biopsy tool was attached to a cordless drill and inserted into the MG tissue through the incision using a clock-wise drill action. The needle biopsy tool was manually inserted into the incision site. Immediately after the procedure, an assistant applied pressure on the incision site for 20 to 25 min using a sterile towel to achieve hemostasis. Stainless steel surgical staples were used to oppose the skin incision. The staples were removed 10 days post-procedure. The main advantages of core and needle biopsies is that both approaches are minimally invasive procedures that can be safely performed in healthy cows. Milk yield following the biopsy was unaffected. These procedures require a short recovery time and result in fewer risks of complications. Specific limitations may include bleeding after the biopsy and infection on the biopsy site. Applications of these techniques include tissue collection for clinical diagnosis and research purposes, such as primary cell culture

All-cause mortality and long-term exposure to low level air pollution in the ‘45 and up study’ cohort, Sydney, Australia, 2006–2015

Epidemiological studies show that long-term exposure to ambient air pollution reduces life expectancy. Most studies have been in environments with relatively high concentrations such as North America, Europe and Asia. Associations at the lower end of the concentration-response function are not well defined.We assessed associations between all-cause mortality and exposure to annual average particulate matte

Alanine Aminotransferase, γ-Glutamyltransferase, and Incident Diabetes: The British Women's Heart and Health Study and meta-analysis

OBJECTIVE - To estimate and compare associations of alanine aminotransferase (ALT) and gamma-glutamyltransferase (GGT) with incident diabetes. RESEARCH DESIGN AND METHODS - ALT and GGT were studied as determinants of diabetes in the British Women's Heart and Health Study, a cohort of 4,286 women 60-79 years old (median follow-up 7.3 years). A systematic review and a mete-analysis of 21 prospective, population-based studies of ultrasonography, which diagnosed nonalcoholic fatty liver disease (NAFLD), ALT, and GGT as determinants of diabetes, were conducted, and associations of ALT and GGT with diabetes were compared. RESULTS - Ultrasonography-diagnosed NAFLD was associated with more than a doubling in the risk of incident diabetes (three studies). ALT and GGT both predicted diabetes. The fully adjusted hazard ratio (HR) for diabetes per increase in one unit of logged ALT was 1.83 (95% Cl 1.57-2.14, I-2 = 8%) and for GGT was 1.92 (1.66-2.21, I-2 = 55%). To directly compare ALT and GGT as determinants of diabetes, the fully adjusted risk of diabetes in the top versus bottom fourth of the ALT and GGT distributions was estimated using data from studies that included results for both markers. For ALT, the HR was 2.02 (1.59-2.58, I-2 = 27%), and for GIST the HR was 2.94 (1.98-3.88, I-2 = 20%), suggesting that GGT may be a better predictor (P = 0.05). CONCLUSIONS - Findings are consistent with the role of liver fat in diabetes pathogenesis. GGT may be a better diabetes predictor than ALT, but additional studies with directly determined liver fat content, ALT, and GGT are needed to confirm this finding. Document Type: Revie

Riječ uredništva

BACKGROUND: Methods for estimating air pollutant exposures for epidemiological studies are becoming more complex in an effort to minimise exposure error and its associated bias. While land use regression (LUR) modelling is now an established method, there has been little comparison between LUR and other recent, more complex estimation methods. Our aim was to develop a LUR model to estimate intra-city exposures to nitrogen dioxide (NO2) for a Sydney cohort, and to compare those with estimates from a national satellite-based LUR model (Sat-LUR) and a regional Bayesian Maximum Entropy (BME) model. METHODS: Satellite-based LUR and BME estimates were obtained using existing models. We used methods consistent with the European Study of Cohorts for Air Pollution Effects (ESCAPE) methodology to develop LUR models for NO2 and NOx. We deployed 46 Ogawa passive samplers across western Sydney during 2013/2014 and acquired data on land use, population density, and traffic volumes for the study area. Annual average NO2 concentrations for 2013 were estimated for 947 addresses in the study area using the three models: standard LUR, Sat-LUR and a BME model. Agreement between the estimates from the three models was assessed using interclass correlation coefficient (ICC), Bland-Altman methods and correlation analysis (CC). RESULTS: The NO2 LUR model predicted 84% of spatial variability in annual mean NO2 (RMSE: 1.2 ppb; cross-validated R2: 0.82) with predictors of major roads, population and dwelling density, heavy traffic and commercial land use. A separate model was developed that captured 92% of variability in NOx (RMSE 2.3 ppb; cross-validated R2: 0.90). The annual average NO2 concentrations were 7.31 ppb (SD: 1.91), 7.01 ppb (SD: 1.92) and 7.90 ppb (SD: 1.85), for the LUR, Sat-LUR and BME models respectively. Comparing the standard LUR with Sat-LUR NO2 cohort estimates, the mean estimates from the LUR were 4% higher than the Sat-LUR estimates, and the ICC was 0.73. The Pearson's correlation coefficients (CC) for the LUR vs Sat-LUR values were r = 0.73 (log-transformed data) and r = 0.69 (untransformed data). Comparison of the NO2 cohort estimates from the LUR model with the BME blended model indicated that the LUR mean estimates were 8% lower than the BME estimates. The ICC for the LUR vs BME estimates was 0.73. The CC for the logged LUR vs BME estimates was r = 0.73 and for the unlogged estimates was r = 0.69. CONCLUSIONS: Our LUR models explained a high degree of spatial variability in annual mean NO2 and NOx in western Sydney. The results indicate very good agreement between the intra-city LUR, national-scale sat-LUR, and regional BME models for estimating NO2 for a cohort of children residing in Sydney, despite the different data inputs and differences in spatial scales of the models, providing confidence in their use in epidemiological studies