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

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

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

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

Human Papillomavirus 16 E5 Induces Bi-Nucleated Cell Formation By Cell-Cell Fusion

Human Papillomaviruses (HPV) 16 is a DNA virus encoding three oncogenes – E5, E6, and E7. The E6 and E7 proteins have well-established roles as inhibitors of tumor suppression, but the contribution of E5 to malignant transformation is controversial. Using spontaneously immortalized human keratinocytes (HaCaT cells), we demonstrate that expression of HPV16 E5 is necessary and sufficient for the formation of bi-nucleated cells, a common characteristic of precancerous cervical lesions. Expression of E5 from non-carcinogenic HPV6b does not produce bi-nucleate cells. Video microscopy and biochemical analyses reveal that bi-nucleates arise through cell-cell fusion. Although most E5-induced bi-nucleates fail to propagate, co-expression of HPV16 E6/E7 enhances the proliferation of these cells. Expression of HPV16 E6/E7 also increases bi-nucleated cell colony formation. These findings identify a new role for HPV16 E5 and support a model in which complementary roles of the HPV16 oncogenes lead to the induction of carcinogenesis

Developing and interpreting aqueous functional assays for comparative property-activity relationships of different nanoparticles

It is difficult to relate intrinsic nanomaterial properties to their functional behavior in the environment. Unlike frameworks for dissolved organic chemicals, there are few frameworks comparing multiple and inter-related properties of engineered nanomaterials (ENMs) to their fate, exposure, and hazard in environmental systems. We developed and evaluated reproducibility and inter-correlation of 12 physical, chemical, and biological functional assays in water for eight different engineered nanomaterials (ENMs) and interpreted results using activity-profiling radar plots. The functional assays were highly reproducible when run in triplicate (average coefficient of variation [CV] = 6.6%). Radar plots showed that each nanomaterial exhibited unique activity profiles. Reactivity assays showed dissolution or aggregation potential for some ENMs. Surprisingly, multi-walled carbon nanotubes (MWCNTs) exhibited movement in a magnetic field. We found high inter-correlations between cloud point extraction (CPE) and distribution to sewage sludge (R-2 = 0.99), dissolution at pH 8 and pH 4.9 (R-2 = 0.98), and dissolution at pH 8 and zebrafish mortality at 24 hpf (R-2 = 0.94). Additionally, most ENMs tend to distribute out of water and into other phases (i.e., soil surfaces, surfactant micelles, and sewage sludge). The activity-profiling radar plots provide a framework and estimations of likely ENM disposition in the environment. (C) 2018 Elsevier B.V. All rights reserved

Lipid Peroxidative Damage on Cisplatin Exposure and Alterations in Antioxidant Defense System in Rat Kidneys: A Possible Protective Effect of Selenium

Cisplatin (Cis-diamminedichloroplatinum II, CP) is an important chemotherapeutic agent, useful in the treatment of several cancers, but with several side effects such as nephrotoxicity. The present study investigated the possible protective effect of selenium (Se) against CP-induced oxidative stress in the rat kidneys. Male Wistar albino rats were injected with a single dose of cisplatin (7 mg CP/kg b.m., i.p.) and selenium (6 mg Se/kg b.m, as Na2SeO3, i.p.), alone or in combination. The obtained results showed that CP increased lipid peroxidation (LPO) and decreased reduced glutathione (GSH) concentrations, suggesting the CP-induced oxidative stress, while Se treatment reversed this change to control values. Acute intoxication of rats with CP was followed by statistically significant decreased activity of antioxidant defense enzymes: superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GSH-Px), glutathione reductase (GR) and glutathione-S-transferase (GST). Treatment with Se reversed CP-induced alterations of antioxidant defense enzyme activities and significantly prevented the CP-induced kidney damage

Optimizing expression and purification of an ATP-binding gene gsiA from Escherichia coli k-12 by using GFP fusion

The cloning, expression and purification of the glutathione (sulfur) import system ATP-binding protein (gsiA) was carried out. The coding sequence of Escherichia coli gsiA, which encodes the ATP-binding protein of a glutathione importer, was amplified by PCR, and then inserted into a prokaryotic expression vector pWaldo-GFPe harboring green fluorescent protein (GFP) reporter gene. The resulting recombinant plasmid pWaldo-GFP-GsiA was transformed into various E. coli strains, and expression conditions were optimized. The effect of five E. coli expression strains on the production of the recombinant gsiA protein was evaluated. E. coli BL21 (DE3) was found to be the most productive strain for GsiA-GFP fusion-protein expression, most of which was insoluble fraction. However, results from in-gel and Western blot analysis suggested that expression of recombinant GsiA in Rosetta (DE3) provides an efficient source in soluble form. By using GFP as reporter, the most suitable host strain was conveniently obtained, whereby optimizing conditions for overexpression and purification of the proteins for further functional and structural studies, became, not only less laborious, but also time-saving