Animal - Open Space最新文献

This work was originated from the need to study how animals individually react to environmental challenges. Common practical constraints in research protocols often lead to data collected at frequencies that are not high enough to capture the dynamics of animal responses. One approach to deal with that issue is to transform discrete empirical time series into continuous functions from which several descriptors can be extracted to characterise the response. A method for the extraction of smoothed functions from milk yield (MY) time series has been published before for dairy cows. This method was applied to detect challenges a posteriori. In this paper, we present an adaptation of this differential smoothing methodology, for the case when the environmental challenge is known a priori. This is advantageous because it allows for a more detailed characterisation of the response. Full description of the methodology is presented, where operations from differential calculus are applied to the smoothed functions to extract 23 descriptors that characterise the shape, dynamics and delay of individual responses to a single known challenge. We present examples of the application of the algorithm to individual time series of MY and plasma non-esterified fatty acid concentrations from suckling cows exposed to nutritional challenges that are known a priori. We propose a selection strategy for the smoothing coefficient (λ) based on the optimisation between noise reduction and output stability. If applied to groups of individuals that are sufficiently large, this methodology could provide information to help discriminating animals based on how they respond to the environmental challenges. This methodology may be used to develop decision-making tools for the selection of resilient individuals aiming at improving robustness and performance.

Heat stress is a growing problem in dairy cows, and interest is developing in calculating heat stress risk (Temperature Humidity Index – THI) without using specific farm data and in forecasting THI changes a few days in advance. Previous workers have shown that calculating THI values inside cattle sheds using data from local Meteorological Stations is not sufficiently accurate. Weather forecasting is becoming more local and can forecast on-farm temperature and humidity. This work looked at how well THI inside a cow shed could be predicted from data collected outside the cow shed on British farms. Six farms were monitored from 1 May 2021 to 30 Sept 2021 using bespoke data monitors that uploaded the data to the cloud in real time through the cellular network. Calculated THI values for inside and outside the cow shed were highly correlated (P < 0.001), and a regression predicting THI inside the shed from the THI outside the shed was highly significant (P < 0.001). However, farm-specific regressions had significantly different regression intercepts. Including calving pattern type (autumn or all year round) and calendar month separately or together improved the regression. The 95% confidence interval (CI) of the prediction was 10.8 THI units for the simple one-component model (THI_outside) and 7.8 for the three-component model (THI_outside, calendar month, calving pattern type). Farm-specific regressions had the lowest CI values suggesting there are farm-specific factors affecting THI that had not been captured. As a predictive model, the simple single component regression would be the most applicable but the relatively high CI means that predictions will not be that accurate with the risk of heat stress either under- or overemphasised on different farms. With one THI unit equating to approximately a 200 ml drop in milk yield in heat-stressed cows, such errors will be of biological and commercial significance. This in part may be due to the THI equation only considering temperature and humidity and ignoring solar radiation, shade, wind and animal factors such as milk yield, stage of pregnancy, weight and genetic variability. Further work is underway to develop an index that quantifies how the cow is responding to the combined heat-loading factors which may improve the prediction of heat stress.

Dual-Energy X-ray Absorptiometry (DEXA) has been shown to predict the processing weights of the carcass and cut-up pieces of the chicken. This study aimed to validate models to predict processing weights using DEXA that were previously developed. An experiment was conducted with broilers grown up in 80 floor pens randomly subjected to one of five dietary treatments. On day 41, all birds were weighed, seven were randomly selected per pen, and their weights were recorded. After a feed withdrawal period, five selected birds per pen were transported to a processing plant, and the rest of the birds were fed again. The carcass was weighed before and after chilling for one hour. The chilled carcass was cut up, the weights of each commercial piece were recorded (breast fillet, tenders, wings, leg quarters, total white meat, and ready-to-cook parts), and the corresponding unchilled weights were calculated. On day 43 or 44, the other two birds selected per pen were weighed, DEXA-scanned without fasting, and their fasted weights were determined by applying a previously developed equation. Predicted processing weights were obtained by entering DEXA-reported values into a set of models previously developed. All the data were adjusted to the same BW basis. The pen mean observed processing weights and the DEXA-predicted ones were used to validate the models. The linear regression between predicted and observed values was calculated, and the R² was used as a precision index. The parallelism of the predicted and observed response curves across dietary treatments, and the model prediction error and accuracy were determined. The validation criteria were based on the validation R², the change in R² from development to validation, the parallelism of response curves, and the prediction accuracy. The validation R² of all tested models predicting the weight of cut-up pieces was > 0.84, and their prediction errors were ≤ 5.85 %, except for the model predicting the weight of the wings (prediction error > 10 %). All traits showed parallel trends when the response curves across treatments obtained with the DEXA-predicted values or the processing plant data were compared. In conclusion, all models but the one predicting the weight of wings satisfied the evaluation criteria and were validated, supporting the use of DEXA to determine the processing weights of broilers and its application to the study of nutrition interventions to improve breast meat production.

Early posthatch physiological responses and growth performances were assessed in 80 chicks hatched from Transylvanian naked neck (TN) chicken eggs stored in a cold room for 0, 3, 6, 9, and 12d before incubation with four replicates per group. The rectal temperature (RT) of birds hatched from 0 and 3d stored eggs were significantly (P < 0.01) lower than in the 9–12d storage groups. There was (P < 0.001) an increase in skin temperature on the breast (STB) as the length of storage increased. Day-old chicks and 28d-old chickens from non-stored eggs had higher (P < 0.05) packed cell volume (PCV) and haemoglobin (Hb) concentration than eggs stored for 12d. Chickens in 0, 3 and 6d storage groups had (P < 0.01) higher platelet count values than in the 12d group. During d1-28, chicks from 0 to 6d egg storage had (P < 0.001) higher weight gain and final live weight than 9–12d storage. Feed consumption was (P < 0.01) lower in 3d than in 6–12d. Chicks from 0 to 6d storage recorded a lower (P < 0.001) feed conversion ratio than in 9–12d storage. In conclusion, prolonged storage of TN eggs resulted in higher RT and STB, lower PCV and Hb, and lower growth performance during early posthatch age.

Efficient cell lysis is critical for the extraction of DNA from difficult-to-lyse microorganisms such as Gram-positive bacteria and filamentous fungi. A bead-beating (BB) step is usually included in DNA extraction protocols to improve cell lysis. However, there is no consensus on the duration of BB that is necessary for complete lysis of the microbial communities present in complex microbial ecosystems, but which will still maintain the integrity of DNA released from easy-to-lyse microbes. Another consideration is that most protocols are tailored to one particular target group of microbes, typically either bacteria or fungi, in a given sample matrix. In this study, we investigated the impact of five BB durations (0, 3, 10, 15 and 20 min) during DNA extraction with the QIAamp® Fast DNA Stool Mini Kit, on the bacterial and fungal communities of single pig faecal and liquid feed samples, extracted in triplicate, with the objective of determining a suitable ‘catch-all’ method. Both sample types were subjected to the BB durations in triplicate, followed by 16S (bacterial) and ITS2 (fungal) rDNA amplicon sequencing. The performance of the different BB durations was assessed based on the quantity of total DNA extracted, alpha- and beta-diversity analyses of the resultant microbial communities and differential abundance of bacterial and fungal taxa. Our results suggest that 20 min of BB is most appropriate for maximising the lysis of difficult-to-lyse bacteria and fungi in both pig faeces and liquid feed, while minimising the negative impact on easier-to-lyse microbes. Total DNA yield increased with BB duration for both sample types; however, the yield from faeces decreased after 20 min of BB. Despite this, DESeq2 analysis indicated that changes in the differential abundances of the dominant taxa at this point were limited, which was supported by the Shannon diversity results. Maximising the BB duration appeared to be necessary in order to obtain a representative profile of the Gram-positive bacteria, particularly in liquid feed, and of the filamentous fungi present in both sample types. However, considering the small sample size, along with the reliance on differential as opposed to absolute abundances to validate increases or decreases in taxa, a larger-scale study is necessary to verify the findings of the present study.

Loline alkaloid has suggested antimicrobial and anthelmintic properties with very low mammalian toxicity. There are several known derivatives of loline; N-formyl loline (NFL), N-acetyl loline (NAL), N-acetyl norloline (NANL), N-methyl loline (NML), and loline base. However, these must reach the abomasum or intestine and then be absorbed to have any potential effect. Therefore, passive absorption in isolated gastrointestinal tissues and distribution in the tissue of lambs were determined. Experiment 1: Lamb (n = 6) isolated gastrointestinal tissues were removed and mounted in an Ussing chamber. Approximately, 1 034 µg/g of loline and 22.1 µg/mL of caffeine were added to the donor chamber to measure loline flux at 0, 0.5, 1, and 2 hours. Experiment 2: Two, 12-week-old lambs were dosed with 52.5 mg/kg BW loline twice and were slaughtered to determine the distribution in gastrointestinal, organs, and blood. Passive absorption was 5% in ileum, <2% in ruminal or abomasal tissues. Loline base and NFL were passively absorbed across all tissues, with NAL and NANL only crossing small intestine tissues. Surprisingly, no caffeine crossed any tissues. Loline base and small amounts of NFL were in blood plasma, and loline base was also found in liver and kidneys. Results indicate either the majority of loline is not passively absorbed or membrane integrity was affected as suggested by lack of caffeine absorption.

Bending is a method for transforming symmetric non-positive-definite matrices to positive-definite (PD) to guarantee the invertibility of the matrix. Most of the bending approaches are based on eigendecomposition and eigenvalue modification of the matrix. Genetic and residual covariance matrices among traits used in multivariate analyses are among those matrices. Due to computational limitations, variance components for many traits are often estimated for multiple subsets of traits. Collating smaller matrices into a larger matrix may result in a non-PD matrix. Although the estimated covariance matrix from a single variance component estimation procedure is PD, the variance component estimation procedure requires a starting PD matrix. Aiming to modify the existing bending methods to improve bending performance, several tests were performed on a sample non-PD covariance matrix. Replacing negative eigenvalues with small positive values in decreasing order did not improve the bending performance (average absolute deviation between the upper triangle elements of the original matrix and the bent matrix) compared to replacing eigenvalues smaller than a small positive value with that small positive value (ε = 1e−4). Bending increases the sum of eigenvalues. Keeping the sum of eigenvalues constant (equal to the trace of the original matrix) did not improve the bending performance. Bending performance deteriorated when large eigenvalues were reduced to keep the sum of eigenvalues constant. In another attempt, besides increasing eigenvalues smaller than ε to ε, the smallest eigenvalue greater than ε was reduced. Reducing that eigenvalue to a certain level improved the bending performance. Therefore, a controlled reduction of the smallest eigenvalue greater than ε while simultaneously monitoring the improvement in bending performance is recommended.

In pig farming, physical constraints and genetic selection for high production are risk factors for the development of leg disorders, such as degraded locomotor activity. Interactions between both factors need to be explored. The study was carried out on two replicates of 80 pure-bred Large White growing-finishing pigs from the 8th generation of two divergent lines selected for low and high residual feed intake (LRFI, HRFI). Each replicate included 40 LRFI pigs and 40 HRFI pigs, housed on partly slatted flooring in a room equipped with a sorter allowing access to electronic self-feeders during two replicates. Ear tags determined the side of the room to which the pigs were oriented after the sorter exit and the distance back to the sorter (short: spontaneous activity, long: forced activity (FA)). Lameness was assessed individually weekly using visual gait scoring. At slaughter (weight of 100 kg), postmortem quantification of osteochondrosis (OC) lesions was performed on both the proximal and distal extremities of the humerus and femur. Low RFI pigs showed a lower feed conversion ratio (P < 0.001). They also showed lower individual numbers of sorter crossings per day and a lower proportion of standing pigs, which confirmed their lower physical activity. Forced activity clearly increased the number of sorter crossings/d/pig (P < 0.001), and the magnitude of the effect of FA was clearly lower in LRFI pigs than in HRFI pigs. The occurrence of gait was low (less than 9% of recorded scores). The proportion of scores classified as stiffness was higher for LRFI pigs than in HRFI pigs (P < 0.0001). The average lameness score was also higher for LRFI pigs and lower with FA (P < 0.05). The pigs of the LRFI line showed higher OC scores on both the proximal humerus and femur (P < 0.001) and lower OC scores on the distal humerus with surface evaluation (P < 0.05). The carcasses of LRFI pigs were heavier with a higher lean meat percentage (P < 0.001). Most OC scores were unaffected by FA. Only the OC scores of the distal femur (slice method) were higher with increased activity in LRFI pigs, whereas they were lower in HRFI pigs (P < 0.05). Seric biomarkers of cartilage synthesis and degradation were higher for pigs from the LRFI line, but no correlation could be observed between individual OC scores and cartilage biomarker contents.

Changes in the postprandial nutrient concentration in the plasma are the result of the combined effects of intake, digestion, absorption, and metabolism. The concentration typically follows an asymmetrical bell-shaped curve as a function of the time after the meal. Although differences between dietary treatments can be analysed using a pairwise comparison of the observed nutrient concentrations, this provides little insight in the possible underlying biological mechanisms. These mechanisms may be represented in a model that can be used in a regression analysis to summarise the observed data in a limited number of parameters. The objective of this study was to propose equations that can be used in the statistical analysis of postprandial nutrient concentrations. The equations were derived from the compartmental representation of the Erlang function in which the last of a series of compartments was assumed to represent the nutrient concentration in the plasma. The preceding compartments were used to represent the postprandial response provoked by ingestion of the meal. A homeostatic control mechanism was included based on a target nutrient concentration that the animal seeks to maintain. This target concentration may differ between the fasting state and after ingestion of a meal. The models were developed as differential equations, which were integrated analytically providing equations that can be used for data analysis. The fit of the equations was tested using the postprandial histidine concentration of a pig that received a diet that was either balanced or unbalanced in the amino acid supply. The unbalanced diet was also deficient in histidine. The observed data could be summarised in three or four parameters that describe the target histidine concentration after an overnight fast, the possible change in the target concentration due to ingestion of a meal, the area under curve of the postprandial response (i.e., the “metabolic exposure”), and a rate constant describing the dynamics of the response. The biological interpretation of these and derived parameters is discussed, including the potential pitfalls of interpreting nutrient concentrations as nutrient flows. In conclusion, the models developed here are based on biological concepts and allow to summarise time series of nutrient concentrations in a limited number of parameters. The concepts can be modified depending on how the biological mechanisms involved are perceived and on the type of available data.