Use of inline measures of l-lactate dehydrogenase for classification of posttreatment mammary Staphylococcus aureus infection status in dairy cows.

An automated method for determining whether dairy cows with subclinical mammary infections recover after antibiotic treatment would be a useful tool in dairy production. For that purpose, inline l-lactate dehydrogenase (LDH) measurements was modeled using a dynamic linear model; the variance parameters were estimated using the expectation-maximization algorithm. The method used to classify cows as infected or uninfected was based on a multiprocess Kalman filter. Two learning data sets were created: infected and uninfected. The infected data set consisted of records from 48 cows with subclinical Staphylococcus aureus infection from 4 herds collected in 2010. The uninfected data set came from 35 uninfected cows collected during 2013 from 2 herds. Bacteriological culturing was used as gold standard. To test the model, we collected data from the 48 infected cows 50 d after antibiotic treatment. As a result of the treatment, this test data set consisted of 25 cows that still had a subclinical infection and 23 cows that were recovered. Model sensitivity was 36.0% and specificity was 82.6%. To a large extent, l-lactate dehydrogenase reflected the cow's immune response to the presence of pathogens in the udder. However, cows that were classified correctly before treatment had a better chance of correct classification after treatment. This indicated a variation between cows in immune response to subclinical mammary infection that may complicate the detection of subclinically infected cows and determination of recovery.

Economic comparison of common treatment protocols and J5 vaccination for clinical mastitis in dairy herds using optimized culling decisions.

This study used an existing dynamic optimization model to compare costs of common treatment protocols and J5 vaccination for clinical mastitis in US dairy herds. Clinical mastitis is an infection of the mammary gland causing major economic losses in dairy herds due to reduced milk production, reduced conception, and increased risk of mortality and culling for infected cows. Treatment protocols were developed to reflect common practices in dairy herds. These included targeted therapy following pathogen identification, and therapy without pathogen identification using a broad-spectrum antimicrobial or treating with the cheapest treatment option. The cost-benefit of J5 vaccination was also estimated. Effects of treatment were accounted for as changes in treatment costs, milk loss due to mastitis, milk discarded due to treatment, and mortality. Following ineffective treatments, secondary decisions included extending the current treatment, alternative treatment, discontinuing treatment, and pathogen identification followed by recommended treatment. Average net returns for treatment protocols and vaccination were generated using an existing dynamic programming model. This model incorporates cow and pathogen characteristics to optimize management decisions to treat, inseminate, or cull cows. Of the treatment protocols where 100% of cows received recommended treatment, pathogen-specific identification followed by recommended therapy yielded the highest average net returns per cow per year. Out of all treatment scenarios, the highest net returns were achieved with selecting the cheapest treatment option and discontinuing treatment, or alternate treatment with a similar spectrum therapy; however, this may not account for the full consequences of giving nonrecommended therapies to cows with clinical mastitis. Vaccination increased average net returns in all scenarios.

Optimal insemination and replacement decisions to minimize the cost of pathogen-specific clinical mastitis in dairy cows.

Mastitis is a serious production-limiting disease, with effects on milk yield, milk quality, and conception rate, and an increase in the risk of mortality and culling. The objective of this study was 2-fold: (1) to develop an economic optimization model that incorporates all the different types of pathogens that cause clinical mastitis (CM) categorized into 8 classes of culture results, and account for whether the CM was a first, second, or third case in the current lactation and whether the cow had a previous case or cases of CM in the preceding lactation; and (2) to develop this decision model to be versatile enough to add additional pathogens, diseases, or other cow characteristics as more information becomes available without significant alterations to the basic structure of the model. The model provides economically optimal decisions depending on the individual characteristics of the cow and the specific pathogen causing CM. The net returns for the basic herd scenario (with all CM included) were $507/cow per year, where the incidence of CM (cases per 100 cow-years) was 35.6, of which 91.8% of cases were recommended for treatment under an optimal replacement policy. The cost per case of CM was $216.11. The CM cases comprised (incidences, %) Staphylococcus spp. (1.6), Staphylococcus aureus (1.8), Streptococcus spp. (6.9), Escherichia coli (8.1), Klebsiella spp. (2.2), other treated cases (e.g., Pseudomonas; 1.1), other not treated cases (e.g., Trueperella pyogenes; 1.2), and negative culture cases (12.7). The average cost per case, even under optimal decisions, was greatest for Klebsiella spp. ($477), followed by E. coli ($361), other treated cases ($297), and other not treated cases ($280). This was followed by the gram-positive pathogens; among these, the greatest cost per case was due to Staph. aureus ($266), followed by Streptococcus spp. ($174) and Staphylococcus spp. ($135); negative culture had the lowest cost ($115). The model recommended treatment for most CM cases (>85%); the range was 86.2% (Klebsiella spp.) to 98.5% (Staphylococcus spp.). In general, the optimal recommended time for replacement was up to 5 mo earlier for cows with CM compared with cows without CM. Furthermore, although the parameter estimates implemented in this model are applicable to the dairy farms in this study, the parameters may be altered to be specific to other dairy farms. Cow rankings and values based on disease status, pregnancy status, and milk production can be extracted; these provide guidance when determining which cows to keep or cull.

A multi-level hierarchic Markov process with Bayesian updating for herd optimization and simulation in dairy cattle.

Herd optimization models that determine economically optimal insemination and replacement decisions are valuable research tools to study various aspects of farming systems. The aim of this study was to develop a herd optimization and simulation model for dairy cattle. The model determines economically optimal insemination and replacement decisions for individual cows and simulates whole-herd results that follow from optimal decisions. The optimization problem was formulated as a multi-level hierarchic Markov process, and a state space model with Bayesian updating was applied to model variation in milk yield. Methodological developments were incorporated in 2 main aspects. First, we introduced an additional level to the model hierarchy to obtain a more tractable and efficient structure. Second, we included a recently developed cattle feed intake model. In addition to methodological developments, new parameters were used in the state space model and other biological functions. Results were generated for Dutch farming conditions, and outcomes were in line with actual herd performance in the Netherlands. Optimal culling decisions were sensitive to variation in milk yield but insensitive to energy requirements for maintenance and feed intake capacity. We anticipate that the model will be applied in research and extension.

Optimal replacement policies for dairy cows based on daily yield measurements.

Markov decision processes (MDP) with finite state and action space have often been used to model sequential decision making over time in dairy herds. However, the length of each stage has been at least 1 mo, resulting in models that do not support decisions on a daily basis. The present paper describes the first step of developing an MDP model that can be integrated into a modern herd management system. A hierarchical MDP was formulated for the dairy cow replacement problem with stage lengths of 1 d. It can be used to assist the farmer in replacement decisions on a daily basis and is based on daily milk yield measurements that are available in modern milking systems. Bayesian updating was used to predict the performance of each cow in the herd and economic decisions were based on the prediction. Moreover, parameters in the model were estimated using data records of the specific herd under consideration. This includes herd-specific lactation curves.

Toward better estimates of the real-time individual amino acid requirements of growing-finishing pigs showing deviations from their typical feeding patterns.

Pigs exposed to stressors might change their daily typical feeding intake pattern. The objective of this study was to develop a method for the early identification of deviations from an individual pig's typical feeding patterns. In addition, a general approach was proposed to model feed intake and real-time individual nutrient requirements for pigs with atypical feeding patterns. First, a dynamic linear model (DLM) was proposed to model the typical daily feed intake (DFI) and daily gain (DG) patterns of pigs. Individual DFI and DG dynamics are described by a univariate DLM in conjunction with Kalman filtering. A standardized tabular cumulative sum (CUMSUM) control chart was applied to the forecast errors generated by DLM to activate an alarm when a pig showed deviations from its typical feeding patterns. The relative feed intake (RFI) during a challenge period was calculated. For that, the forecasted individual pig DFI is expressed as its highest DFI relative to the intake during pre-challenge period. Finally, the DLM and RFI approaches were integrated into the actual precision-feeding model (original model) to estimate real-time individual nutrient requirements for pigs with atypical feeding patterns. This general approach was evaluated with data from two studies (130 pigs, at 35.25 ± 3.9 kg of initial BW) that investigated during 84 days the effect of precision-feeding systems for growing-finishing pigs. The proposed general approach to estimating real-time individual nutrient requirements (updated model) was evaluated by comparing its estimates with those generated by the original model. For 11 individuals out of 130, the DLM did not fit the observed data well in a specific period, resulting in an increase in the sum of standardized forecast errors and in the number of time steps that the model needed to adapt to the new patterns. This poor fit can be identified by the increase in the CUMSUM with a consequent alarm generated. The results of this study show that the updated model made it possible to reduce intra-individual variation for the estimated lysine requirements in comparison with the original model, especially for individuals with atypical feeding patterns. In conclusion, the DLM in conjunction with CUMSUM could be used as a tool for the online monitoring of DFI for growing-finishing pigs. Moreover, the proposed general approach allows the estimation of real-time amino acid requirements and accounts for the reduced feed intake and growth potential of pigs with atypical feeding patterns.

Body composition and organ development of intra-uterine growth restricted pigs at weaning.

Sow litter sizes have increased, subjecting more small piglets to intra-uterine growth restriction (IUGR). Research on the development and growth of IUGR pigs is limited. The objective of this study was to compare the body composition and organ development of IUGR pigs at weaning, and to estimate their growth performance from birth to 30 kg. A total of 142 IUGR and 142 normal piglets were classified at birth based on their head morphology. At weaning, 20 IUGR and 20 normal piglets were collected, a whole-body dual-energy x-ray absorption scan was performed, and the piglets were euthanized for organ measurements. Body weight (BW) was measured weekly from birth to 30 kg, rectal temperature and whole-blood glucose levels were measured weekly from birth to weaning, and blood samples were collected at days 7, 14 and 21 for IGF-1 analysis. Results showed that IUGR pigs have a similar percentage of adipose tissue (P > 0.05) compared to normal pigs at 24 days of age. Organs were smaller (P < 0.001) in IUGR pigs than in normal pigs, whereas brain, liver, lungs and adrenal glands were relatively larger (P < 0.05) in relation to the BW of IUGR pigs. Average birth weight (BiW) of normal pigs was greater (P < 0.001) compared with IUGR pigs (1.38 v. 0.75 kg), and the average daily gain (ADG) of IUGR pigs was reduced from day 0 to 14, day 0 to 28 (weaning) and from weaning to 30 kg compared to normal pigs. From birth to weaning at day 28, IUGR piglets had a 72.9 g/day greater fractional ADG (FADG) in relation to their BiW (P < 0.05), but FADG did not differ (P > 0.05) from weaning to 30 kg. Rectal temperature of IUGR piglets was greater (P < 0.05) on day 7 compared with normal piglets, and, even though blood glucose levels were decreased (P < 0.001) in IUGR piglets at day 0, neither glucose nor IGF-1 concentrations differed (P > 0.05) between IUGR and normal piglets. In conclusion, IUGR piglets exhibited some relatively larger organs at weaning compared to normal pigs, but body composition was similar between IUGR and normal pigs. In addition, IUGR pigs had a reduced ADG from birth to 30 kg, and, although they exhibited a greater FADG during nursing, IUGR pigs still require six additional days to reach a BW of 30 kg in comparison to normal pigs.

Analyses of body weight patterns in growing pigs: a new view on body weight in pigs for frequent monitoring.

Frequent BW monitoring of growing pigs can be useful for identifying production (e.g. feeding), health and welfare problems. However, in order to construct a tool which will properly recognize abnormalities in pigs' growth a precise description of the growth process should be used. In this study we proposed a new model of pig growth accounting for daily fluctuations in BW. Body weight measurements of 1710 pigs (865 gilts and 843 barrows) originating from five consecutive batches from a Danish commercial farm were collected. Pigs were inserted into a large pen (maximum capacity=400) between November 2014 and September 2015. On average, each pig was observed for 42 days and weighed 3.6 times a day when passing from the resting to feeding area. Altogether, 243,160 BW measurements were recorded. A multilevel model of pig growth was constructed and fitted to available data. The BW of pigs was modeled as a quadratic function of time. A diurnal pattern was incorporated into the model by a cosine wave with known length (24 h). The model included pig effect which was defined as a random autoregressive process with exponential correlation. Variance of within-pigs error was assumed to increase with time. Because only five batches were observed, it was not possible to obtain the random effect for batch. However, in order to account for the batch effect the model included interactions between batch and fixed parameters: intercept, time, square value of time and cosine wave. The gender effect was not significant and was removed from the final model. For all batches, morning and afternoon peaks in the frequency of visits to the feeding area could be distinguished. According to results, pigs were lighter in the morning and heavier in the evening (minimum BW was reached around 1000 h and maximum around 2200 h). However, the exact time of obtaining maximum and minimum BW during the day differed between batches. Pigs had access to natural light and, therefore, existing differences could be explained by varying daylight level during observations periods. Because the diurnal amplitude for pig growth varied between batches from 0.9 to 1.4 kg, BW monitoring tools based on frequent measurements should account for diurnal variation in BW of pigs. This proposed description of growth will be built into a monitoring tool (a dynamic linear model) and applied to farm data in future studies.

Monitoring growth in finishers by weighing selected groups of pigs - A dynamic approach.

Application of BW monitoring methods for the whole batch of pigs is not common in commercial herds. Instead, farm managers may regularly weigh a chosen subset of pigs (observed group) and use the obtained information for monitoring, forecasting, and decision support. The objective of this study was to construct a model for growth monitoring and forecasting in pig fattening herds and use the developed model framework to quantify the value of information on BW. The dynamic process of pig growing was described by means of a dynamic linear model (DLM) with Kalman filtering. For this study, data from 9 fattening cycles with the total registration for 9,800 pigs were used. The variance components were estimated by fitting a mixed-effects linear model on selected BW measurements. The obtained model was evaluated on its performance in forecasting the number of pigs ready to deliver from the whole batch and from a particular pen given the level of information on a reference data set consisting of 2 batches (Batch 3 [B1] and Batch 4 [B2]). Scenarios with a different frequency of observations (only 1 selected week, every second week, or weekly) on individual and aggregated levels for an observed group comprising 1 pen (36 pigs, which constitute 7.5% of pigs in a batch) or 2 pens (15.5% of pigs) were analyzed. Moreover, results with only initial herd information and insertion BW at the batch, pen, and pig level were presented. The model can be used for growth monitoring of the batch and for prediction of the number of pigs ready for slaughter in a given week (i.e., with a BW exceeding a threshold, which, in this study, is set to 105 kg). With an increased level of information, both accuracy (measured by the mean absolute deviation [MAD] of actual number of pigs above 105 kg from predicted number) and precision (measured by CV) of the model continue to improve. When monitoring all pigs at insertion and the observed groups every week (15.5% of pigs) compared with predictions based on only initial herd information, the MAD between the observed and predicted number of pigs above 105 kg in a single pen decreased by 1.4 and 2 pigs whereas CV was reduced by 147 and 78% for B1 and B2, respectively. The DLM was able to detect variation between pens already at insertion; therefore, data on initial BW had high value for the prediction procedure. Moreover, the aggregation had a marginal effect on model performance.

A comparison of Echinostoma caproni and Echinostoma trivolvis (Trematoda: Echinostomatidae) adults using isoelectrofocusing.

An isoenzymatic analysis using thin-layer agarose gel isoelectrofocusing on laboratory strains of Echinostoma trivolvis and Echinostoma caproni adults showed characteristic monomorphic phenotypes for phosphoglucomutase and glucose phosphate isomerase. The fixed allelic variation observed between these 2 taxa is consistent with their current classification as distinct species.

[Isoenzymatic studies of Mediterranean populations of sandflies of the subgenus Larroussius]. / Etude isoenzymatique de populations Mediterraneennes de phlebotomes du sous-genre Larroussius.

Isoenzymes of hexokinase, phosphoglucomutase and glucosephosphate isomerase were examined, using agarose gel isoelectrofocusing, in populations of Phlebotomus ariasi (France), P. neglectus (Greece), P. perfilewi (Italy & Greece) and P. tobbi (Greece). Comparison between enzymatic profiles by numerical analysis separated Larroussius from Phlebotomus and Paraphlebotomus subgenera and brought together P. ariasi and P. neglectus. Nei genetic coefficients indicated similarities between populations of P. perfiliewi from Corfu and central Italy and between Corfuan and Cretan P. neglectus samples.

Optimal management of replacement heifers in a beef herd: a model for simultaneous optimization of rearing and breeding decisions.

The aim of this study was to provide farmers an efficient tool for supporting optimal decisions in the beef heifer rearing process. The complexity of beef heifer management prompted the development of a model including decisions on the feeding level during prepuberty (age <10 mo), the time of weaning (age, BW, calendar month), the feeding level during the reproductive period (age ≥10 mo), and time of breeding (age, BW, and calendar month). The model was formulated as 3-level hierarchic Markov process. A founder level of the model has 12 states resembling all possible birth months of a heifer. Based on the birth month information from the founder level, for the indoor season (November to April) and outdoor season (May to October), feeding and breeding costs (natural service cost in the outdoor and AI cost in the indoor season) were applied. The optimal rearing strategy was found by maximizing the total discounted net revenues from the predicted future productivity of the Polish Limousine heifers defined as the cumulative BW of calves born from a cow calved until the age of 5 yr, standardized on the 210th day of age. According to the modeled optimal policy, heifers fed during the whole rearing period at the ADG of 810 g/d and generally weaned after the maximum suckling period of 9 mo should already be bred at the age of 13.2 mo and BW constituting 55.6% of the average mature BW. Based on the optimal strategy, 52% of all heifers conceived from May to July and calved from February to April. This optimal rearing pattern resulted in an average net return of EUR 311.6 per pregnant heifer. It was found that the economic efficiency of beef operations can be improved by applying different herd management practices to those currently used in Poland. Breeding at 55.6% of the average mature BW, after a shorter and less expensive rearing period, resulted in an increase in the average net return per heifer by almost 18% compared to the conventional system, in which heifers were bred after attaining 65% of the mature BW. Extension of the rearing period by 2.5 mo (breeding at the age 15.7 mo), due to a prepubertal growth rate lowered by 200 g, reduced the average net return per heifer by 6.2% compared to the results obtained under the basic model assumptions. In the future, the model may also be extended to investigate additional aspects of the beef heifer development, such as the environmental impacts of various heifer management decisions.

Dynamic modelling of a traditional African chicken production system.

A dynamic stochastic model, SimFlock, was developed with the purpose of simulating the dynamics of a traditional African chicken flock at smallholder level. The model simulates outputs, e.g. total number of chickens produced, egg production and net return after entering flock, or area-specific biological parameters. Biological parameters include hatchability, egg production, growth and survival of various age groups, full-grown weights as well as some decision parameters like farmers' need for meat consumption, and slaughtering and selling age of chickens. A simulation job is defined by 'states of nature' (biological parameters specified directly or drawn at random from the hyperparameters), number of replications and number of days to be simulated. Three simulation jobs were selected to demonstrate the use of the model: one with the default settings; one with collection of eggs for 20 days; and one in which growth rate was increased to double. After simulation, the results of each job were compared in relation to total number and weight of chickens produced, egg production and net return. SimFlock could be used as a management or extension tool to identify the biological parameters of major influence on production. Improvement of these parameters will have the largest impact on output.

A stochastic model simulating the feeding-health-production complex in a dairy herd.

A dynamic, stochastic, and mechanistic Monte Carlo model, simulating a dairy herd with focus on the feeding-health-production complex is presented. By specifying biological parameters at cow level and a management strategy at herd level, the model can simulate the technical and economic consequences of scenarios at herd level. The representation of the feeding-health-production complex is aimed to be sufficiently detailed, to include relationships likely to cause significant herd effects, and to be sufficiently simple to enable a feasible parameterization of the model and interpretation of the results from the model. Consequently, diseases are defined as four disease types: two metabolic disease types, an udder disease type, and a reproductive disease type. Risk factors for the diseases were defined as parity, yield capacity, disease recurrence, disease interrelationships, lactation stage, and season. Direct effects of the diseases were defined according to milk yield, feed intake, feed utilization, conception, culling, involuntary removal, and death. Scenarios differing in base risks of milk fever and ketosis, heat detection rate, and culling strategy were simulated for describing the model behavior. Annual milk yield per cow was decreased by increased risk of ketosis and by increased risk of milk fever, even though no direct effect of milk fever on milk yield was modeled at the cow level. The indirect effect from milk fever is a consequence of increased replacement rate (relatively lower milk yield from younger cows). By ignoring the history of milk fever in insemination and replacement decisions, a significantly reduced net income per cow was found in some herds. We concluded that important benefits from using such a herd model are the capability of accounting for herd management factors and the advantage of avoiding to double count the indirect effects from disease, such as increased risk of other diseases, poorer reproduction results, and increased risk of culling and death.

Effects of lithium and neuroleptics and combinations of the two on renal function and structure in rats.

The effects of 8 weeks of treatment with lithium and neuroleptics, alone and combined, on renal concentrating ability and morphology were studied in rats. LiCl was administered in the diet and neuroleptics were given as one daily dose: haloperidol 1 mg/kg, chlorpromazine 15 mg/kg, and perphenazine 4 mg/kg. Plasma lithium levels were about 1 mmol/l, and the area under the plasma concentration curve was not statistically different in the control and neuroleptic groups. Rats treated with lithium developed marked polyuria which was less in rats receiving neuroleptics concomitantly. After 8 weeks, rats treated with lithium alone showed marked impairment of renal concentrating ability and moderate degree of structural renal changes. Neuroleptics alone had no effect on concentrating ability or renal morphology, nor did they aggravate the changes caused by lithium. In fact, neuroleptics seemed to improve the concentrating ability in rats treated with lithium. It is concluded that in rats high doses of neuroleptics do not potentiate lithium-induced functional and structural renal changes.

Chlorpromazine increases the lowered response to antidiuretic hormone in rats with lithium-induced diabetes insipidus.

The interaction between chlorpromazine (CPZ) and lithium on renal concentrating ability was studied in rats fed a Li-containing diet for 8 weeks (plasma-Li 0.6-0.7 mmol/l). CPZ (15 mg/kg daily orally) reduced the polydipsia and increased the ability to concentrate the urine upon water deprivation in rats treated with lithium. CPZ also reduced systolic blood pressure, but had no effect on the glomerular filtration rate or plasma levels of arginine vasopressin (AVP) in hydrated rats treated with lithium. However, CPZ prevented the rise in plasma AVP levels observed in lithium-polyuric rats in response to dehydration. During anaesthesia CPZ partially restored the impaired anti-diuretic response to exogenous AVP in rats treated with lithium. CPZ had no influence on plasma-Li levels in rats treated with lithium. It is suggested that CPZ by unknown mechanisms interferes with the effects of lithium on the water permeability response to AVP.

Optimal replacement of mastitic cows determined by a hierarchic Markov process.

Farmers frequently have to decide whether to keep or to replace cows that suffer from clinical mastitis. A dynamic programming model was developed to optimize these decisions for individual cows within the herd, using the hierarchic Markov process technique. This technique provides a method to model a wide variety of cows, differing in age, productive performance, reproductive status, and clinical mastitis occurrence. The model presented was able to support decisions related to 63% of all replacements. Results--for Dutch conditions--showed the considerable impact of mastitis on expected income of affected cows. Nevertheless, in most cases, the optimal decision was to keep and to treat rather than to replace the cow. Clinical mastitis occurring in the previous lactation negligibly influence expected income. Clinical mastitis in current lactation, especially in the current month, however, had a significant effect on expected income. Total losses caused by clinical mastitis were US$83/yr per cow. Farm level treatment, which reduced incidence by 25%, on a farm with 10 clinical quarter cases per 10,000 cow days, may cost at maximum US$27/yr per cow.

Correlation between distal nephron enzyme activity, structure and function in rats during lithium and lithium plus neuroleptic treatment.

The histochemical activities of nonspecific acid and alkaline phosphatases, NADH- and NADPH-tetrazolium reductases, alpha-glycerophosphate dehydrogenase, succinate dehydrogenase, isocitrate dehydrogenase, lactate dehydrogenase and glucose-6-phosphate dehydrogenase were investigated in kidneys from rats treated with lithium and lithium plus neuroleptics. During the first 8 weeks of lithium treatment the activity of NADH-tetrazolium reductase, succinate dehydrogenase and alpha-glycerophosphate dehydrogenase activity in the collecting ducts increased. The other enzymes did not change. After 8 weeks of treatment no further changes in enzyme activity occurred. Withdrawal of lithium caused normalization of enzyme activity after 8 weeks. A decrease in concentration ability was found in parallel with the increase in enzyme activities (p less than 0.001). The changes in enzyme activity were not significantly correlated to morphological changes in the collecting ducts. Treatment with neuroleptics alone caused no change in enzyme activity. During combined lithium plus neuroleptic treatment the enzyme activities changed in a similar way as during lithium therapy, but the changes were less pronounced. In parallel, a less pronounced decrease in concentration ability was found during this treatment.