Background and Aim:Calfhood disease is an important problem in dairy farming that could cause significant effects on heifer survival and productivity and has economic and welfare effects. Total protein concentration in the blood serum could be one of the predictors of bovine respiratory disease (BRD) in newborn calves. The number of active nucleolus organizers could be used to assess the viability of the protein synthesis system in cells and tissues. We aimed for a comparative assessment of the dynamics of the main indicators of protein metabolism and nucleolus organizer regions (NORs) activity in the lymphocytes of healthy calves (Group I) and calves with BRD (Group II) during the 1st month after birthMaterials and Methods:This study included 30 calves of the red-motley Holstein breed. Venous blood samples were taken from all calves on the 1st, 7th, 14th, and 28th days after birth. Quantitative analysis of total protein (Serum total protein [STP]), immune globulin (Serum immune globulin [SIg]), urea, and creatinine in serum and transcriptionally active chromosome NORs in the interphase nuclei of lymphocytes was conducted using receiver operating characteristic analysis and factor analysis.Results:In Group I, the STP levels decreased during the 1st month of life, and in Group II, the STP levels were variable. The STP levels in both groups remained within the reference intervals. During the first 2 weeks after birth, the calves’ SIg fluctuated within the statistical error limits and did not significantly differ between the groups. On the 28th day, SIg increased in both the groups (by 42.8% for Group I and 33.7% for Group II). The creatinine concentration showed a decrease but did not go beyond the range of reference values. Urea concentration in Group I markedly decreased and remained below the reference values; it did not change in Group II over the entire observation period. The number of NORs in 1-day-old calves did not significantly differ between the groups and amounted to 2.43 in Group I and 2.59 in Group II. A significant increase in the number of active NORs was found in calves in both groups at the ages of 14 and 28 days. Early BRD predictors (at 1-14 days) could not be identified among the studied indicators. The urea and creatinine concentrations and the NOR activity on day 28 after birth could be late BRD predictors. Protein metabolism in the newborn calves’ organisms is regulated by three types of factors: Maintenance of a constant protein concentration in the plasma, protein decomposition, and de novo synthesis.Conclusion:There were no observed significant differences in the protein metabolism values and dynamics of indicators between healthy calves and calves with developed BRD. Alterations in the studied characteristics are the result, but not the cause of BRD. The increase in active NORs under BRD could be a favorable forecasting indicator. Protection against foreign protein and genetic material is a more important task for the organism than ensuring growth processes during the neonatal period.

One of the major challenges the oil and gas industry faces is the enablement of fast and seamless multi-disciplinary integration across the reservoir, production, network and facilities. Integrated Asset Management (IAM) is a key concept for making critical decisions about assets development and to maximise asset value. Although the IAM concept has been used in the past, it's tough to implement due to its inherent complexities. This paper introduces the latest technical innovations and processes that make IAM approach practical and reliable for its implementation. This innovative solution offers flexibility to rapidly adjust the model through "automatic and fast model updates" and provides a fit-for-purpose integrated model. The solution improves the speed and accuracy of decisions, and assists in field development planning workflows, modelling operational challenges and addressing debottlenecking options while considering all the domain constraints and development scenarios. Integrated asset modelling methodology used in this paper, is flexible in capturing domain science at different fidelity levels, incorporating fit for purpose physics, ranging from analytical models to highly complex reservoir simulation models with high-resolution grids that capture geological complexity. Operational logic design and decision cycle implementation for production forecasting are leveraged from an implicitly coupled scheduler, referred to as "Field management". Surface network integration in the integrated asset model is flexible and dependent on the level of fidelity needed – with complete control of all entities inside the surface network model provided by the "Field management" system. Optimisation capabilities provided with the "Field management" system allows for "automatic updates" to entities in the production system to optimise the recovery. "Model updates" pertinent to production data updates is driven by the "rapid model update" technique. Flexible coupling techniques and niche optimisation capabilities offered by the "Field management" scheduler between the different domain models enable optimising the asset's production with implicit operational constraints. The superior performance offered through state-of-the-art solvers, fit for purpose fidelity and parallel scalability offers a practical advantage to this integrated asset management approach. "Rapid model update" workflow allows for seamless and fast integration of production data updates within the integrated asset model, thereby keeping the model in an "evergreen" state, reflective of the subsurface dynamics and operational changes. This paper provides the most practical solution for Integrated asset modelling implementation that provides flexibility to balance between performance and fidelity by leveraging the latest technological advancements and workflows. It is the first solution that offers an optimisation technique capable of "rapid and automatic model updates" and python extensibility to achieve realistic field planning forecasts.

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