Using state space models to monitor and estimate the effects of interventions on treatment risk and milk yield in dairy farms.

Fast, flexible, and internally valid analytical tools are needed to evaluate the effects of management interventions made on dairy farms to support decisions about which interventions to continue or discontinue. The objective of this observational study was to demonstrate the use of state space models (SSM) to monitor and estimate the effect of interventions on 2 specific outcomes: a dynamic linear model (DLM) evaluating herd-level milk yield and a dynamic generalized linear model evaluating treatment risk in a pragmatic pretest/posttest design under field conditions. This demonstration study is part of a Danish common learning project that ran from March 2020 to May 2021 within the framework of veterinary herd health consultancy in relation to reducing antimicrobial use and improving herd health.

Glucose Injections at Birth, Warmth and Placing at a Nurse Sow Improve the Growth of IUGR Piglets.

Intrauterine growth-restricted piglets (IUGR) have a lower rectal temperature, whole-blood glucose, and lower glycogen storages at birth than normal piglets, giving them less energy to maintain body temperature and compete at the udder. The present paper investigated the effects of giving an energy supplementation three times after birth on rectal temperature, glucose levels, and growth until weaning in an on-farm trial. Eighty-eight newborn piglets were classified as IUGR (based on head morphology), placed under a heating lamp for one hour and allocated to one of four treatments-warmed water (WATER), glucose injection (GLUC), colostrum bolus (COLOS; porcine colostrum), and colostrum bolus and glucose injection (GLUC + COLOS)-before being placed at a nursing sow.

Multivariate dynamic linear models for estimating the effect of experimental interventions in an evolutionary operations setup in dairy herds.

Evolutionary operations is a method to exploit the association of often small changes in process variables, planned during systematic experimentation and occurring during the normal production flow, to production characteristics to find a way to alter the production process to be more efficient. The objective of this study was to construct a tool to assess the intervention effect on milk production in an evolutionary operations setup. The method used for this purpose was a dynamic linear model (DLM) with Kalman filtering.

The value of pathogen information in treating clinical mastitis.

The objective of this study was to determine the economic value of obtaining timely and more accurate clinical mastitis (CM) test results for optimal treatment of cows. Typically CM is first identified when the farmer observes recognisable outward signs. Further information of whether the pathogen causing CM is Gram-positive, Gram-negative or other (including no growth) can be determined by using on-farm culture methods.

Oncofetal Chondroitin Sulfate Glycosaminoglycans Are Key Players in Integrin Signaling and Tumor Cell Motility.

Unlabelled: Many tumors express proteoglycans modified with oncofetal chondroitin sulfate glycosaminoglycan chains (ofCS), which are normally restricted to the placenta. However, the role of ofCS in cancer is largely unknown. The function of ofCS in cancer was analyzed using the recombinant ofCS-binding VAR2CSA protein (rVAR2) derived from the malaria parasite, Plasmodium falciparum We demonstrate that ofCS plays a key role in tumor cell motility by affecting canonical integrin signaling pathways.

From biological models to economic optimization.

This article addresses the additional challenges being faced when biological models are used as a basis for decision support in livestock herds. The challenges include dealing with uncertain information, observation costs, herd dynamics and methodological issues in relation to the computational methods applied particularly in the dynamic case. The desired key property of information included in models is that it can be used as the basis for unbiased prediction of the future performance of the animals.

Development of a computational framework for the analysis of protein correlation profiling and spatial proteomics experiments.

Unlabelled: Standard approaches to studying an interactome do not easily allow conditional experiments but in recent years numerous groups have demonstrated the potential for co-fractionation/co-migration based approaches to assess an interactome at a similar sensitivity and specificity yet significantly lower cost and higher speed than traditional approaches. Unfortunately, there is as yet no implementation of the bioinformatics tools required to robustly analyze co-fractionation data in a way that can also integrate the valuable information contained in biological replicates. Here we have developed a freely available, integrated bioinformatics solution for the analysis of protein correlation profiling SILAC data.

Protein correlation profiling-SILAC to study protein-protein interactions.

An interactome describes the global organization of protein interactions within a cell and is typically generated using affinity purification-mass spectrometry (AP-MS), yeast two-hybrid screening, or protein-fragment complementation assays (Gavin et al. Nature 440: 631-636, 2006; Krogan et al. Nature 440: 637-643, 2006; Uetz et al.

Characterization of early autophagy signaling by quantitative phosphoproteomics.

Under conditions of nutrient shortage autophagy is the primary cellular mechanism ensuring availability of substrates for continuous biosynthesis. Subjecting cells to starvation or rapamycin efficiently induces autophagy by inhibiting the MTOR signaling pathway triggering increased autophagic flux. To elucidate the regulation of early signaling events upon autophagy induction, we applied quantitative phosphoproteomics characterizing the temporal phosphorylation dynamics after starvation and rapamycin treatment.

Protein synthesis rate is the predominant regulator of protein expression during differentiation.

External perturbations, by forcing cells to adapt to a new environment, often elicit large-scale changes in gene expression resulting in an altered proteome that improves the cell's fitness in the new conditions. Steady-state levels of a proteome depend on transcription, the levels of transcripts, translation and protein degradation but system-level contribution that each of these processes make to the final protein expression change has yet to be explored. We therefore applied a systems biology approach to characterize the regulation of protein expression during cellular differentiation using quantitative proteomics.

High throughput strategies for probing the different organizational levels of protein interaction networks.

Most proteins do not exist as isolated molecules in the cell, but instead serve as nodes of protein interaction networks. A number of techniques have been developed in the last two decades to study protein interaction networks at different levels of detail. Here we describe some of the techniques for characterizing protein interactions and protein complexes on a system-wide scale, focusing especially on newly emerging techniques that use co-migration.

Association of connexin43 with E3 ubiquitin ligase TRIM21 reveals a mechanism for gap junction phosphodegron control.

Gap junctions (GJs) are sites of direct cell-to-cell communication formed by the connexin (Cx) family of ion channel proteins. The aberrant intercellular communication mediated by GJs is associated with a variety of hereditary and acquired human diseases. GJs utilize a highly interconnected network that is indispensible for synthesis, trafficking and degradation of their constituent proteins.

A high-throughput approach for measuring temporal changes in the interactome.

Interactomes are often measured using affinity purification-mass spectrometry (AP-MS) or yeast two-hybrid approaches, but these methods do not provide stoichiometric or temporal information. We combine quantitative proteomics and size-exclusion chromatography to map 291 coeluting complexes. This method allows mapping of an interactome to the same depth and accuracy as AP-MS with less work and without overexpression or tagging.

Identification of autophagosome-associated proteins and regulators by quantitative proteomic analysis and genetic screens.

Autophagy is one of the major intracellular catabolic pathways, but little is known about the composition of autophagosomes. To study the associated proteins, we isolated autophagosomes from human breast cancer cells using two different biochemical methods and three stimulus types: amino acid deprivation or rapamycin or concanamycin A treatment. The autophagosome-associated proteins were dependent on stimulus, but a core set of proteins was stimulus-independent.

Herd and sow-related risk factors for mortality in sows in group-housed systems.

Mortality of sows is a major problem for pig production worldwide. In this study, we used hierarchical multivariable logistic analyses to investigate different risk factors for mortality at the sow and herd level in herds with group-housed pregnant sows. Data included 3652 pregnant and 1266 lactating sows from 34 sow herds.

Quantitative proteomics identifies ferritin in the innate immune response of C. elegans.

When encountering a pathogen, all organisms evoke a protective response by inducing defense mechanisms to help fight off the invader. The invertebrate model organism Caenorhabditis elegans has proven to be valuable for studies of the host response and the small nematode mounts a substantial transcriptional response to numerous pathogens. Here, we use global quantitative proteomics to profile the response to infection with E.

A modelling approach to support dynamic decision-making in the control of FMD epidemics.

Most studies on control strategies for contagious diseases such as foot-and-mouth disease (FMD) evaluate pre-defined control strategies and imply static decision-making during epidemic control. Such a static approach contradicts the dynamic nature of the decision-making process during epidemic control. This paper presents an integrated epidemic-economic modelling approach to support dynamic decision-making in controlling FMD epidemics.

Modelling the economic impact of three lameness causing diseases using herd and cow level evidence.

Diseases to the cow's hoof, interdigital skin and legs are highly prevalent and of large economic impact in modern dairy farming. In order to support farmer's decisions on preventing and treating lameness and its underlying causes, decision support models can be used to predict the economic profitability of such actions. An existing approach of modelling lameness as one health disorder in a dynamic, stochastic and mechanistic simulation model has been improved in two ways.

Estimation of probability for the presence of claw and digital skin diseases by combining cow- and herd-level information using a Bayesian network.

Cross sectional data on the prevalence of claw and (inter) digital skin diseases on 4854 Holstein Friesian cows in 50 Danish dairy herds was used in a Bayesian network to create herd specific probability distributions for the presence of lameness causing diseases. Parity and lactation stage are identified as risk factors on cow level, for the prevalence of the three lameness causing diseases digital dermatitits, other infectious diseases and claw horn diseases. Four herd level risk factors have been identified; herd size, the use of footbaths, a grazing strategy and total mixed ration.

An object-oriented Bayesian network modeling the causes of leg disorders in finisher herds.

The implementation of an effective control strategy against disease in a finisher herd requires knowledge regarding the disease level in the herd. A Bayesian network was constructed that can estimate risk indexes for three cause-categories of leg disorders in a finisher herd. The cause-categories of leg disorders were divided into infectious causes (arthritis caused by infectious pathogens), physical causes (e.

Ordered bulk degradation via autophagy.

During amino acid starvation, cells undergo macroautophagy which is regarded as an unspecific bulk degradation process. Lately, more and more organelle-specific autophagy subtypes such as reticulophagy, mitophagy and ribophagy have been described and it could be shown, depending on the experimental setup, that autophagy specifically can remove certain subcellular components. We used an unbiased quantitative proteomics approach relying on stable isotope labeling by amino acids in cell culture (SILAC) to study global protein dynamics during amino acid starvation-induced autophagy.

Ordered organelle degradation during starvation-induced autophagy.

Upon starvation cells undergo autophagy, a cellular degradation pathway important in the turnover of whole organelles and long lived proteins. Starvation-induced protein degradation has been regarded as an unspecific bulk degradation process. We studied global protein dynamics during amino acid starvation-induced autophagy by quantitative mass spectrometry and were able to record nearly 1500 protein profiles during 36 h of starvation.

Identification of cognate host targets and specific ubiquitylation sites on the Salmonella SPI-1 effector SopB/SigD.

Salmonella enterica is a bacterial pathogen responsible for enteritis and typhoid fever. Virulence is linked to two Salmonella pathogenicity islands (SPI-1 and SPI-2) on the bacterial chromosome, each of which encodes a type III secretion system. While both the SPI-1 and SPI-2 systems secrete an array of effectors into the host, relatively few host proteins have been identified as targets for their effects.