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The safe upper limit for inclusion of vitamin A in complete diets for growing dogs is uncertain, with the result that current recommendations range from 5.24 to 104.80 mu mol retinol (5000 to 100 000 IU vitamin A)/4184 kJ (1000 kcal) metabolisable energy (ME). The aim of the present study was to determine the effect of feeding four concentrations of vitamin A to puppies from weaning until 1 year of age. A total of forty-nine puppies, of two breeds, Labrador Retriever and Miniature Schnauzer, were randomly assigned to one of four treatment groups. Following weaning at 8 weeks of age, puppies were fed a complete food supplemented with retinyl acetate diluted in vegetable oil and fed at 1ml oil/100 g diet to achieve an intake of 5.24, 13.10, 78.60 and 104.80 mu mol retinol (5000, 12 500, 75 000 and 100 000 IU vitamin A)/4184 kJ (1000 kcal) ME. Fasted blood and urine samples were collected at 8, 10, 12, 14, 16, 20, 26, 36 and 52 weeks of age and analysed for markers of vitamin A metabolism and markers of safety including haematological and biochemical variables, bone-specific alkaline phosphatase, cross-linked carboxyterminal telopeptides of type I collagen and dual-energy X-ray absorptiometry. Clinical examinations were conducted every 4 weeks. Data were analysed by means of a mixed model analysis with Bonferroni corrections for multiple endpoints. There was no effect of vitamin A concentration on any of the parameters, with the exception of total serum retinyl esters, and no effect of dose on the number, type and duration of adverse events. We therefore propose that 104.80 mu mol retinol (100 000 IU vitamin A)/4184 kJ (1000 kcal) is a suitable safe upper limit for use in the formulation of diets designed for puppy growth.
Dynamic regulatory on/off minimization for biological systems under internal temporal perturbations
(2012)
Background: Flux balance analysis (FBA) together with its extension, dynamic FBA, have proven instrumental for analyzing the robustness and dynamics of metabolic networks by employing only the stoichiometry of the included reactions coupled with adequately chosen objective function. In addition, under the assumption of minimization of metabolic adjustment, dynamic FBA has recently been employed to analyze the transition between metabolic states.
Results: Here, we propose a suite of novel methods for analyzing the dynamics of (internally perturbed) metabolic networks and for quantifying their robustness with limited knowledge of kinetic parameters. Following the biochemically meaningful premise that metabolite concentrations exhibit smooth temporal changes, the proposed methods rely on minimizing the significant fluctuations of metabolic profiles to predict the time-resolved metabolic state, characterized by both fluxes and concentrations. By conducting a comparative analysis with a kinetic model of the Calvin-Benson cycle and a model of plant carbohydrate metabolism, we demonstrate that the principle of regulatory on/off minimization coupled with dynamic FBA can accurately predict the changes in metabolic states.
Conclusions: Our methods outperform the existing dynamic FBA-based modeling alternatives, and could help in revealing the mechanisms for maintaining robustness of dynamic processes in metabolic networks over time.
Cell-level kinetic models for therapeutically relevant processes increasingly benefit the early stages of drug development. Later stages of the drug development processes, however, rely on pharmacokinetic compartment models while cell-level dynamics are typically neglected. We here present a systematic approach to integrate cell-level kinetic models and pharmacokinetic compartment models. Incorporating target dynamics into pharmacokinetic models is especially useful for the development of therapeutic antibodies because their effect and pharmacokinetics are inherently interdependent. The approach is illustrated by analysing the F(ab)-mediated inhibitory effect of therapeutic antibodies targeting the epidermal growth factor receptor. We build a multi-level model for anti-EGFR antibodies by combining a systems biology model with in vitro determined parameters and a pharmacokinetic model based on in vivo pharmacokinetic data. Using this model, we investigated in silico the impact of biochemical properties of anti-EGFR antibodies on their F(ab)-mediated inhibitory effect. The multi-level model suggests that the F(ab)-mediated inhibitory effect saturates with increasing drug-receptor affinity, thereby limiting the impact of increasing antibody affinity on improving the effect. This indicates that observed differences in the therapeutic effects of high affinity antibodies in the market and in clinical development may result mainly from Fc-mediated indirect mechanisms such as antibody-dependent cell cytotoxicity.
This study provides a detailed analysis of the mid-Holocene to present-day precipitation change in the Asian monsoon region. We compare for the first time results of high resolution climate model simulations with a standardised set of mid-Holocene moisture reconstructions. Changes in the simulated summer monsoon characteristics (onset, withdrawal, length and associated rainfall) and the mechanisms causing the Holocene precipitation changes are investigated. According to the model, most parts of the Indian subcontinent received more precipitation (up to 5 mm/day) at mid-Holocene than at present-day. This is related to a stronger Indian summer monsoon accompanied by an intensified vertically integrated moisture flux convergence. The East Asian monsoon region exhibits local inhomogeneities in the simulated annual precipitation signal. The sign of this signal depends on the balance of decreased pre-monsoon and increased monsoon precipitation at mid-Holocene compared to present-day. Hence, rainfall changes in the East Asian monsoon domain are not solely associated with modifications in the summer monsoon circulation but also depend on changes in the mid-latitudinal westerly wind system that dominates the circulation during the pre-monsoon season. The proxy-based climate reconstructions confirm the regional dissimilarities in the annual precipitation signal and agree well with the model results. Our results highlight the importance of including the pre-monsoon season in climate studies of the Asian monsoon system and point out the complex response of this system to the Holocene insolation forcing. The comparison with a coarse climate model simulation reveals that this complex response can only be resolved in high resolution simulations.
The distinctness of, and overlap between, pea genotypes held in several Pisum germplasm collections has been used to determine their relatedness and to test previous ideas about the genetic diversity of Pisum. Our characterisation of genetic diversity among 4,538 Pisum accessions held in 7 European Genebanks has identified sources of novel genetic variation, and both reinforces and refines previous interpretations of the overall structure of genetic diversity in Pisum. Molecular marker analysis was based upon the presence/absence of polymorphism of retrotransposon insertions scored by a high-throughput microarray and SSAP approaches. We conclude that the diversity of Pisum constitutes a broad continuum, with graded differentiation into sub-populations which display various degrees of distinctness. The most distinct genetic groups correspond to the named taxa while the cultivars and landraces of Pisum sativum can be divided into two broad types, one of which is strongly enriched for modern cultivars. The addition of germplasm sets from six European Genebanks, chosen to represent high diversity, to a single collection previously studied with these markers resulted in modest additions to the overall diversity observed, suggesting that the great majority of the total genetic diversity collected for the Pisum genus has now been described. Two interesting sources of novel genetic variation have been identified. Finally, we have proposed reference sets of core accessions with a range of sample sizes to represent Pisum diversity for the future study and exploitation by researchers and breeders.
Background
High blood glucose and diabetes are amongst the conditions causing the greatest losses in years of healthy life worldwide. Therefore, numerous studies aim to identify reliable risk markers for development of impaired glucose metabolism and type 2 diabetes. However, the molecular basis of impaired glucose metabolism is so far insufficiently understood. The development of so called 'omics' approaches in the recent years promises to identify molecular markers and to further understand the molecular basis of impaired glucose metabolism and type 2 diabetes. Although univariate statistical approaches are often applied, we demonstrate here that the application of multivariate statistical approaches is highly recommended to fully capture the complexity of data gained using high-throughput methods.
Methods
We took blood plasma samples from 172 subjects who participated in the prospective Metabolic Syndrome Berlin Potsdam follow-up study (MESY-BEPO Follow-up). We analysed these samples using Gas Chromatography coupled with Mass Spectrometry (GC-MS), and measured 286 metabolites. Furthermore, fasting glucose levels were measured using standard methods at baseline, and after an average of six years. We did correlation analysis and built linear regression models as well as Random Forest regression models to identify metabolites that predict the development of fasting glucose in our cohort.
Results
We found a metabolic pattern consisting of nine metabolites that predicted fasting glucose development with an accuracy of 0.47 in tenfold cross-validation using Random Forest regression. We also showed that adding established risk markers did not improve the model accuracy. However, external validation is eventually desirable. Although not all metabolites belonging to the final pattern are identified yet, the pattern directs attention to amino acid metabolism, energy metabolism and redox homeostasis.
Conclusions
We demonstrate that metabolites identified using a high-throughput method (GC-MS) perform well in predicting the development of fasting plasma glucose over several years. Notably, not single, but a complex pattern of metabolites propels the prediction and therefore reflects the complexity of the underlying molecular mechanisms. This result could only be captured by application of multivariate statistical approaches. Therefore, we highly recommend the usage of statistical methods that seize the complexity of the information given by high-throughput methods.
The development of infrared observational facilities has revealed a number of massive stars in obscured environments throughout the Milky Way and beyond. The determination of their stellar and wind properties from infrared diagnostics is thus required to take full advantage of the wealth of observations available in the near and mid infrared. However, the task is challenging. This session addressed some of the problems encountered and showed the limitations and successes of infrared studies of massive stars.
ASP modulo CSP
(2012)
We present the hybrid ASP solver clingcon, combining the simple modeling language and the high performance Boolean solving capacities of Answer Set Programming (ASP) with techniques for using non-Boolean constraints from the area of Constraint Programming (CP). The new clingcon system features an extended syntax supporting global constraints and optimize statements for constraint variables. The major technical innovation improves the interaction between ASP and CP solver through elaborated learning techniques based on irreducible inconsistent sets. A broad empirical evaluation shows that these techniques yield a performance improvement of an order of magnitude.
Mineral chemistry and thermobarometry of the staurolite-chloritoid schists from Poshtuk, NW Iran
(2012)
The Poshtuk metapelitic rocks in northwestern Iran underwent two main phases of regional and contact metamorphism. Microstructures, textural features and field relations indicate that these rocks underwent a polymetamorphic history. The dominant metamorphic assemblage of the metapelites is garnet, staurolite, chloritoid, chlorite, muscovite and quartz, which grew mainly syntectonically during the later contact metamorphic event. Peak metamorphic conditions of this event took place at 580 ◦ C and ∼ 3–4 kbar, indicating that this event occurred under high-temperature and low-pressure conditions (HT/LP metamorphism), which reflects the high heat flow in this part of the crust. This event is mainly controlled by advective heat input through magmatic intrusions into all levels of the crust. These extensive Eocene metamorphic and magmatic activities can be associated with the early Alpine Orogeny, which resulted in this area from the convergence between the Arabian and Eurasian plates, and the Cenozoic closure of the Tethys oceanic tract(s).
F2C2
(2012)
Background: Flux coupling analysis (FCA) has become a useful tool in the constraint-based analysis of genome-scale metabolic networks. FCA allows detecting dependencies between reaction fluxes of metabolic networks at steady-state. On the one hand, this can help in the curation of reconstructed metabolic networks by verifying whether the coupling between reactions is in agreement with the experimental findings. On the other hand, FCA can aid in defining intervention strategies to knock out target reactions.
Results: We present a new method F2C2 for FCA, which is orders of magnitude faster than previous approaches. As a consequence, FCA of genome-scale metabolic networks can now be performed in a routine manner.
Conclusions: We propose F2C2 as a fast tool for the computation of flux coupling in genome-scale metabolic networks. F2C2 is freely available for non-commercial use at https://sourceforge.net/projects/f2c2/files/.