The influence of population density and duration of breeding on broiler chickens productivity and profitability

Fattening of broiler chickens is a very specific production process characterized by intensive production principles, rapid increase in broilers, small consumption of food/kg of gain (feed conversion ratio) and a large production of broilers’ meat per square meter of surface. In order to increase the profitability of this production, the intention of farmers is to reduce the duration of production as well as to increase population density, with the aim of increasing the production of broiler chickens’ meat,calculated per unit of surface (m2). However, most of the countries in the world, in order to protect and preserve the welfare of poultry, limit the maximum of broilers’ meat production by regulations and standards. These researches aim to determine the optimal density and duration of fattening in a way to achieve the best production results and the profitability of fattening the broiler chickens hybrids Cobb 500 in temperate continental climate, while preserving the welfare of poultry. From six different population densities (16.84, 16.33, 14.29, 12.75, 14.80 and 15.46 birds per m2) and fattening period between 37 and 40 days, the best production and economic performance showed that a group ofchickens that was fattened in a period of 40 days had a population density of about 16 birds per m2. This group of broilers produced the most meat per m2 (about 33 kg), the welfare of poultry is maintained and the standard was not exceeded, so we can say that the best economy and profitability of fattening broiler chickens in the concerned region is achieved. In other groups of chickens, profitability could be more advantageous if the increased population density goes up to 16 birds perm2; or the duration of fattening could be extended up to 40 days and by this way the welfare of poultry would not be violated

Optimised Curing of Silver Ink Jet Based Printed Traces

Manufacturing electronic devices by printing techniques with low temperature sintering of nano-size material particles can revolutionize the electronics industry in coming years. The impact of this change to the industry can be significant enabling low-cost products and flexibility in manufacturing. implementation of a new production technology with new materials requires thorough elementary knowledge creation. It should be noticed that although some of first electronic devices ideally can be manufactured by printing, at the present several modules are in fact manufactured by using hybrid techniques (for instance photolithography, vapor depositions, spraying, etc...).Comment: Submitted on behalf of TIMA Editions (http://irevues.inist.fr/tima-editions

BayFlux: A Bayesian Method to Quantify Metabolic Fluxes and their Uncertainty at the Genome Scale.

Metabolic fluxes, the number of metabolites traversing each biochemical reaction in a cell per unit time, are crucial for assessing and understanding cell function. 13C Metabolic Flux Analysis (13C MFA) is considered to be the gold standard for measuring metabolic fluxes. 13C MFA typically works by leveraging extracellular exchange fluxes as well as data from 13C labeling experiments to calculate the flux profile which best fit the data for a small, central carbon, metabolic model. However, the nonlinear nature of the 13C MFA fitting procedure means that several flux profiles fit the experimental data within the experimental error, and traditional optimization methods offer only a partial or skewed picture, especially in “non-gaussian” situations where multiple very distinct flux regions fit the data equally well. Here, we present a method for flux space sampling through Bayesian inference (BayFlux), that identifies the full distribution of fluxes compatible with experimental data for a comprehensive genome-scale model. This Bayesian approach allows us to accurately quantify uncertainty in calculated fluxes. We also find that, surprisingly, the genome-scale model of metabolism produces narrower flux distributions (reduced uncertainty) than the small core metabolic models traditionally used in 13C MFA. The different results for some reactions when using genome-scale models vs core metabolic models advise caution in assuming strong inferences from 13C MFA since the results may depend significantly on the completeness of the model used. Based on BayFlux, we developed and evaluated novel methods (P-13C MOMA and P-13C ROOM) to predict the biological results of a gene knockout, that improve on the traditional MOMA and ROOM methods by quantifying prediction uncertainty

Spectroscopy of 34,35Si^{34,35}Si by β\beta decay: sd-fp shell gap and single-particle states

The $^{34,35}Al\beta$ decays were studied at the CERN on-line mass separator ISOLDE by $\beta-\gamma, \beta-\gamma-\gamma$ and $\beta-n-\gamma$ measurements, in order to corroborate thelow-level description of $^{34}Si$ and to obtain the first information on the level structure of the N=21 isotope $^{35}Si$. Earlier observed $\gamma$ lines in $^{34} Al$ decay were confirmed and new gamma transitions following both beta decay and $\beta$-delayed neutron emission were established. The first level scheme in $^{35}Si$, including three excited states at 910, 974 and 2168 keV, is consistent with $J^{\pi} =3/2^{-}$ and $3/2^{+}$ for the first two states respectively. Beta-decay half-life of $T_{1/2} = 38.6 (4)$ ms and beta-delayed neutron branching $P_{n}$ value $(P_{n} =41(13)$ were measured unambiguously. The significance of the single-particle energy determination at N=21, Z=14, for assessing the effective interaction in sd-fp shell-model calculations, is discussed and illustrated by predictions for different n-rich isotopes

Predictive engineering and optimization of tryptophan metabolism in yeast through a combination of mechanistic and machine learning models

In combination with advanced mechanistic modeling and the generation of high-quality multi-dimensional data sets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can complement each other and be used in a combined approach to enable accurate genotype-to-phenotype predictions. We use a genome-scale model to pinpoint engineering targets and produce a large combinatorial library of metabolic pathway designs with different promoters which, once phenotyped, provide the basis for machine learning algorithms to be trained and used for new design recommendations. The approach enables successful forward engineering of aromatic amino acid metabolism in yeast, with the new recommended designs improving tryptophan production by up to 17% compared to the best designs used for algorithm training, and ultimately producing a total increase of 106% in tryptophan accumulation compared to optimized reference designs. Based on a single high-throughput data-generation iteration, this study highlights the power of combining mechanistic and machine learning models to enhance their predictive power and effectively direct metabolic engineering efforts

Impact of renal impairment on atrial fibrillation: ESC-EHRA EORP-AF Long-Term General Registry

Background: Atrial fibrillation (AF) and renal impairment share a bidirectional relationship with important pathophysiological interactions. We evaluated the impact of renal impairment in a contemporary cohort of patients with AF. Methods: We utilised the ESC-EHRA EORP-AF Long-Term General Registry. Outcomes were analysed according to renal function by CKD-EPI equation. The primary endpoint was a composite of thromboembolism, major bleeding, acute coronary syndrome and all-cause death. Secondary endpoints were each of these separately including ischaemic stroke, haemorrhagic event, intracranial haemorrhage, cardiovascular death and hospital admission. Results: A total of 9306 patients were included. The distribution of patients with no, mild, moderate and severe renal impairment at baseline were 16.9%, 49.3%, 30% and 3.8%, respectively. AF patients with impaired renal function were older, more likely to be females, had worse cardiac imaging parameters and multiple comorbidities. Among patients with an indication for anticoagulation, prescription of these agents was reduced in those with severe renal impairment, p <.001. Over 24 months, impaired renal function was associated with significantly greater incidence of the primary composite outcome and all secondary outcomes. Multivariable Cox regression analysis demonstrated an inverse relationship between eGFR and the primary outcome (HR 1.07 [95% CI, 1.01–1.14] per 10 ml/min/1.73 m2 decrease), that was most notable in patients with eGFR <30 ml/min/1.73 m2 (HR 2.21 [95% CI, 1.23–3.99] compared to eGFR ≥90 ml/min/1.73 m2). Conclusion: A significant proportion of patients with AF suffer from concomitant renal impairment which impacts their overall management. Furthermore, renal impairment is an independent predictor of major adverse events including thromboembolism, major bleeding, acute coronary syndrome and all-cause death in patients with AF

Sensitivity of contribution margin in milk production on family farms

© 2018, University of Zagreb - Faculty of Agriculture. All rights reserved. Milk production in Serbia is characterized by small surface of production and a large number of small family farms. For rural areas where this production is mostly carried out, economic profitability of production contributes to becoming a family business. This study refers to milk family farms of smaller and medium economic and production capacity, for which the economic analysis in the period of three years was applied and production results were calculated. Applying the calculation method the total revenues and total costs, and contribution margins per cow and per liter of produced milk were computed. By comparing the results obtained on farms A and B, conclusions were drawn, and the sensitivity of contribution margins to change of the given parameters (purchase prices of milk and milk produced per cow) was determined by sensitivity analysis. The stability in production is realized by achieving higher contribution margins with satisfactory milk yield per cow and quality milk, as well acceptable purchase prices of milk