Nitrogen and energy partitioning in two genetic groups of pigs fed low-protein diets at 130 kg body weight

The aim was to evaluate the effect of low-protein (LP) or low-amino acid diets on digestibility, energy and nitrogen (N) utilisation in 2 genetic groups (GG) of pigs (129±11 kg BW). Duroc×Large White (A) pigs were chosen to represent a traditional GG for ham production, and Danbred Duroc (D) pigs to represent a GG with fast growing rate and high carcass lean yield. Dietary treatments: a conventional diet (CONV) containing 13.2% CP, and two LP diets, one with LP (10.4%) and low essential AA (LP1), the second with LP (9.7%) and high essential AA (LP2). Compared to CONV, LP2 had the same essential AA content per unit feed, while LP1 the same essential AA content per unit CP. Feed was restricted (DMI=6.8% BW0.75). Four consecutive digestibility/balances periods were conducted with 24 barrows, 12 A and 12 D. Metabolic cages and respiration chambers were used. No significant difference between diets was registered for digestibility. Nitrogen excreted: 41.3, 33.4 and 29.0 g/d (P=0.009), for CONV, LP1 and LP2 diets, respectively. Nitrogen retention was similar between the diets. Heat production (HP) was the lowest for LP diets. There was a tendency (P=0.079) for a lower energy digestibility in D group. The D pigs also had a higher HP and hence a lower retained energy in comparison with the A pigs. In conclusion: it is possible to reduce N excretion using very LP diets and LP-low AA diets; Danbred GG have a higher heat production and a lower energy retention than A pigs

Effect of raw sunflower seeds on goat milk production in different farming systems

Aim of this study was to test the effect of raw sunflower seeds on goat milk production. Two farms with different farming systems (intensive and semi-intensive) participated to the trial. In each farm about 60 mid-lactation Alpine goats were divided in two groups during spring-summer time. A diet containing 5-6% of sunflower seeds on DM basis was compared with a control diet in a change-over design. In the semi-intensive farm milk yield of goats fed sunflower was 3.46 kg/d compared to 3.58 kg/d of goats fed control diet, whereas in the intensive farm milk yield was 4.60 kg/d vs 4.66 kg/d. Fat content increased significantly from 2.99% to 3.23% only in the intensive farm. The research in the intensive farm investigated also milk and cheese fatty acids composition. Medium and short chain fatty acids (C8-C16) content dropped and long chain fatty acids content increased when sunflower was added. In conclusion raw sunflower seed inclusion in dairy goat diets can be useful, in order to limit the inversion of fat and protein percentages in milk

High order edge finite element approximations for the time-harmonic Maxwell{'}s equations

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Feeding behaviour, digestibility, energy balance and productive performance of lactating goats fed forage-based and forage-free diets

Six lactating Saanen goats have been used in a Latin Square design to evaluate a grass-based diet (G), a hay-based diet(H) and a nonforage diet (NF). On dry matter, grass and hay contributed for 55% of the diets and had 13.7 and 16.1%CP, 55.4 and 49.4% NDF, 38.0 and 31.6% ADF, respectively. Diet NF had beet pulp, cracked carob beans and whole cottonseedas main ingredients, with more than 75% of the particles greater than 2 mm.Independently of the dietary treatment, the goats spent more time eating than ruminating. Diet NF proved to be effectivein stimulating chewing activity, despite a trend for a lower chewing activity for eating (178, 185, 125 min/kg DMIfor diets G, H and NF, respectively), but not for ruminating (84, 80, 80 min/kg DMI for diets G, H and NF, respectively).Feed intake did not differ among diets, while regarding digestibility diet NF had the highest values for DM (74.1%), OM(75.7%) and non-fibrous carbohydrates (92.0%), but the lowest for ADF (44.5%). For treatments G, H and NF milk yieldswere 3011, 3688 and 3212 g/d (P<0.05 between H and G), while milk fat and protein were respectively 3.37, 3.24,2.96% (P<0.05 between G and NF) and 3.11, 3.32, 3.29%. Milk urea N was lower for diet NF (18.8, 18.6, 12.7 mg/100ml, P<0.001). Diet NF increased the concentration of the short chain fatty acids of milk fat and decreased the content ofC18:0, C18:1 and C18:3 in comparison to the other two diets. No difference among treatments was recorded for CLA.Intake energy was digested to a lesser extent for diet G (68.9, 70.0, 72.7%, P<0.05 between G and NF) due to its poorquality forage. Urinary energy losses reflected the corresponding protein contents of the diets, while no difference wasrecorded for methane production. ME resulted higher for diet NF (60.0, 60.7, 65.1% of the intake energy, P<0.01), whileheat production and milk energy yield were similar in the three treatments. Diet NF had a higher ME content (11.13,11.26, 11.93 MJ/kg DM, P<0.05), while no significant difference among the diets was recorded in terms of kl (0.64, 0.70,0.69) and NEl (7.20, 7.93, 8.30 MJ/kg DM).It is concluded from the study that a nonforage diet with an adequate amount of structured fibre could substitute a rationbased on poor quality forage in lactating goats; however, good forage seems to enhance milk performance to a greaterextent

On the determination of the deceleration parameter from Supernovae data

Supernovae searches have shown that a simple matter-dominated and decelerating universe should be ruled out. However a determination of the present deceleration parameter $q_0$ through a simple kinematical description is not exempt of possible drawbacks. We show that, with a time dependent equation of state for the dark energy, a bias is present for $q_0$ : models which are very far from the so-called Concordance Model can be accommodated by the data and a simple kinematical analysis can lead to wrong conclusions. We present a quantitative treatment of this bias and we present our conclusions when a possible dynamical dark energy is taken into account.Comment: 4 pages, 3 figures, submitte

Prediction of the nutritive value of maize silage using in vitro and near infrared reflectance spectroscopy (NIRS) techniques

Maize silage is by far the most used forage in the diets for dairy cows and beef cattle in a large part of the Po plain, Italy. However, its chemical composition and its nutritive value range widely according to the genotype and to the climatic and agronomic conditions, particularly with regards to the plant maturity at harvest

Tannin treated lucerne silage in dairy cow feeding

The effects of the addition of tannins to lucerne silage were investigated. At ensiling, chestnut hydrolyzable tannins were added to lucerne forage (T=tannins treated lucerne silage vs C=control lucerne silage). Fifty lactating Holstein cows, fed two diets different for lucerne silage treatment (C or T), were used in a cross-over design. In situ rumen soluble protein fraction (%CP) was higher for C (67.9 vs 59.4; P<0.01), whereas potentially rumen degradable protein (%CP) was lower (24.5 vs 32.1 for C and T; P<0.01). Intestinal rumen escape protein digestibility (%) was numerically higher for T (48.3 vs 54.3). Dry matter intake (21.5 kg/d for both diets) and milk yield (29.8 and 30.2 kg/d for C and T) were not affected by dietary treatment, whereas FCM was slightly higher for T diet (27.5 vs 27.9 kg/d for C and T; P<0.10). Adding tannins to lucerne silage is effective in shifting part of N utilization from the rumen to the intestine, leading to similar productive performance in lactating cows

TimeSOAP: Tracking high-dimensional fluctuations in complex molecular systems via time variations of SOAP spectra

Many molecular systems and physical phenomena are controlled by local fluctuations and microscopic dynamical rearrangements of the constitutive interacting units that are often difficult to detect. This is the case, for example, of phase transitions, phase equilibria, nucleation events, and defect propagation, to mention a few. A detailed comprehension of local atomic environments and of their dynamic rearrangements is essential to understand such phenomena and also to draw structure-property relationships useful to unveil how to control complex molecular systems. Considerable progress in the development of advanced structural descriptors [e.g., Smooth Overlap of Atomic Position (SOAP), etc.] has certainly enhanced the representation of atomic-scale simulations data. However, despite such efforts, local dynamic environment rearrangements still remain difficult to elucidate. Here, exploiting the structurally rich description of atomic environments of SOAP and building on the concept of time-dependent local variations, we developed a SOAP-based descriptor, TimeSOAP (τSOAP), which essentially tracks time variations in local SOAP environments surrounding each molecule (i.e., each SOAP center) along ensemble trajectories. We demonstrate how analysis of the time-series τSOAP data and of their time derivatives allows us to detect dynamic domains and track instantaneous changes of local atomic arrangements (i.e., local fluctuations) in a variety of molecular systems. The approach is simple and general, and we expect that it will help shed light on a variety of complex dynamical phenomena

The Λ\LambdaCDM growth rate of structure revisited

We re-examine the growth index of the concordance $\Lambda$ cosmology in the light of the latest 6dF and {\em WiggleZ} data. In particular, we investigate five different models for the growth index $\gamma$, by comparing their cosmological evolution using observational data of the growth rate of structure formation at different redshifts. Performing a joint likelihood analysis of the recent supernovae type Ia data, the Cosmic Microwave Background shift parameter, Baryonic Acoustic Oscillations and the growth rate data, we determine the free parameters of the $\gamma(z)$ parametrizations and we statistically quantify their ability to represent the observations. We find that the addition of the 6dF and {\em WiggleZ} growth data in the likelihood analysis improves significantly the statistical results. As an example, considering a constant growth index we find $\Omega_{m0}=0.273\pm 0.011$ and $\gamma=0.586^{+0.079}_{-0.074}$.Comment: 8 pages, 5 figures, Accepted for publication by International J. of Modern Physics D (IJMPD). arXiv admin note: substantial text overlap with arXiv:1203.672