Effects of Chia (Salvia hispanica L.) seed supplementation on rabbits meat quality, oxidative stability and sensory traits

Chia (Salvia hispanica L.) seed (SHS) dietary supplementation is effective in improving the nutritional quality of rabbit meat for consumers and could contribute to the novel concept of “functional food” in human nutrition. A trial has been conducted in order to verify the effects of three levels (0, 10, or 15%) of SHS inclusion in a rabbit diet on the meat quality, oxidative stability and sensory traits. The dietary treatment did not induce any differences in the ultimate pH, chemical composition, drip losses of the longissimus dorsi muscle or the initial and ultimate pH of the biceps femoris muscle, but the SHS supplementation increased cooking losses of the rabbit meat. The inclusion of SHS also reduced oxidative stability during meat storage. No adverse effects were observed on the meat quality or customer acceptability. The inclusion of SHS in rabbit diets, which is effective in improving the n-3 polyunsaturated fatty acids content of meat, increased the lipid oxidation in the hind leg meat. An improvement in tissue oxidative stability could be obtained by feeding rabbits with higher levels of antioxidants

Reducing the Complexity of Complex Gene Coexpression Networks by Coupling Multiweighted Labeling with Topological Analysis

Undirected gene coexpression networks obtained from experimental expression data coupled with efficient computational procedures are increasingly used to identify potentially relevant biological information (e.g., biomarkers) for a particular disease. However, coexpression networks built from experimental expression data are in general large highly connected networks with an elevated number of false-positive interactions (nodes and edges). In order to infer relevant information, the network must be properly filtered and its complexity reduced. Given the complexity and the multivariate nature of the information contained in the network, this requires the development and application of efficient feature selection algorithms to be able to exploit the topological characteristics of the network to identify relevant nodes and edges. This paper proposes an efficient multivariate filtering designed to analyze the topological properties of a coexpression network in order to identify potential relevant genes for a given disease. The algorithm has been tested on three datasets for three well known and studied diseases: acute myeloid leukemia, breast cancer, and diffuse large B-cell lymphoma. Results have been validated resorting to bibliographic data automatically mined using the ProteinQuest literature mining too

Reducing the Complexity of Complex Gene Coexpression Networks by Coupling Multiweighted Labeling with Topological Analysis

Undirected gene coexpression networks obtained from experimental expression data coupled with efficient computational procedures are increasingly used to identify potentially relevant biological information (e.g., biomarkers) for a particular disease. However, coexpression networks built from experimental expression data are in general large highly connected networks with an elevated number of false-positive interactions (nodes and edges). In order to infer relevant information, the network must be properly filtered and its complexity reduced. Given the complexity and the multivariate nature of the information contained in the network, this requires the development and application of efficient feature selection algorithms to be able to exploit the topological characteristics of the network to identify relevant nodes and edges. This paper proposes an efficient multivariate filtering designed to analyze the topological properties of a coexpression network in order to identify potential relevant genes for a given disease. The algorithm has been tested on three datasets for three well known and studied diseases: acute myeloid leukemia, breast cancer, and diffuse large B-cell lymphoma. Results have been validated resorting to bibliographic data automatically mined using the ProteinQuest literature mining tool

In situ single-crystal X-ray diffraction of olivine inclusion in diamond from Shandong, China: implications for the depth of diamond formation

We have investigated a suite of natural diamonds from the kimberlite pipe of the Changma Kimberlite Belt, Mengyin County, Shandong Province, China, with the aim of constraining pressures and temperatures of formation. Here we report the non-destructive investigation of an olivine inclusion still entrapped within a lithospheric diamond by single-crystal X-ray diffraction. We were able to refine anisotropically its crystal structure to R1= 1.42 % using ionized scattering curves; this allows estimation of the composition of the olivine as Mg1.82Fe0.18SiO4. This composition corresponds to a calculated unit-cell volume equal to V= 292.70 Å3 at room temperature and pressure. We have validated the above-calculated composition and unit-cell volume by releasing the inclusion from the diamond host, resulting in a consistent composition calculated using non-destructive methods of Mg1.84Fe0.16SiO4 and V= 292.80 ± 0.07 Å3. Considering that the unit-cell volume of the olivine still inside its diamond host is V= 289.7 ± 0.2 Å3, we calculated a residual pressure Pinc= 1.4 ± 0.1 GPa with respect to the released crystal and Pinc= 1.3 ± 0.2 GPa with respect to the volume calculated from the “composition” indirectly retrieved by the structure refinement under ambient conditions. The two values of Pinc overlap within experimental uncertainty. We performed Fourier transform infrared (FTIR) analysis on the diamond host in order to calculate its mantle residence temperature, Tres, which resulted in a value of 1189 ∘C (for an assumed diamond age of 3 Ga) and 1218 ∘C (for an age of 1 Ga), with an average Tres equal to 1204 ± 15 ∘C. Using the most up-to-date pressure–volume–temperature equations of state for olivine and diamond, the residual pressure Pinc= 1.4 ± 0.1 GPa and average residence temperature of the diamond host Tres= 1204 ∘C, we retrieved a pressure of entrapment Ptrap= 6.3 ± 0.4 GPa. Using the non-destructive approach and relative Pinc = 1.3 GPa, we obtained a perfectly overlapping Ptrap= 6.2 GPa, within experimental uncertainty. This entrapment pressure corresponds to depths of about 190 ± 12 km. These results demonstrate that for high-quality crystal structure data measured on inclusions still trapped within diamond hosts, even a non-destructive approach can be used to calculate the depth of formation of diamond–olivine pairs. In terms of geological implications, the results from this work show that Changma diamonds formed under a conductive geotherm lying between 35 and 40 mW m−2, at a depth of about 190 km. This value lies within the recently reported upper limit of the average depth of formation of worldwide lithospheric diamonds, which is 175 ± 15 km and is in agreement with P–T data obtained in the literature from kimberlite xenoliths.</p