A dimensional reduction approach to modulate the core ruminal microbiome associated with methane emissions via selective breeding

The rumen is a complex microbial system of substantial importance in terms of greenhouse gas emissions and feed efficiency. This study proposes combining metagenomic and host genomic data for selective breeding of the cow hologenome toward reduced methane emissions. We analyzed nanopore long reads from the rumen metagenome of 437 Holstein cows from 14 commercial herds in 4 northern regions in Spain. After filtering, data were treated as compositional. The large complexity of the rumen microbiota was aggregated, through principal component analysis (PCA), into few principal components (PC) that were used as proxies of the core metagenome. The PCA allowed us to condense the huge and fuzzy taxonomical and functional information from the metagenome into a few PC. Bivariate animal models were applied using these PC and methane production as phenotypes. The variability condensed in these PC is controlled by the cow genome, with heritability estimates for the first PC of ~0.30 at all taxonomic levels, with a large probability (>83%) of the posterior distribution being >0.20 and with the 95% highest posterior density interval (95%HPD) not containing zero. Most genetic correlation estimates between PC1 and methane were large (≥0.70), with most of the posterior distribution (>82%) being >0.50 and with its 95%HPD not containing zero. Enteric methane production was positively associated with relative abundance of eukaryotes (protozoa and fungi) through the first component of the PCA at phylum, class, order, family, and genus. Nanopore long reads allowed the characterization of the core rumen metagenome using whole-metagenome sequencing, and the purposed aggregated variables could be used in animal breeding programs to reduce methane emissions in future generations.     

Artificial neural networks as alternative tool for minimizing error predictions in manufacturing ultradeformable nanoliposome formulations

This work was aimed at determining the feasibility of artificial neural networks (ANN) by implementing backpropagation algorithms with default settings to generate better predictive models than multiple linear regression (MLR) analysis. The study was hypothesized on timolol-loaded liposomes. As tutorial data for ANN, causal factors were used, which were fed into the computer program. The number of training cycles has been identified in order to optimize the performance of the ANN. The optimization was performed by minimizing the error between the predicted and real response values in the training step. The results showed that training was stopped at 10?000 training cycles with 80% of the pattern values, because at this point the ANN generalizes better. Minimum validation error was achieved at 12 hidden neurons in a single layer. MLR has great prediction ability, with errors between predicted and real values lower than 1% in some of the parameters evaluated. Thus, the performance of this model was compared to that of the MLR using a factorial design. Optimal formulations were identified by minimizing the distance among measured and theoretical parameters, by estimating the prediction errors. Results indicate that the ANN shows much better predictive ability than the MLR model. These findings demonstrate the increased efficiency of the combination of ANN and design of experiments, compared to the conventional MLR modeling techniques.     

Vitamin C Enhances Vitamin E Status and Reduces Oxidative Stress Indicators in Sea Bass Larvae Fed High DHA Microdiets

Docosahexaenoic acid (DHA) is an essential fatty acid necessary for many biochemical, cellular and physiological functions in fish. However, high dietary levels of DHA increase free radical injury in sea bass (Dicentrarchus labrax) larvae muscle, even when vitamin E (α-tocopherol, α-TOH) is increased. Therefore, the inclusion of other nutrients with complementary antioxidant functions, such as vitamin C (ascorbic acid, vitC), could further contribute to prevent these lesions. The objective of the present study was to determine the effect of vitC inclusion (3,600?mg/kg) in high DHA (5?% DW) and α-TOH (3,000?mg/kg) microdiets (diets 5/3,000 and 5/3,000?+?vitC) in comparison to a control diet (1?% DHA DW and 1,500?mg/kg of α-TOH; diet 1/1,500) on sea bass larvae growth, survival, whole body biochemical composition and thiobarbituric acid reactive substances (TBARS) content, muscle morphology, skeletal deformities and antioxidant enzymes, insulin-like growth factors (IGFs) and myosin expression (MyHC). Larvae fed diet 1/1,500 showed the best performance in terms of total length, incidence of muscular lesions and ossification degree. IGFs gene expression was elevated in 5/3,000 diet larvae, suggesting an increased muscle mitogenesis that was confirmed by the increase in the mRNA copies of MyHC. vitC effectively controlled oxidative damages in muscle, increased α-TOH larval contents and reduced TBARS content and the occurrence of skull deformities. The results of the present study showed the antioxidant synergism between vitamins E and C when high contents of DHA are included in sea bass larvae diets.     

Optimized laser processing of calibration blocks for grinding burn detection with Barkhausen noise

Calibration samples for the Barkhausen noise (BN) method were produced with laser processing. A planet gear wheel used for production quality control was subjected to laser irradiation to verify the BN sensor output. Different samples were found to respond similarly to the laser processing although the laser parameters needed to be adjusted for different surface qualities separately. The surface optimization for laser processing was studied with different surface qualities of samples. The ground surface was compared with a sandblasted and vibratory ground surface. The ground and sandblasted surfaces were both amenable to the laser processing whereas the vibratory grinding process created inhomogeneous surfaces for laser beam absorption. Laser processing was found to produce uniform changes in the residual stress values in two perpendicular measuring directions. The root mean square value of the BN voltage signal exhibited linear correlations with the values of the residual stress and surface hardness.     

Stability of the antimicrobial activity of acetogenins from avocado seed,under common food processing conditions,against Clostridium sporogenes vegetative cell growth and endospore germination

Anticlostridial activity of acetogenins from avocado seed was further characterised, and their stability and effectiveness under food processing conditions, and in a model food system, studied for the first time. Isolated AcO‐avocadenyne (1) and AcO‐avocadene (2) showed anticlostridial potential, particularly the latter molecule. Enriched acetogenins extracts, obtained at laboratory scale (EAE) and semi‐commercially (Avosafe^®), presented similar MIC values (3.9–9.8 ppm) and a bacteriostatic effect. Extracts bioactivity showed resistance to heat (≤120 °C), high hydrostatic pressure (HHP; 300–600 MPa, 3–6 min, 25 °C) and salt (≤3% w/v). In addition, the extract was most stable at pH ≥ 7.0 and potency against endospores increased after HHP treatment and exposure to pH 9.5, suggesting a positive effect on solubility or structure of particular acetogenins. In a model food system processed by HHP, acetogenins were retained; however, initial quantities gradually declined by 63% and 32% at 25 and 4 °C, respectively, at the end of the storage period (42 days). Most stable molecules (persediene (4) > persenones > AcO‐avocadene (2)) possess a keto or trans‐enone group at C‐4 in the aliphatic chain, which could support hydrogen donation to surrounding carbon atoms and confer antioxidant activity. Active endospores were completely inhibited by 5000 ppm Avosafe^® in the model food system (37 °C, 72 h) and lower concentrations (500–1000 ppm) resulted in 1–2 log reduction of a 3 log inoculum target. Efficacy information generated in the present work is considered crucial to improve scientific knowledge on spore inhibition properties of avocado acetogenins.     

Antioxidant activity of phenolic and phenylethanoid glycosides from Teucrium polium L

In the present study, the chemical composition and antioxidant activities of Teucrium polium L. (Lamiaceae) were assessed. Fractionation of methanol extract obtained from the aerial parts of T. polium yielded one new phenylethanoid glycoside, named poliumoside B, together with four known flavonoids, two iridoid glycosides and a known poliumoside. The structures of all of these compounds were elucidated on the basis of spectroscopic data from 1D and 2D NMR experiments and MS spectral analyses. The antioxidant activities of the crude extracts and of the isolated compounds were evaluated through tests such as DPPH radical-scavenging capability, reducing power, xanthine oxidase inhibitory effect and inhibition of linoleic acid peroxidation. The highest antioxidant activity was found in the n-butanol extract, which also contained active polyphenols, thus suggesting that this plant could be used as a source of natural molecules, to provide safe antioxidant additives and nutraceuticals. The structure–activity relationships of the isolated compounds are also discussed.     

Towards Automated Binding Affinity Prediction Using an Iterative Linear Interaction Energy Approach

Binding affinity prediction of potential drugs to target and off-target proteins is an essential asset in drug development. These predictions require the calculation of binding free energies. In such calculations, it is a major challenge to properly account for both the dynamic nature of the protein and the possible variety of ligand-binding orientations, while keeping computational costs tractable. Recently, an iterative Linear Interaction Energy (LIE) approach was introduced, in which results from multiple simulations of a protein-ligand complex are combined into a single binding free energy using a Boltzmann weighting-based scheme. This method was shown to reach experimental accuracy for flexible proteins while retaining the computational efficiency of the general LIE approach. Here, we show that the iterative LIE approach can be used to predict binding affinities in an automated way. A workflow was designed using preselected protein conformations, automated ligand docking and clustering, and a (semi-)automated molecular dynamics simulation setup. We show that using this workflow, binding affinities of aryloxypropanolamines to the malleable Cytochrome P450 2D6 enzyme can be predicted without a priori knowledge of dominant protein-ligand conformations. In addition, we provide an outlook for an approach to assess the quality of the LIE predictions, based on simulation outcomes only.     

Ontologies versus relational databases: are they so different? A comparison

Two main data models are currently used for representing knowledge and information in computer systems. Database models, especially relational databases, have been the leader in last few decades, enabling information to be efficiently stored and queried. On the other hand, ontologies have appeared as an alternative to databases in applications that require a more ‘enriched’ meaning. However, there is controversy regarding the best information modeling technique, as both models present similar characteristics. In this paper, we present a review of how ontologies and databases are related, of what their main differences are and of the mechanisms used to communicate with each other.