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.     

Automatic bearing fault diagnosis based on one-class ν-SVM

Rolling-element bearings are among the most used elements in industrial machinery, thus an early detection of a defect in these components is necessary to avoid major machine failures. Vibration analysis is a widely used condition monitoring technique for high-speed rotating machinery. Using the information contained in the vibration signals, an automatic method for bearing fault detection and diagnosis is presented in this work. Initially, a one-class ν-SVM is used to discriminate between normal and faulty conditions. In order to build a model of normal operation regime, only data extracted under normal conditions is used. Band-pass filters and Hilbert Transform are then used sequentially to obtain the envelope spectrum of the original raw signal that will finally be used to identify the location of the problem. In order to check the performance of the method, two different data sets are used: (a) real data from a laboratory test-to-failure experiment and (b) data obtained from a fault-seeded bearing test. The results showed that the method was able not only to detect the failure in an incipient stage but also to identify the location of the defect and qualitatively assess its evolution over time.     

A review of adaptive online learning for artificial neural networks

In real applications learning algorithms have to address several issues such as, huge amount of data, samples which arrive continuously and underlying data generation processes that evolve over time. Classical learning is not always appropriate to work in these environments since independent and indentically distributed data are assumed. Taking into account the requirements of the learning process, systems should be able to modify both their structures and their parameters. In this survey, our aim is to review the developed methodologies for adaptive learning with artificial neural networks, analyzing the strategies that have been traditionally applied over the years. We focus on sequential learning, the handling of the concept drift problem and the determination of the network structure. Despite the research in this field, there are currently no standard methods to deal with these environments and diverse issues remain an open problem.     

Synthesis at several length scales of zeolite A membranes using a continuous flow method

Zeolite A membranes were synthesized in the inner side of porous TiO₂ and α-Al₂O₃ tubular supports by a continuous method. The methodology was then used at several scales for preparing membranes on TiO₂ and α-Al₂O₃ supports with lengths of 6, 12 and 25 cm. Formation of an homogeneous zeolite film was confirmed by XRD and SEM in all supports. Single-gas permeation experiments (He, H₂, N₂ and n-C₃H₈) indicated that Knudsen diffusion was the predominant mechanism in both supports. All the synthesized membranes present high flux and moderate selectivity to water in water–ethanol mixtures.     

Efficient visualization of 3D models on hardware-limited portable devices

Managing the geometry of a 3D scene efficiently is a key aspect of an interactive 3D application. This aspect is more important if we target at portable devices, which have limited hardware capabilities. Developing new means for improving the interaction with 3D content in mobile devices is key. The aim of this work is to present a technique which can manage the level-of-detail of 3D meshes in portable devices. This solution has been devised considering the restrictions that this kind of devices poses. The results section shows how the integration has been successful while obtaining good performance.     

Functionality of egg white proteins as affected by high intensity ultrasound

The goal of this contribution was to determine the impact of HIUS on the thermal aggregation, gelation, foaming and emulsifying properties of egg white (EW) proteins.     

Genetic Variation of the Endangered Gentiana lutea L. var. aurantiaca (Gentianaceae) in Populations from the Northwest Iberian Peninsula

Gentiana lutea L. (G. lutea L.) is an endangered plant, patchily distributed along the mountains of Central and Southern Europe. In this study, inter-simple sequence repeat (ISSR) markers were used to investigate the genetic variation in this species within and among populations of G. lutea L. var. aurantiaca of the Cantabrian Mountains (Northwest Iberian Peninsula). Samples of G. lutea L. collected at different locations of the Pyrenees and samples of G. lutea L. subsp. vardjanii of the Dolomites Alps were also analyzed for comparison. Using nine ISSR primers, 106 bands were generated, and 89.6% of those were polymorphic. The populations from the Northwest Iberian Peninsula were clustered in three different groups, with a significant correlation between genetic and geographic distances. Gentiana lutea L. var. aurantiaca showed 19.8% private loci and demonstrated a remarkable level of genetic variation, both among populations and within populations; those populations with the highest level of isolation show the lowest genetic variation within populations. The low number of individuals, as well as the observed genetic structure of the analyzed populations makes it necessary to protect them to ensure their survival before they are too small to persist naturally.     

Amphiphilic drug interactions with model cellular membranes are influenced by lipid chain-melting temperature

Small-molecule amphiphilic species such as many drug molecules frequently exhibit low-to-negligible aqueous solubility, and generally have no identified transport proteins assisting their distribution, yet are able to rapidly penetrate significant distances into patient tissue and even cross the blood–brain barrier. Previous work has identified a mechanism of translocation driven by acid-catalysed lipid hydrolysis of biological membranes, a process which is catalysed by the presence of cationic amphiphilic drug molecules. In this study, the interactions of raclopride, a model amphiphilic drug, were investigated with mixtures of biologically relevant lipids across a range of compositions, revealing the influence of the chain-melting temperature of the lipids upon the rate of acyl hydrolysis.     

Design of whey protein nanostructures for incorporation and release of nutraceutical compounds in food

Whey proteins are widely used as nutritional and functional ingredients in formulated foods because they are relatively inexpensive, generally recognized as safe (GRAS) ingredient, and possess important biological, physical, and chemical functionalities. Denaturation and aggregation behavior of these proteins is of particular relevance toward manufacture of novel nanostructures with a number of potential uses. When these processes are properly engineered and controlled, whey proteins may be formed into nanohydrogels, nanofibrils, or nanotubes and be used as carrier of bioactive compounds. This review intends to discuss the latest understandings of nanoscale phenomena of whey protein denaturation and aggregation that may contribute for the design of protein nanostructures. Whey protein aggregation and gelation pathways under different processing and environmental conditions such as microwave heating, high voltage, and moderate electrical fields, high pressure, temperature, pH, and ionic strength were critically assessed. Moreover, several potential applications of nanohydrogels, nanofibrils, and nanotubes for controlled release of nutraceutical compounds (e.g. probiotics, vitamins, antioxidants, and peptides) were also included. Controlling the size of protein networks at nanoscale through application of different processing and environmental conditions can open perspectives for development of nanostructures with new or improved functionalities for incorporation and release of nutraceuticals in food matrices.