In vitro and in vivo selection of potentially probiotic lactobacilli from Nocellara del Belice table olives

Table olives are increasingly recognized as a vehicle as well as a source of probiotic bacteria, especially those fermented with traditional procedures based on the activity of indigenous microbial consortia, originating from local environments. In the present study, we report characterization at the species level of 49 Lactic Acid Bacteria (LAB) strains deriving from Nocellara del Belice table olives fermented with the Spanish or Castelvetrano methods, recently isolated in our previous work. Ribosomal 16S DNA analysis allowed identification of 4 Enterococcus gallinarum, 3 E. casseliflavus, 14 Leuconostoc mesenteroides, 19 Lactobacillus pentosus, 7 L. coryniformis, and 2 L. oligofermentans. The L. pentosus and L. coryniformis strains were subjected to further screening to evaluate their probiotic potential, using a combination of in vitro and in vivo approaches. The majority of them showed high survival rates under in vitro simulated gastro-intestinal conditions, and positive antimicrobial activity against Salmonella enterica serovar Typhimurium, Listeria monocytogenes and enterotoxigenic Escherichia coli (ETEC) pathogens. Evaluation of antibiotic resistance to ampicillin, tetracycline, chloramphenicol, or erythromycin was also performed for all selected strains. Three L. coryniformis strains were selected as very good performers in the initial in vitro testing screens, they were antibiotic susceptible, as well as capable of inhibiting pathogen growth in vitro. Parallel screening employing the simplified model organism Caenorhabditis elegans, fed the Lactobacillus strains as a food source, revealed that one L. pentosus and one L. coryniformis strains significantly induced prolongevity effects and protection from pathogen-mediated infection. Moreover, both strains displayed adhesion to human intestinal epithelial Caco-2 cells and were able to outcompete foodborne pathogens for cell adhesion. Overall, these results are suggestive of beneficial features for novel LAB strains, which renders them promising candidates as starters for the manufacturing of fermented table olives with probiotic added value

Simultaneous Inference of User Representations and Trust

Inferring trust relations between social media users is critical for a number of applications wherein users seek credible information. The fact that available trust relations are scarce and skewed makes trust prediction a challenging task. To the best of our knowledge, this is the first work on exploring representation learning for trust prediction. We propose an approach that uses only a small amount of binary user-user trust relations to simultaneously learn user embeddings and a model to predict trust between user pairs. We empirically demonstrate that for trust prediction, our approach outperforms classifier-based approaches which use state-of-the-art representation learning methods like DeepWalk and LINE as features. We also conduct experiments which use embeddings pre-trained with DeepWalk and LINE each as an input to our model, resulting in further performance improvement. Experiments with a dataset of $\sim$356K user pairs show that the proposed method can obtain an high F-score of 92.65%.Comment: To appear in the proceedings of ASONAM'17. Please cite that versio

GEMSEC: Graph Embedding with Self Clustering

Modern graph embedding procedures can efficiently process graphs with millions of nodes. In this paper, we propose GEMSEC -- a graph embedding algorithm which learns a clustering of the nodes simultaneously with computing their embedding. GEMSEC is a general extension of earlier work in the domain of sequence-based graph embedding. GEMSEC places nodes in an abstract feature space where the vertex features minimize the negative log-likelihood of preserving sampled vertex neighborhoods, and it incorporates known social network properties through a machine learning regularization. We present two new social network datasets and show that by simultaneously considering the embedding and clustering problems with respect to social properties, GEMSEC extracts high-quality clusters competitive with or superior to other community detection algorithms. In experiments, the method is found to be computationally efficient and robust to the choice of hyperparameters

On the deflection of asteroids with mirrors

This paper presents an analysis of an asteroid deflection method based on multiple solar concentrators. A model of the deflection through the sublimation of the surface material of an asteroid is presented, with simulation results showing the achievable orbital deflection with, and without, accounting for the effects of mirror contamination due to the ejected debris plume. A second model with simulation results is presented analyzing an enhancement of the Yarkovsky effect, which provides a significant deflection even when the surface temperature is not high enough to sublimate. Finally the dynamical model of solar concentrators in the proximity of an irregular celestial body are discussed, together with a Lyapunov-based controller to maintain the spacecraft concentrators at a required distance from the asteroid

Statistically Significant Detection of Linguistic Change

We propose a new computational approach for tracking and detecting statistically significant linguistic shifts in the meaning and usage of words. Such linguistic shifts are especially prevalent on the Internet, where the rapid exchange of ideas can quickly change a word's meaning. Our meta-analysis approach constructs property time series of word usage, and then uses statistically sound change point detection algorithms to identify significant linguistic shifts. We consider and analyze three approaches of increasing complexity to generate such linguistic property time series, the culmination of which uses distributional characteristics inferred from word co-occurrences. Using recently proposed deep neural language models, we first train vector representations of words for each time period. Second, we warp the vector spaces into one unified coordinate system. Finally, we construct a distance-based distributional time series for each word to track it's linguistic displacement over time. We demonstrate that our approach is scalable by tracking linguistic change across years of micro-blogging using Twitter, a decade of product reviews using a corpus of movie reviews from Amazon, and a century of written books using the Google Book-ngrams. Our analysis reveals interesting patterns of language usage change commensurate with each medium.Comment: 11 pages, 7 figures, 4 table

Optimal low-thrust trajectories to asteroids through an algorithm based on differential dynamic programming

In this paper an optimisation algorithm based on Differential Dynamic Programming is applied to the design of rendezvous and fly-by trajectories to near Earth objects. Differential dynamic programming is a successive approximation technique that computes a feedback control law in correspondence of a fixed number of decision times. In this way the high dimensional problem characteristic of low-thrust optimisation is reduced into a series of small dimensional problems. The proposed method exploits the stage-wise approach to incorporate an adaptive refinement of the discretisation mesh within the optimisation process. A particular interpolation technique was used to preserve the feedback nature of the control law, thus improving robustness against some approximation errors introduced during the adaptation process. The algorithm implements global variations of the control law, which ensure a further increase in robustness. The results presented show how the proposed approach is capable of fully exploiting the multi-body dynamics of the problem; in fact, in one of the study cases, a fly-by of the Earth is scheduled, which was not included in the first guess solution

The effect of seasoning with herbs on the nutritional, safety and sensory properties of reduced-sodium fermented Cobrançosa cv. table olives

This study aimed at evaluating the effectiveness of seasoning Cobrancosa table olives in a brine with aromatic ingredients, in order to mask the bitter taste given by KCl when added to reduced-sodium fermentation brines. Olives were fermented in two different salt combinations: Brine A, containing 8% NaCl and, Brine B, a reduced-sodium brine, containing 4% NaCl + 4% KCl. After the fermentation the olives were immersed in seasoning brines with NaCl (2%) and the aromatic herbs (thyme, oregano and calamintha), garlic and lemon. At the end of the fermentation and two weeks after seasoning, the physicochemical, nutritional, organoleptic, and microbiological parameters, were determined. The olives fermented in the reduced-sodium brines had half the sodium concentration, higher potassium and calcium content, a lower caloric level, but were considered, by a sensorial panel, more bitter than olives fermented in NaCl brine. Seasoned table olives, previously fermented in Brine A and Brine B, had no significant differences in the amounts of protein (1.23% or 1.11%), carbohydrates (1.0% or 0.66%), fat (20.0% or 20.5%) and dietary fiber (3.4% or 3.6%). Regarding mineral contents, the sodium-reduced fermented olives, presented one third of sodium, seven times more potassium and three times more calcium than the traditional olives fermented in 8% NaCl. Additionally, according to the panelists' evaluation, seasoning the olives fermented in 4% NaCl + 4% KCl, resulted in a decrease in bitterness and an improvement in the overall evaluation and flavor. Escherichia coli and Salmonella were not found in the olives produced.info:eu-repo/semantics/publishedVersio

Test beam performance measurements for the Phase I upgrade of the CMS pixel detector

A new pixel detector for the CMS experiment was built in order to cope with the instantaneous luminosities anticipated for the Phase I Upgrade of the LHC. The new CMS pixel detector provides four-hit tracking with a reduced material budget as well as new cooling and powering schemes. A new front-end readout chip mitigates buffering and bandwidth limitations, and allows operation at low comparator thresholds. In this paper, comprehensive test beam studies are presented, which have been conducted to verify the design and to quantify the performance of the new detector assemblies in terms of tracking efficiency and spatial resolution. Under optimal conditions, the tracking efficiency is (99.95 ± 0.05) %, while the intrinsic spatial resolutions are (4.80 ± 0.25) μm and (7.99 ± 0.21) μm along the 100 μm and 150 μm pixel pitch, respectively. The findings are compared to a detailed Monte Carlo simulation of the pixel detector and good agreement is found.Peer reviewe

Characterisation of irradiated thin silicon sensors for the CMS phase II pixel upgrade

The high luminosity upgrade of the Large Hadron Collider, foreseen for 2026, necessitates the replacement of the CMS experiment's silicon tracker. The innermost layer of the new pixel detector will be exposed to severe radiation, corresponding to a 1 MeV neutron equivalent fluence of up to Phi(eq) = 2x10(16) cm(-2), and an ionising dose of approximate to 5 MGy after an integrated luminosity of 3000 fb(-1). Thin, planar silicon sensors are good candidates for this application, since the degradation of the signal produced by traversing particles is less severe than for thicker devices. In this paper, the results obtained from the characterisation of 100 and 200 mu m thick p-bulk pad diodes and strip sensors irradiated up to fluences of Phi(eq) = 1.3 x 10(16) cm(-2) are shown.Peer reviewe