Encapsulation of Bifidobacterium longum in alginate-dairy matrices and survival in simulated gastrointestinal conditions, refrigeration, cow milk and goat milk

The aim of this study was to microencapsulate Bifidobacterium longum subsp. infantis CCUG 52486 using the extrusion method in a variety of matrices, namely sodium alginate (SA), sodium alginate-cow milk (SACM), sodium alginate-goat milk (SAGM) and sodium alginate-casein hydrolysate (SACH), and to evaluate the survival of free and encapsulated bacterial cells under different conditions. The encapsulation yield, size and surface morphology of the microcapsules were evaluated. The survival of microencapsulated bacterial cells and free bacterial cells were evaluated under simulated gastrointestinal conditions as well as in refrigeration, cow milk and goat milk during storage at 4 oC for 28 days. The average size of SACM capsules and SAGM capsules was 2.8±0.3 mm and 3.1±0.2 mm respectively. Goat milk and cow milk based matrices resulted in dense microcapsules which led to better performances in simulated gastrointestinal conditions than SA and SACH microcapsules. The bacterial cells encapsulated in SAGM showed the highest survival rate in cow milk (7.61 log cfu g-1) and goat milk (8.10 log cfu g-1) after the storage of 28 d. The cells encapsulated in SA and SACH and the free cells performed poorly under the simulated gastrointestinal conditions and in all different storage conditions. This study showed that SACM and SAGM are suitable to encapsulate B. longum subsp. infantis CCUG 52486 using the extrusion technique and more specifically, SAGM has a potential to be used as a new encapsulation material for encapsulating probiotic bacteria, resulting milk and goat milk-based products with higher probiotic cell concentrations during refrigerated storage

Cell-Probe Bounds for Online Edit Distance and Other Pattern Matching Problems

We give cell-probe bounds for the computation of edit distance, Hamming distance, convolution and longest common subsequence in a stream. In this model, a fixed string of $n$ symbols is given and one $\delta$-bit symbol arrives at a time in a stream. After each symbol arrives, the distance between the fixed string and a suffix of most recent symbols of the stream is reported. The cell-probe model is perhaps the strongest model of computation for showing data structure lower bounds, subsuming in particular the popular word-RAM model. * We first give an $\Omega((\delta \log n)/(w+\log\log n))$ lower bound for the time to give each output for both online Hamming distance and convolution, where $w$ is the word size. This bound relies on a new encoding scheme and for the first time holds even when $w$ is as small as a single bit. * We then consider the online edit distance and longest common subsequence problems in the bit-probe model ($w=1$) with a constant sized input alphabet. We give a lower bound of $\Omega(\sqrt{\log n}/(\log\log n)^{3/2})$ which applies for both problems. This second set of results relies both on our new encoding scheme as well as a carefully constructed hard distribution. * Finally, for the online edit distance problem we show that there is an $O((\log n)^2/w)$ upper bound in the cell-probe model. This bound gives a contrast to our new lower bound and also establishes an exponential gap between the known cell-probe and RAM model complexities.Comment: 32 pages, 4 figure

Joint Structure Learning of Multiple Non-Exchangeable Networks

Several methods have recently been developed for joint structure learning of multiple (related) graphical models or networks. These methods treat individual networks as exchangeable, such that each pair of networks are equally encouraged to have similar structures. However, in many practical applications, exchangeability in this sense may not hold, as some pairs of networks may be more closely related than others, for example due to group and sub-group structure in the data. Here we present a novel Bayesian formulation that generalises joint structure learning beyond the exchangeable case. In addition to a general framework for joint learning, we (i) provide a novel default prior over the joint structure space that requires no user input; (ii) allow for latent networks; (iii) give an efficient, exact algorithm for the case of time series data and dynamic Bayesian networks. We present empirical results on non-exchangeable populations, including a real data example from biology, where cell-line-specific networks are related according to genomic features.Comment: To appear in Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics (AISTATS

A Theory of a Spot

We present a simple inflationary scenario that can produce arbitrarily large spherical underdense or overdense regions embedded in a standard Lambda cold dark matter paradigm, which we refer to as bubbles. We analyze the effect such bubbles would have on the Cosmic Microwave Background (CMB). For super-horizon sized bubble in the vicinity of the last scattering surface, a signal is imprinted onto CMB via a combination of Sach-Wolfe and an early integrated Sach-Wolfe (ISW) effects. Smaller, sub-horizon sized bubbles at lower redshifts (during matter domination and later) can imprint secondary anisotropies on the CMB via Rees-Sciama, late-time ISW and Ostriker-Vishniac effects. Our scenario, and arguably most similar inflationary models, produce bubbles which are over/underdense in potential: in density such bubbles are characterized by having a distinct wall with the interior staying at the cosmic mean density. We show that such models can potentially, with only moderate fine tuning, explain the \emph{cold spot}, a non-Gaussian feature identified in the Wilkinson Microwave Anisotropy Probe (WMAP) data by several authors. However, more detailed comparisons with current and future CMB data are necessary to confirm (or rule out) this scenario.Comment: 19 pages, 19 figures, added references and explanations, JCAP in pres

Spanish banking sector: past, present and future

Evento: 15th Annual European Financials Conference. Organizado por: Goldman Sach