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

Faster Approximate Pattern Matching: {A} Unified Approach

Approximate pattern matching is a natural and well-studied problem on strings: Given a text $T$, a pattern $P$, and a threshold $k$, find (the starting positions of) all substrings of $T$ that are at distance at most $k$ from $P$. We consider the two most fundamental string metrics: the Hamming distance and the edit distance. Under the Hamming distance, we search for substrings of $T$ that have at most $k$ mismatches with $P$, while under the edit distance, we search for substrings of $T$ that can be transformed to $P$ with at most $k$ edits. Exact occurrences of $P$ in $T$ have a very simple structure: If we assume for simplicity that $|T| \le 3|P|/2$ and trim $T$ so that $P$ occurs both as a prefix and as a suffix of $T$, then both $P$ and $T$ are periodic with a common period. However, an analogous characterization for the structure of occurrences with up to $k$ mismatches was proved only recently by Bringmann et al. [SODA'19]: Either there are $O(k^2)$ $k$-mismatch occurrences of $P$ in $T$, or both $P$ and $T$ are at Hamming distance $O(k)$ from strings with a common period $O(m/k)$. We tighten this characterization by showing that there are $O(k)$ $k$-mismatch occurrences in the case when the pattern is not (approximately) periodic, and we lift it to the edit distance setting, where we tightly bound the number of $k$-edit occurrences by $O(k^2)$ in the non-periodic case. Our proofs are constructive and let us obtain a unified framework for approximate pattern matching for both considered distances. We showcase the generality of our framework with results for the fully-compressed setting (where $T$ and $P$ are given as a straight-line program) and for the dynamic setting (where we extend a data structure of Gawrychowski et al. [SODA'18])

Faster Pattern Matching under Edit Distance

We consider the approximate pattern matching problem under the edit distance.Given a text $T$ of length $n$, a pattern $P$ of length $m$, and a threshold$k$, the task is to find the starting positions of all substrings of $T$ thatcan be transformed to $P$ with at most $k$ edits. More than 20 years ago, Coleand Hariharan [SODA'98, J. Comput.'02] gave an $\mathcal{O}(n+k^4 \cdot n/m)$-time algorithm for this classic problem, and this runtime has not beenimproved since. Here, we present an algorithm that runs in time $\mathcal{O}(n+k^{3.5}\sqrt{\log m \log k} \cdot n/m)$, thus breaking through this long-standingbarrier. In the case where n^{1/4+\varepsilon} \leq k \leqn^{2/5-\varepsilon} for some arbitrarily small positive constant$\varepsilon$, our algorithm improves over the state-of-the-art by polynomialfactors: it is polynomially faster than both the algorithm of Cole andHariharan and the classic $\mathcal{O}(kn)$-time algorithm of Landau andVishkin [STOC'86, J. Algorithms'89]. We observe that the bottleneck case of the alternative $\mathcal{O}(n+k^4\cdot n/m)$-time algorithm of Charalampopoulos, Kociumaka, and Wellnitz[FOCS'20] is when the text and the pattern are (almost) periodic. Our newalgorithm reduces this case to a new dynamic problem (Dynamic Puzzle Matching),which we solve by building on tools developed by Tiskin [SODA'10,Algorithmica'15] for the so-called seaweed monoid of permutation matrices. Ouralgorithm relies only on a small set of primitive operations on strings andthus also applies to the fully-compressed setting (where text and pattern aregiven as straight-line programs) and to the dynamic setting (where we maintaina collection of strings under creation, splitting, and concatenation),improving over the state of the art.<br

Encapsulation in an alginate–goats’ milk–inulin matrix improves survival of probiotic Bifidobacterium in simulated gastrointestinal conditions and goats’ milk yoghurt

In this work, a new encapsulating matrix, alginate–goats’ milk–inulin, was used to encapsulate Bifidobacterium animalis subsp. lactis BB‐12. The addition of inulin resulted in capsules with a compact structure, and a higher probiotic cell count under simulated gastrointestinal conditions and in probiotic goats’ milk yoghurt during refrigerated storage. Encapsulation of the probiotic bacteria led to slower post‐acidification yoghurts. The results of this study showed that the alginate–goats’ milk–inulin matrix has potential to be used as a new encapsulation material to encapsulate probiotics for use in goats’ milk‐based probiotic fermented dairy products, avoiding the cross‐contamination caused by using capsules based on cows’ milk

Efficient Computation of Sequence Mappability

Sequence mappability is an important task in genome re-sequencing. In the $(k,m)$-mappability problem, for a given sequence $T$ of length $n$, our goal is to compute a table whose $i$th entry is the number of indices $j \ne i$ such that length-$m$ substrings of $T$ starting at positions $i$ and $j$ have at most $k$ mismatches. Previous works on this problem focused on heuristic approaches to compute a rough approximation of the result or on the case of $k=1$. We present several efficient algorithms for the general case of the problem. Our main result is an algorithm that works in $\mathcal{O}(n \min\{m^k,\log^{k+1} n\})$ time and $\mathcal{O}(n)$ space for $k=\mathcal{O}(1)$. It requires a carefu l adaptation of the technique of Cole et al.~[STOC 2004] to avoid multiple counting of pairs of substrings. We also show $\mathcal{O}(n^2)$-time algorithms to compute all results for a fixed $m$ and all $k=0,\ldots,m$ or a fixed $k$ and all $m=k,\ldots,n-1$. Finally we show that the $(k,m)$-mappability problem cannot be solved in strongly subquadratic time for $k,m = \Theta(\log n)$ unless the Strong Exponential Time Hypothesis fails.Comment: Accepted to SPIRE 201

Response to pulmonary arterial hypertension drug therapies in patients with pulmonary arterial hypertension and cardiovascular risk factors.

The age at diagnosis of pulmonary arterial hypertension (PAH) and the prevalence of cardiovascular (CV) risk factors are increasing. We sought to determine whether the response to drug therapy was influenced by CV risk factors in PAH patients. We studied consecutive incident PAH patients (n = 146) between January 1, 2008, and July 15, 2011. Patients were divided into two groups: the PAH-No CV group included patients with no CV risk factors (obesity, systemic hypertension, type 2 diabetes mellitus, permanent atrial fibrillation, mitral and/or aortic valve disease, and coronary artery disease), and the PAH-CV group included patients with at least one. The response to PAH treatment was analyzed in all the patients who received PAH drug therapy. The PAH-No CV group included 43 patients, and the PAH-CV group included 69 patients. Patients in the PAH-No CV group were younger than those in the PAH-CV group (P < 0.0001). In the PAH-No CV group, 16 patients (37%) improved on treatment and 27 (63%) did not improve, compared with 11 (16%) and 58 (84%) in the PAH-CV group, respectively (P = 0.027 after adjustment for age). There was no difference in survival at 30 months (P = 0.218). In conclusion, in addition to older age, CV risk factors may predict a reduced response to PAH drug therapy in patients with PAH

A Gaussian moment method and its augmentation via LSTM recurrent neural networks for the statistics of cavitating bubble populations

Phase-averaged dilute bubbly flow models require high-order statistical moments of the bubble population. The method of classes, which directly evolve bins of bubbles in the probability space, are accurate but computationally expensive. Moment-based methods based upon a Gaussian closure present an opportunity to accelerate this approach, particularly when the bubble size distributions are broad (polydisperse). For linear bubble dynamics a Gaussian closure is exact, but for bubbles undergoing large and nonlinear oscillations, it results in a large error from misrepresented higher-order moments. Long short-term memory recurrent neural networks, trained on Monte Carlo truth data, are proposed to improve these model predictions. The networks are used to correct the low-order moment evolution equations and improve prediction of higher-order moments based upon the low-order ones. Results show that the networks can reduce model errors to less than 1% of their unaugmented values

Longest common substring made fully dynamic

Given two strings S and T, each of length at most n, the longest common substring (LCS) problem is to find a longest substring common to S and T. This is a classical problem in computer science with an O(n)-time solution. In the fully dynamic setting, edit operations are allowed in either of the two strings, and the problem is to find an LCS after each edit. We present the first solution to this problem requiring sublinear time in n per edit operation. In particular, we show how to find an LCS after each edit operation in Õ(n2/3) time, after Õ(n)-time and space preprocessing. 1 This line of research has been recently initiated in a somewhat restricted dynamic variant by Amir et al. [SPIRE 2017]. More specifically, they presented an Õ(n)-sized data structure that returns an LCS of the two strings after a single edit operation (that is reverted afterwards) in Õ(1) time. At CPM 2018, three papers (Abedin et al., Funakoshi et al., and Urabe et al.) studied analogously restricted dynamic variants of problems on strings. We show that the techniques we develop can be applied to obtain fully dynamic algorithms for all of these variants. The only previously known sublinear-time dynamic algorithms for problems on strings were for maintaining a dynamic collection of strings for comparison queries and for pattern matching, with the most recent advances made by Gawrychowski et al. [SODA 2018] and by Clifford et al. [STACS 2018]. As an intermediate problem we consider computing the solution for a string with a given set of k edits, which leads us, in particular, to answering internal queries on a string. The input to such a query is specified by a substring (or substrings) of a given string. Data structures for answering internal string queries that were proposed by Kociumaka et al. [SODA 2015] and by Gagie et al. [CCCG 2013] are used, along with new ones, based on ingredients such as the suffix tree, heavy-path decomposition, orthogonal range queries, difference covers, and string periodicity