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    Food-breastmilk combinations alter the colonic microbiome of weaning infants: an in silico study
    (American Society for Microbiology, 2024-09) da Silva VG; Smith NW; Mullaney JA; Wall C; Roy NC; McNabb WC; Garrido D
    The introduction of solid foods to infants, also known as weaning, is a critical point for the development of the complex microbial community inhabiting the human colon, impacting host physiology in infancy and later in life. This research investigated in silico the impact of food-breastmilk combinations on growth and metabolite production by colonic microbes of New Zealand weaning infants using the metagenome-scale metabolic model named Microbial Community. Eighty-nine foods were individually combined with breastmilk, and the 12 combinations with the strongest influence on the microbial production of short-chain fatty acids (SCFAs) and branched-chain fatty acids (BCFAs) were identified. Fiber-rich and polyphenol-rich foods, like pumpkin and blackcurrant, resulted in the greatest increase in predicted fluxes of total SCFAs and individual fluxes of propionate and acetate when combined, respectively, with breastmilk. Identified foods were further combined with other foods and breastmilk, resulting in 66 multiple food-breastmilk combinations. These combinations altered in silico the impact of individual foods on the microbial production of SCFAs and BCFAs, suggesting that the interaction between the dietary compounds composing a meal is the key factor influencing colonic microbes. Blackcurrant combined with other foods and breastmilk promoted the greatest increase in the production of acetate and total SCFAs, while pork combined with other foods and breastmilk decreased the production of total BCFAs. IMPORTANCE Little is known about the influence of complementary foods on the colonic microbiome of weaning infants. Traditional in vitro and in vivo microbiome methods are limited by their resource-consuming concerns. Modeling approaches represent a promising complementary tool to provide insights into the behavior of microbial communities. This study evaluated how foods combined with other foods and human milk affect the production of short-chain fatty acids and branched-chain fatty acids by colonic microbes of weaning infants using a rapid and inexpensive in silico approach. Foods and food combinations identified here are candidates for future experimental investigations, helping to fill a crucial knowledge gap in infant nutrition.
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    Extensive bacteriocin gene shuffling in the Streptococcus bovis/Streptococcus equinus complex reveals gallocin D with activity against vancomycin resistant enterococci.
    (Springer Nature Limited, 2020-08-10) Hill D; O'Connor PM; Altermann E; Day L; Hill C; Stanton C; Ross RP
    Streptococcus gallolyticus LL009 produces gallocin D, a narrow spectrum two component bacteriocin with potent activity against vancomycin-resistant enterococci. Gallocin D is distinct from gallocin A, a separate two component bacteriocin produced by S. gallolyticus. Although the gene clusters encoding gallocin A and gallocin D have a high degree of gene synteny, the structural genes are highly variable and appear to have undergone gene shuffling with other streptococcal species. Gallocin D was analysed in laboratory-based experiments. The mature peptides are 3,343 ± 1 Da and 3,019 ± 1 Da and could be readily synthesized and display activity against a vancomycin resistant Enterococcus strain EC300 with a MIC value of 1.56 µM. Importantly, these bacteriocins could contribute to the ability of S. gallolyticus to colonize the colon where they have been associated with colorectal cancer.
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    XSim version 2: simulation of modern breeding programs
    (Oxford University Press on behalf of Genetics Society of America, 2022-04-04) Chen CJ; Garrick D; Fernando R; Karaman E; Stricker C; Keehan M; Cheng H; de Koning D-J
    Simulation can be an efficient approach to design, evaluate, and optimize breeding programs. In the era of modern agriculture, breeding programs can benefit from a simulator that integrates various sources of big data and accommodates state-of-the-art statistical models. The initial release of XSim, in which stochastic descendants can be efficiently simulated with a drop-down strategy, has mainly been used to validate genomic selection results. In this article, we present XSim Version 2 that is an open-source tool and has been extensively redesigned with additional features to meet the needs in modern breeding programs. It seamlessly incorporates multiple statistical models for genetic evaluations, such as GBLUP, Bayesian alphabets, and neural networks, and it can effortlessly simulate successive generations of descendants based on complex mating schemes by the aid of its modular design. Case studies are presented to demonstrate the flexibility of XSim Version 2 in simulating crossbreeding in animal and plant populations. Modern biotechnology, including double haploids and embryo transfer, can all be simultaneously integrated into the mating plans that drive the simulation. From a computing perspective, XSim Version 2 is implemented in Julia, which is a computer language that retains the readability of scripting languages (e.g. R and Python) without sacrificing much computational speed compared to compiled languages (e.g. C). This makes XSim Version 2 a simulation tool that is relatively easy for both champions and community members to maintain, modify, or extend in order to improve their breeding programs. Functions and operators are overloaded for a better user interface so they may concatenate, subset, summarize, and organize simulated populations at each breeding step. With the strong and foreseeable demands in the community, XSim Version 2 will serve as a modern simulator bridging the gaps between theories and experiments with its flexibility, extensibility, and friendly interface.
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    A multi-objective genetic algorithm to find active modules in multiplex biological networks
    (PLOS, 2021-08-30) Novoa-Del-Toro EM; Mezura-Montes E; Vignes M; Térézol M; Magdinier F; Tichit L; Baudot A; Jensen P
    The identification of subnetworks of interest-or active modules-by integrating biological networks with molecular profiles is a key resource to inform on the processes perturbed in different cellular conditions. We here propose MOGAMUN, a Multi-Objective Genetic Algorithm to identify active modules in MUltiplex biological Networks. MOGAMUN optimizes both the density of interactions and the scores of the nodes (e.g., their differential expression). We compare MOGAMUN with state-of-the-art methods, representative of different algorithms dedicated to the identification of active modules in single networks. MOGAMUN identifies dense and high-scoring modules that are also easier to interpret. In addition, to our knowledge, MOGAMUN is the first method able to use multiplex networks. Multiplex networks are composed of different layers of physical and functional relationships between genes and proteins. Each layer is associated to its own meaning, topology, and biases; the multiplex framework allows exploiting this diversity of biological networks. We applied MOGAMUN to identify cellular processes perturbed in Facio-Scapulo-Humeral muscular Dystrophy, by integrating RNA-seq expression data with a multiplex biological network. We identified different active modules of interest, thereby providing new angles for investigating the pathomechanisms of this disease.
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    On the use of optimal search algorithms with artificial potential field for robot soccer navigation : Computer Science, Master of Science
    (Massey University, 2018) Dong, Chen
    The artificial potential field (APF) is a popular method of choice for robot navigation, as it offers an intuitive model clearly defining all attractive and repulsive forces acting on the robot [3] [25] [29] [43] [50]. However, there are drawbacks that limit the usage of this method. For instance, the local minima problem that gets a robot trapped, and the Goal-Non-Reachable-with-Obstacle-Nearby (GNRON) problem, as reported in [51] [5] [23] [2] and [3]. In order to avoid these limitations, this research focuses on devising a methodology of combining the artificial potential field with a selection of optimal search algorithms. This work investigates the performance of the method when using different optimal search algorithms such as the A* algorithm and the any-angle path-planning Theta* Search, in combination with different types of artifcial potential field generators. We also present a novel integration technique, whereby the Potential Field approach is utilized as an internal component of an optimal search algorithm, considering the safeness of the calculated paths. Furthermore, this study also explores the optimization of several auxiliary algorithms used in conjunction with the APF-Optimal search integration: There are three different methods proposed for implementing the line-of-sight (LOS) component of the Theta* search, namely the simple line-of-sight checking algorithm, the modified Bresenham's line algorithm and the modified Cohen-Sutherland algorithm. Contrary to the studies presented in [5], [42], [48] and [40] where the APF and the optimal search algorithms were used separately, in this research, an integrative methodology involving the APF inside the optimal search with a newly proposed Safety Factor (SF) is explored. Experiment results indicate that the APF-A* Search with the SF can reduce the number of state expansions and therefore also the running time up to 19.61%, while maintaining the safeness of the path, as compared to APF-A* when not using the SF. Furthermore, this research also explores how the proposed hybrid algorithms can be used in developing multi-objective behaviours of single robot. In this regard, a robot soccer simulation platform with a physics engine is developed as well to support the exploration. Lastly, the performance of the proposed algorithms is examined under varying environment conditions. Evidences are provided showing that the method can be used in constructing the intelligence for a robot goal keeper and a robot attacker (ball shooter). A multitude of AI robot behaviours using the proposed methods are integrated via a finite state machine including: defensive positioning/parking, ball kicking/shooting, and target pursuing behaviours. Keywords : Artificial Potential Field, Optimal Searches, Robot Navigation, Multi- objective Behaviours.