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Lactate can be produced by many gut bacteria, but in adults its accumulation in the colon is often an indicator of microbiota perturbation. Using continuous culture anaerobic fermentor systems, we found that lactate concentrations remained low in communities of human colonic bacteria maintained at pH 6.5, even when DL-lactate was infused at 10 or 20 mM. In contrast, lower pH (5.5) led to periodic lactate accumulation following lactate infusion in three fecal microbial communities examined. Lactate accumulation was concomitant with greatly reduced butyrate and propionate production and major shifts in microbiota composition, with Bacteroidetes and anaerobic Firmicutes being replaced by Actinobacteria, lactobacilli, and Proteobacteria. Pure-culture experiments confirmed that Bacteroides and Firmicutes isolates were susceptible to growth inhibition by relevant concentrations of lactate and acetate, whereas the lactate-producer Bifidobacterium adolescentis was resistant. To investigate system behavior further, we used a mathematical model (microPop) based on 10 microbial functional groups. By incorporating differential growth inhibition, our model reproduced the chaotic behavior of the system, including the potential for lactate infusion both to promote and to rescue the perturbed system. The modeling revealed that system behavior is critically dependent on the proportion of the community able to convert lactate into butyrate or propionate. Communities with low numbers of lactate-utilizing bacteria are inherently less stable and more prone to lactate-induced perturbations. These findings can help us to understand the consequences of interindividual microbiota variation for dietary responses and microbiota changes associated with disease states. IMPORTANCE Lactate is formed by many species of colonic bacteria, and can accumulate to high levels in the colons of inflammatory bowel disease subjects. Conversely, in healthy colons lactate is metabolized by lactate-utilizing s

Contains fulltext : 207578.pdf (publisher's version ) (Closed access), Semantic unification during sentence comprehension has been associated with amplitude change of the N400 in event-related potential (ERP) studies, and activation in the left inferior frontal gyrus (IFG) in functional magnetic resonance imaging (fMRI) studies. However, the specificity of this activation to semantic unification remains unknown. To more closely examine the brain processes involved in semantic unification, we employed simultaneous EEG-fMRI to time-lock the semantic unification related N400 change, and integrated trial-by-trial variation in both N400 and BOLD change beyond the condition-level BOLD change difference measured in traditional fMRI analyses. Participants read sentences in which semantic unification load was parametrically manipulated by varying cloze probability. Separately, ERP and fMRI results replicated previous findings, in that semantic unification load parametrically modulated the amplitude of N400 and cortical activation. Integrated EEG-fMRI analyses revealed a different pattern in which functional activity in the left IFG and bilateral supramarginal gyrus (SMG) was associated with N400 amplitude, with the left IFG activation and bilateral SMG activation being selective to the condition-level and trial-level of semantic unification load, respectively. By employing the EEG-fMRI integrated analyses, this study among the first sheds light on how to integrate trial-level variation in language comprehension.

Atrioventricular septal defect (AVSD) is a clinically significant subtype of congenital heart disease (CHD) that severely influences the health of babies during birth and is associated with Down syndrome (DS). Thus, exploring the differences in functional genes in DS samples with and without AVSD is a critical way to investigate the complex association between AVSD and DS. In this study, we present a computational method to distinguish DS patients with AVSD from those without AVSD using the newly proposed self-normalizing neural network (SNN). First, each patient was encoded by using the copy number of probes on chromosome 21. The encoded features were ranked by the reliable Monte Carlo feature selection (MCFS) method to obtain a ranked feature list. Based on this feature list, we used a two-stage incremental feature selection to construct two series of feature subsets and applied SNNs to build classifiers to identify optimal features. Results show that 2737 optimal features were obtained, and the corresponding optimal SNN classifier constructed on optimal features yielded a Matthew’s correlation coefficient (MCC) value of 0.748. For comparison, random forest was also used to build classifiers and uncover optimal features. This method received an optimal MCC value of 0.582 when top 132 features were utilized. Finally, we analyzed some key features derived from the optimal features in SNNs found in literature support to further reveal their essential roles.