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Increased dietary fiber consumption has been shown to increase human gut microbial diversity, but the mechanisms driving this effect remain unclear. One possible explanation is that microbes are able to divide metabolic labor in consumption of complex carbohydrates, which are composed of diverse glycosidic linkages that require specific cognate enzymes for degradation. However, as naturally derived fibers vary in both sugar composition and linkage structure, it is challenging to separate out the impact of each of these variables. We hypothesized that fine differences in carbohydrate linkage structure would govern microbial community structure and function independently of variation in glycosyl residue composition. To test this hypothesis, we fermented commercially available soluble resistant glucans, which are uniformly composed of glucose linked in different structural arrangements, in vitro with fecal inocula from each of three individuals. We measured metabolic outputs (pH, gas, and short-chain fatty acid production) and community structure via 16S rRNA amplicon sequencing. We determined that community metabolic outputs from identical glucans were highly individual, emerging from divergent initial microbiome structures. However, specific operational taxonomic units responded similarly in growth responses across individuals’ microbiota, though in context-dependent ways; these data suggested that certain taxa were more efficient in competing for some structures than others. Together, these data support the hypothesis that variation in linkage structure, independent of sugar composition, governs compositional and functional responses of microbiota.ImportancePrevious studies have reported how physical and chemical structures of carbohydrates influence the gut microbiota, however, variability across dietary fibers in monosaccharide composition and linkage structure obscures the relationship between fine polysaccharide linkage structure and microbial fitness. Revealing connections between subtle differences in glucan structure and microbial composition and metabolic responses, this study suggests much greater attention to substrate structure in the design of experiments to test fiber-microbiome responses in vitro and in vivo. Further, it underscores that, although microbiome responses to distinct fibers are individual and vary among specific glucans, similar carbohydrate structure-microbe relationships occur across individual donor communities. Together, these data may help explain why some individuals may respond (while others do not) to fiber treatments in human feeding trials and support the long-term goal of rational inclusion of specific fibers in dietary patterns to modulate the gut microbiome in support of health.

Living systems propagate by undergoing rounds of cell growth and division. Cell division is at heart a physical process that requires mechanical forces, usually exerted by protein assemblies. Here we developed the first physical model for the division of archaeal cells, which despite their structural simplicity share machinery and evolutionary origins with eukaryotes. We show how active geometry changes of elastic ESCRT-III filaments, coupled to filament disassembly, are sufficient to efficiently split the cell. We explore how the non-equilibrium processes that govern the filament behaviour impact the resulting cell division. We show how a quantitative comparison between our simulations and dynamic data for ESCRTIII-mediated division in Sulfolobus acidocaldarius, the closest archaeal relative to eukaryotic cells that can currently be cultured in the lab, and reveal the most likely physical mechanism behind its division.

Face anti-spoofing approaches based on domain generalization (DG) have drawn growing attention due to their robustness for unseen scenarios. Previous methods treat each sample from multiple domains indiscriminately during the training process, and endeavor to extract a common feature space to improve the generalization. However, due to complex and biased data distribution, directly treating them equally will corrupt the generalization ability. To settle the issue, we propose a novel Dual Reweighting Domain Generalization (DRDG) framework which iteratively reweights the relative importance between samples to further improve the generalization. Concretely, Sample Reweighting Module is first proposed to identify samples with relatively large domain bias, and reduce their impact on the overall optimization. Afterwards, Feature Reweighting Module is introduced to focus on these samples and extract more domain-irrelevant features via a self-distilling mechanism. Combined with the domain discriminator, the iteration of the two modules promotes the extraction of generalized features. Extensive experiments and visualizations are presented to demonstrate the effectiveness and interpretability of our method against the state-of-the-art competitors., Comment: accepted on IJCAI 2021

BackgroundRecently, various studies have identified circular RNAs (circRNAs) to play a significant role in tumorigenesis, thereby showing potential as novel tumor biomarkers. circSIPA1L1 is a new-found circular RNA formed by back-splicing of SIPA1L1 and is found increased in osteosarcoma (OS). Nevertheless, the specific functions of circSIPA1L1 in OS remain unknown. MethodsIn the present study, circSIPA1L1 was obtained from a previously reported circRNA microarray (GSE96964) in the GEO database. Quantitative real-time polymerase chain reaction (qRT-PCR) was employed to assess the mRNA level of circSIPA1L1 in OS cell lines and tissue samples. Bioinformatics analysis, luciferase reporter assays, real-time PCR, RNA pull-down assays and RNA immunoprecipitation (RIP) were employed to verify the binding of circSIPA1L1 with miR-411-5p. Xenograft tumor models were established to identify the role of circSIPA1L1 in vivo. A series of in-vitro experiments, such as western blotting , colony formation, transwell assays and anoikis assay were employed to confirm the relationship across circSIPA1L1, miR-411-5p, and RAB9A. ResultsOur study confirmed circSIPA1L1 to be upregulated in both human OS samples and OS cell lines. Mechanistically, circSIPA1L1 could serve as a miR-411-5p molecular sponge to increase RAB9A expression, which was confirmed to be a tumor promoter mediating carcinogenesis. Silencing of circSIPA1L1 attenuated the vitality, invasion, migration and proliferation of OS cell lines both in vivo and in vitro. miR-411-5p inhibition or RAB9A overexpression reversed the anti-tumor effects caused by circSIPA1L1 knockdown. ConclusionBriefly, circSIPA1L1 acts as a driver gene in OS and could initiate OS tumorigenesis via the miR-411-5p/RAB9A axis, which might become a potential therapeutic biomarker for OS treatment.

Eliciting all the components of prospect theory –curvature of the utility function, weighting function and loss aversion– remains an open empirical challenge. We develop a semi-parametric method that keeps the tractability of parametric methods while providing more precise estimates. Using the data of Tversky and Kahneman (1992), we revisit their main parametric results. We reject the convexity of the utility function in the loss domain, find lower probability weighting, and confirm loss aversion. We also report that the probability weighting function does not exhibit duality and equality across domains, in line with cumulative prospect theory and in contrast with original prospect and rank dependent utility theories.

In Africa, there are many countries that rarely know the change of their president. The aim of this paper is to study the economic and non-economic determinants of number of power alternation in Africa over the period 1990-2015. The poisson regression is used. The results indicate that the growth of the gross domestic product per capita has a negative impact on the number of political change while the democracy index and the coup have a positive influence.Keyswords: poisson model, power alternation, AfricaJEL Classifications: D72, C53DOI: https://doi.org/10.32479/ijefi.6335