A Robust Framework for Microbial Archaeology.
Ontology highlight
ABSTRACT:
SUBMITTER: Warinner C
PROVIDER: S-EPMC5581243 | biostudies-other | 2017 Aug
REPOSITORIES: biostudies-other
Publications
A Robust Framework for Microbial Archaeology.
Warinner Christina C Herbig Alexander A Mann Allison A Fellows Yates James A JA Weiß Clemens L CL Burbano Hernán A HA Orlando Ludovic L Krause Johannes J
Annual review of genomics and human genetics 20170426
Microbial archaeology is flourishing in the era of high-throughput sequencing, revealing the agents behind devastating historical plagues, identifying the cryptic movements of pathogens in prehistory, and reconstructing the ancestral microbiota of humans. Here, we introduce the fundamental concepts and theoretical framework of the discipline, then discuss applied methodologies for pathogen identification and microbiome characterization from archaeological samples. We give special attention to th ...[more]
Similar Datasets
Project description:We assessed the utilities of integrating four advanced sequencing and multiplex imaging technologies to study cancer-immune cell interactions. These four technologies include single-cell RNA sequencing and Spatial Transcriptomic (both measuring over >20,000 genes), RNA In Situ Hybridization (multiplex 4-12 genes) and Opal multiplex protein staining (4-9 proteins). We evaluated the approach to discover and validate cell-cell interaction in situ through in-depth analysis of two types of cancer, basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), which account for over 70% of skin cancer cases.
2022-07-26 | E-MTAB-11932 | biostudies-arrayexpress
Project description:The cytoskeleton is a highly dynamical protein network that plays a central role in numerous cellular physiological processes, and is traditionally divided into three components according to its chemical composition, i.e. actin, tubulin and intermediate filament cytoskeletons. Understanding the cytoskeleton dynamics is of prime importance to unveil mechanisms involved in cell adaptation to any stress type. Fluorescence imaging of cytoskeleton structures allows analyzing the impact of mechanical stimulation in the cytoskeleton, but it also imposes additional challenges in the image processing stage, such as the presence of imaging-related artifacts and heavy blurring introduced by (high-throughput) automated scans. However, although there exists a considerable number of image-based analytical tools to address the image processing and analysis, most of them are unfit to cope with the aforementioned challenges. Filamentous structures in images can be considered as a piecewise composition of quasi-straight segments (at least in some finer or coarser scale). Based on this observation, we propose a three-steps actin filaments extraction methodology: (i) first the input image is decomposed into a 'cartoon' part corresponding to the filament structures in the image, and a noise/texture part, (ii) on the 'cartoon' image, we apply a multi-scale line detector coupled with a (iii) quasi-straight filaments merging algorithm for fiber extraction. The proposed robust actin filaments image analysis framework allows extracting individual filaments in the presence of noise, artifacts and heavy blurring. Moreover, it provides numerous parameters such as filaments orientation, position and length, useful for further analysis. Cell image decomposition is relatively under-exploited in biological images processing, and our study shows the benefits it provides when addressing such tasks. Experimental validation was conducted using publicly available datasets, and in osteoblasts grown in two different conditions: static (control) and fluid shear stress. The proposed methodology exhibited higher sensitivity values and similar accuracy compared to state-of-the-art methods.
| S-EPMC4995035 | biostudies-literature
Project description:Organic frameworks (OFs) offer a novel strategy for assembling organic semiconductors into robust networks that facilitate transport, especially the covalent organic frameworks (COFs). However, poor electrical conductivity through covalent bonds and insolubility of COFs limit their practical applications in organic electronics. It is known that the two-dimensional intralayer π∙∙∙π transfer dominates transport in organic semiconductors. However, because of extremely labile inherent features of noncovalent π∙∙∙π interaction, direct construction of robust frameworks via noncovalent π∙∙∙π interaction is a difficult task. Toward this goal, we report a robust noncovalent π∙∙∙π interaction-stacked organic framework, namely πOF, consisting of a permanent three-dimensional porous structure that is held together by pure intralayer noncovalent π∙∙∙π interactions. The elaborate porous structure, with a 1.69-nm supramaximal micropore, is composed of fully conjugated rigid aromatic tetragonal-disphenoid-shaped molecules with four identical platforms. πOF shows excellent thermostability and high recyclability and exhibits self-healing properties by which the parent porosity is recovered upon solvent annealing at room temperature. Taking advantage of the long-range π∙∙∙π interaction, we demonstrate remarkable transport properties of πOF in an organic-field-effect transistor, and the mobility displays relative superiority over the traditional COFs. These promising results position πOF in a direction toward porous and yet conductive materials for high-performance organic electronics.
| S-EPMC7456091 | biostudies-literature
Project description:A large fraction of microbial life on earth exists in complex communities where metabolic exchange is vital. Microbes trade essential resources to promote their own growth in an analogous way to countries that exchange goods in modern economic markets. Inspired by these similarities, we developed a framework based on general equilibrium theory (GET) from economics to predict the population dynamics of trading microbial communities. Our biotic GET (BGET) model provides an a priori theory of the growth benefits of microbial trade, yielding several novel insights relevant to understanding microbial ecology and engineering synthetic communities. We find that the economic concept of comparative advantage is a necessary condition for mutualistic trade. Our model suggests that microbial communities can grow faster when species are unable to produce essential resources that are obtained through trade, thereby promoting metabolic specialization and increased intercellular exchange. Furthermore, we find that species engaged in trade exhibit a fundamental tradeoff between growth rate and relative population abundance, and that different environments that put greater pressure on group selection versus individual selection will promote varying strategies along this growth-abundance spectrum. We experimentally tested this tradeoff using a synthetic consortium of Escherichia coli cells and found the results match the predictions of the model. This framework provides a foundation to study natural and engineered microbial communities through a new lens based on economic theories developed over the past century.
| S-EPMC4519184 | biostudies-literature
Project description:A new class of dual metal and N doped carbon catalysts with well-defined porous structure derived from metal-organic frameworks (MOFs) has been developed as a high-performance electrocatalyst for oxygen reduction reaction (ORR). Furthermore, the microbial fuel cell (MFC) device based on the as-prepared Ni/Co and N codoped carbon as air cathode catalyst achieves a maximum power density of 4335.6 mW m-2 and excellent durability.
| S-EPMC5063114 | biostudies-literature
Project description:Functional data are increasingly encountered in scientific studies, and their high dimensionality and complexity lead to many analytical challenges. Various methods for functional data analysis have been developed, including functional response regression methods that involve regression of a functional response on univariate/multivariate predictors with nonparametrically represented functional coefficients. In existing methods, however, the functional regression can be sensitive to outlying curves and outlying regions of curves, so is not robust. In this paper, we introduce a new Bayesian method, robust functional mixed models (R-FMM), for performing robust functional regression within the general functional mixed model framework, which includes multiple continuous or categorical predictors and random effect functions accommodating potential between-function correlation induced by the experimental design. The underlying model involves a hierarchical scale mixture model for the fixed effects, random effect and residual error functions. These modeling assumptions across curves result in robust nonparametric estimators of the fixed and random effect functions which down-weight outlying curves and regions of curves, and produce statistics that can be used to flag global and local outliers. These assumptions also lead to distributions across wavelet coefficients that have outstanding sparsity and adaptive shrinkage properties, with great flexibility for the data to determine the sparsity and the heaviness of the tails. Together with the down-weighting of outliers, these within-curve properties lead to fixed and random effect function estimates that appear in our simulations to be remarkably adaptive in their ability to remove spurious features yet retain true features of the functions. We have developed general code to implement this fully Bayesian method that is automatic, requiring the user to only provide the functional data and design matrices. It is efficient enough to handle large data sets, and yields posterior samples of all model parameters that can be used to perform desired Bayesian estimation and inference. Although we present details for a specific implementation of the R-FMM using specific distributional choices in the hierarchical model, 1D functions, and wavelet transforms, the method can be applied more generally using other heavy-tailed distributions, higher dimensional functions (e.g. images), and using other invertible transformations as alternatives to wavelets.
| S-EPMC3270884 | biostudies-literature
Project description:Case-control designs are commonly employed in genetic association studies. In addition to the case-control status, data on secondary traits are often collected. Directly regressing secondary traits on genetic variants from a case-control sample often leads to biased estimation. Several statistical methods have been proposed to address this issue. The inverse probability weighting (IPW) approach and the semiparametric maximum-likelihood (SPML) approach are the most commonly used. A new weighted estimating equation (WEE) approach is proposed to provide unbiased estimation of genetic associations with secondary traits, by combining observed and counterfactual outcomes. Compared to the existing approaches, WEE is more robust against biased sampling and disease model misspecification. We conducted simulations to evaluate the performance of the WEE under various models and sampling schemes. The WEE demonstrated robustness in all scenarios investigated, had appropriate type I error, and was as powerful or more powerful than the IPW and SPML approaches. We applied the WEE to an asthma case-control study to estimate the associations between the thymic stromal lymphopoietin gene and two secondary traits: overweight status and serum IgE level. The WEE identified two SNPs associated with overweight in logistic regression, three SNPs associated with serum IgE levels in linear regression, and an additional four SNPs that were missed in linear regression to be associated with the 75th quantile of IgE in quantile regression. The WEE approach provides a general and robust secondary analysis framework, which complements the existing approaches and should serve as a valuable tool for identifying new associations with secondary traits.
| S-EPMC4827729 | biostudies-literature
Project description:Researchers are regularly interested in interpreting the multipartite structure of data entities according to their functional relationships. Data is often heterogeneous with intricately hidden inner structure. With limited prior knowledge, researchers are likely to confront the problem of transforming this data into knowledge. We develop a new framework, called heat-passing, which exploits intrinsic similarity relationships within noisy and incomplete raw data, and constructs a meaningful map of the data. The proposed framework is able to rank, cluster, and visualize the data all at once. The novelty of this framework is derived from an analogy between the process of data interpretation and that of heat transfer, in which all data points contribute simultaneously and globally to reveal intrinsic similarities between regions of data, meaningful coordinates for embedding the data, and exemplar data points that lie at optimal positions for heat transfer. We demonstrate the effectiveness of the heat-passing framework for robustly partitioning the complex networks, analyzing the globin family of proteins and determining conformational states of macromolecules in the presence of high levels of noise. The results indicate that the methodology is able to reveal functionally consistent relationships in a robust fashion with no reference to prior knowledge. The heat-passing framework is very general and has the potential for applications to a broad range of research fields, for example, biological networks, social networks and semantic analysis of documents.
| S-EPMC4323200 | biostudies-literature
Project description:A critical challenge faced by sustainability science is to develop strategies to cope with highly uncertain social and ecological dynamics. This article explores the use of the robust control framework toward this end. After briefly outlining the robust control framework, we apply it to the traditional Gordon-Schaefer fishery model to explore fundamental performance-robustness and robustness-vulnerability trade-offs in natural resource management. We find that the classic optimal control policy can be very sensitive to parametric uncertainty. By exploring a large class of alternative strategies, we show that there are no panaceas: even mild robustness properties are difficult to achieve, and increasing robustness to some parameters (e.g., biological parameters) results in decreased robustness with respect to others (e.g., economic parameters). On the basis of this example, we extract some broader themes for better management of resources under uncertainty and for sustainability science in general. Specifically, we focus attention on the importance of a continual learning process and the use of robust control to inform this process.
| S-EPMC2000534 | biostudies-literature
Project description:Microbes form complex communities that perform critical roles for the integrity of their environment or the well-being of their hosts. Controlling these microbial communities can help us restore natural ecosystems and maintain healthy human microbiota. However, the lack of an efficient and systematic control framework has limited our ability to manipulate these microbial communities. Here we fill this gap by developing a control framework based on the new notion of structural accessibility. Our framework uses the ecological network of the community to identify minimum sets of its driver species, manipulation of which allows controlling the whole community. We numerically validate our control framework on large communities, and then we demonstrate its application for controlling the gut microbiota of gnotobiotic mice infected with Clostridium difficile and the core microbiota of the sea sponge Ircinia oros. Our results provide a systematic pipeline to efficiently drive complex microbial communities towards desired states.
| S-EPMC6401173 | biostudies-literature