33 research outputs found

    Microbial Community Dynamics of Lactate Enriched Hanford Groundwaters

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    The Department of Energy site at Hanford, WA, has been historically impacted by U and Cr from the nuclear weapons industry. In an attempt to stimulate microbial remediation of these metals, in-situ lactate enrichment experiments are ongoing. In order to bridge the gap from the laboratory to the field, we inoculated triplicate anaerobic, continuous-flow glass reactors with groundwater collected from well Hanford 100-H in order to obtain a stable, enriched community while selecting for metal-reducing bacteria. Each reactor was fed from a single carboy containing defined media with 30 mM lactate at a rate of 0.223 ml/min under continuous nitrogen flow at 9 ml/min. Cell counts, organic acids, gDNA (for qPCR and pyrosequencing) and gases were sampled during the experiment. Cell counts remained low (less than 1x107 cells/ml) during the first two weeks of the experiment, but by day 20, had reached a density greater than 1x108 cells/ml. Metabolite analysis showed a decrease in the lactate concentrations over time. Pyruvate concentrations ranged from 20-40 uM the first week of the experiment then was undetectable after day 10. Likewise, formate appeared in the reactors during the first week with concentrations of 1.48-1.65 mM at day 7 then the concentrations decreased to 0.69-0.95 on day 10 and were undetectable on day 15. Acetate was present in low amounts on day 3 (0.15-0.33 mM) and steadily increased to 3.35-5.22 mM over time. Similarly, carbon dioxide was present in low concentrations early on and increased to 0.28-0.35 mM as the experiment progressed. We also were able to detect low amounts of methane (10-20 uM) during the first week of the experiment, but by day 10 the methane was undetectable. From these results and pyrosequencing analysis, we conclude that a shift in the microbial community dynamics occurred over time to eventually form a stable and enriched microbial community. Comprehensive investigations such as these allow for the examination of not only which nutrient source will accelerate site remediation, but also provide insight to evaluate remediation strategies through which enriched community members are important for bioremediation

    Struktur und Werkzeuge des experiment-spezifischen Datenbereichs der SFB501 Erfahrungsdatenbank

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    Software-Entwicklungsartefakte müssen zielgerichtet während der Durchführung eines Software- Projekts erfasst werden, um für die Wiederverwendung aufbereitet werden zu können. Die methodische Basis hierzu bildet im Sonderforschungsbereich 501 das Konzept der Erfahrungsdatenbank. In ihrem experiment-spezifischen Datenbereich werden für jedes Entwicklungsprojekt alle Software-Entwicklungsartefakte abgelegt, die während des Lebenszyklus eines Projektes anfallen. In ihrem übergreifenden Datenbereich werden all die jenigen Artefakte aus dem experiment-spezifischen Datenbereich zusammengefasst, die für eine Wiederverwendung in nachfolgenden Projekten in Frage kommen. Es hat sich gezeigt, dass bereits zur Nutzung der Datenmengen im experiment- spezifischen Datenbereich der Erfahrungsdatenbank ein systematischer Zugriff notwendig ist. Ein systematischer Zugriff setzt jedoch eine normierte Struktur voraus. Im experiment-spezifischen Bereich werden zwei Arten von Experimenttypen unterschieden: "Kontrollierte Experimente" und "Fallstudien". Dieser Bericht beschreibt die Ablage- und Zugriffsstruktur für den Experimenttyp "Fallstudien". Die Struktur wurde aufgrund der Erfahrungen in ersten Fallstudien entwickelt und evaluiert

    Struktur und Werkzeuge des experiment-spezifischen Datenbereichs der SFB501 Erfahrungsdatenbank

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    Software-Entwicklungsartefakte müssen zielgerichtet während der Durchführung eines Software- Projekts erfasst werden, um für die Wiederverwendung aufbereitet werden zu können. Die methodische Basis hierzu bildet im Sonderforschungsbereich 501 das Konzept der Erfahrungsdatenbank. In ihrem experiment-spezifischen Datenbereich werden für jedes Entwicklungsprojekt alle Software-Entwicklungsartefakte abgelegt, die während des Lebenszyklus eines Projektes anfallen. In ihrem übergreifenden Datenbereich werden all die jenigen Artefakte aus dem experiment-spezifischen Datenbereich zusammengefasst, die für eine Wiederverwendung in nachfolgenden Projekten in Frage kommen. Es hat sich gezeigt, dass bereits zur Nutzung der Datenmengen im experiment- spezifischen Datenbereich der Erfahrungsdatenbank ein systematischer Zugriff notwendig ist. Ein systematischer Zugriff setzt jedoch eine normierte Struktur voraus. Im experiment-spezifischen Bereich werden zwei Arten von Experimenttypen unterschieden: "Kontrollierte Experimente" und "Fallstudien". Dieser Bericht beschreibt die Ablage- und Zugriffsstruktur für den Experimenttyp "Fallstudien". Die Struktur wurde aufgrund der Erfahrungen in ersten Fallstudien entwickelt und evaluiert

    The importance of correcting for sampling bias in MaxEnt species distribution models

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    Aim:Advancement in ecological methods predicting species distributions is a crucial precondition for deriving sound management actions. Maximum entropy (MaxEnt) models are a popular tool to predict species distributions, as they are considered able to cope well with sparse, irregularly sampled data and minor location errors. Although a fundamental assumption of MaxEnt is that the entire area of interest has been systematically sampled, in practice, MaxEnt models are usually built from occurrence records that are spatially biased towards better‐surveyed areas. Two common, yet not compared, strategies to cope with uneven sampling effort are spatial filtering of occurrence data and background manipulation using environmental data with the same spatial bias as occurrence data. We tested these strategies using simulated data and a recently collated dataset on Malay civet Viverra tangalunga in Borneo.Location:Borneo, Southeast Asia.Methods:We collated 504 occurrence records of Malay civets from Borneo of which 291 records were from 2001 to 2011 and used them in the MaxEnt analysis (baseline scenario) together with 25 environmental input variables. We simulated datasets for two virtual species (similar to a range‐restricted highland and a lowland species) using the same number of records for model building. As occurrence records were biased towards north‐eastern Borneo, we investigated the efficacy of spatial filtering versus background manipulation to reduce overprediction or underprediction in specific areas.Results:Spatial filtering minimized omission errors (false negatives) and commission errors (false positives). We recommend that when sample size is insufficient to allow spatial filtering, manipulation of the background dataset is preferable to not correcting for sampling bias, although predictions were comparatively weak and commission errors increased.Main Conclusions:We conclude that a substantial improvement in the quality of model predictions can be achieved if uneven sampling effort is taken into account, thereby improving the efficacy of species conservation planning.publishe

    Pan-cancer proteogenomics connects oncogenic drivers to functional states

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    Cancer driver events refer to key genetic aberrations that drive oncogenesis; however, their exact molecular mechanisms remain insufficiently understood. Here, our multi-omics pan-cancer analysis uncovers insights into the impacts of cancer drivers by identifying their significant cis-effects and distal trans-effects quantified at the RNA, protein, and phosphoprotein levels. Salient observations include the association of point mutations and copy-number alterations with the rewiring of protein interaction networks, and notably, most cancer genes converge toward similar molecular states denoted by sequence-based kinase activity profiles. A correlation between predicted neoantigen burden and measured T cell infiltration suggests potential vulnerabilities for immunotherapies. Patterns of cancer hallmarks vary by polygenic protein abundance ranging from uniform to heterogeneous. Overall, our work demonstrates the value of comprehensive proteogenomics in understanding the functional states of oncogenic drivers and their links to cancer development, surpassing the limitations of studying individual cancer types
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