Phosphorus loadings to the world's largest lakes: sources and trends

Eutrophication is a major water quality issue in lakes worldwide and is principally caused by the loadings of phosphorus from catchment areas. It follows that to develop strategies to mitigate eutrophication, we must have a good understanding of the amount, sources, and trends of phosphorus pollution. This paper provides the first consistent and harmonious estimates of current phosphorus loadings to the world's largest 100 lakes, along with the sources of these loadings and their trends. These estimates provide a perspective on the extent of lake eutrophication worldwide, as well as potential input to the evaluation and management of eutrophication in these lakes. We take a modeling approach and apply the WorldQual model for these estimates. The advantage of this approach is that it allows us to fill in large gaps in observational data. From the analysis, we find that about 66 of the 100 lakes are located in developing countries and their catchments have a much larger average phosphorus yield than the lake catchments in developed countries (11.1 versus 0.7 kg TP km−2 year−1). Second, the main source of phosphorus to the examined lakes is inorganic fertilizer (47% of total). Third, between 2005–2010 and 1990–1994, phosphorus pollution increased at 50 out of 100 lakes. Sixty percent of lakes with increasing pollution are in developing countries. P pollution changed primarily due to changing P fertilizer use. In conclusion, we show that the risk of P‐stimulated eutrophication is higher in developing countries

Genesis-DB: a database for autonomous laboratory systems

Artificial intelligence (AI)-driven laboratory automation - combining robotic labware and autonomous software agents - is a powerful trend in modern biology. We developed Genesis-DB, a database system designed to support AI-driven autonomous laboratories by providing software agents access to large quantities of structured domain information. In addition, we present a new ontology for modeling data and metadata from autonomously performed yeast microchemostat cultivations in the framework of the Genesis robot scientist system. We show an example of how Genesis-DB enables the research life cycle by modeling yeast gene regulation, guiding future hypotheses generation and design of experiments. Genesis-DB supports AI-driven discovery through automated reasoning and its design is portable, generic, and easily extensible to other AI-driven molecular biology laboratory data and beyond

EMBARCAÇÃO SOLAR DE PEQUENO PORTE COMO OBJETO DE PESQUISA PARA O DESENVOLVIMENTO E DIVULGAÇÃO DO USO DE TECNOLOGIAS ASSOCIADAS À ENERGIAS LIMPAS

Como proposta alternativa à veículos que popularmente utilizam motores a combustão de baixa eficiência e elevado impacto ambiental, este projeto busca estudar e desenvolver cada um dos componentes necessários para a construção de uma embarcação energeticamente eficiente, a fim de substituir a queima de combustíveis fósseis pela captação de energia solar por painéis fotovoltaicos. Abrangendo estudos em diversas subáreas da mecânica e elétrica, o barco solar de pequeno porte brevemente descrito neste artigo é desenvolvido para utilização em ralis nos quais toda a energia disponível para a propulsão dos veículos é proveniente da luz do sol. Buscando demonstrar a aplicabilidade dos veículos solares e promover o uso de tecnologias mais sustentáveis que primam pela eficiência energética, é apresentado o funcionamento de cada parte da embarcação, contendo a descrição dos principais dispositivos necessários para o controle, monitoração e segurança deste tipo de embarcação.

AutonoMS

<p>This repository contains the raw data, processed output, and visualization scripts from the Agilent RapidFire (365) - 6560 IM-QTOF runs from the pilot AutonoMS studies described in the manuscript.</p&gt

Drift-Diffusion Modeling of a Perceptual Decision-Making Task

Decision-making has long been a subject of particular interest to neuroscientists. This work begins by describing two distinct types of decisions: perceptual and value-based. The accumulator model, a framework that has proven itself relevant and successful in describing the decision-making process, is then covered. This is expanded upon in a discussion of the drift-diffusion implementation of accumulator models. The two-alternative forced task experimental paradigm is then introduced before describing a perceptual experiment in which rats were prompted to choose between click trains according to their size. A specific drift-diffusion model used to fit the data from this experiment is outlined, including the process of adding a new parameter to the model and testing it. This work concludes by introducing a data-interface created for the rat auditory click experimen

High-throughput metabolomics for the design and validation of a diauxic shift model.

Funder: The Wallenberg AI, Autonomous Systems and Software ProgramSaccharomyces cerevisiae is a very well studied organism, yet ∼20% of its proteins remain poorly characterized. Moreover, recent studies seem to indicate that the pace of functional discovery is slow. Previous work has implied that the most probable path forward is via not only automation but fully autonomous systems in which active learning is applied to guide high-throughput experimentation. Development of tools and methods for these types of systems is of paramount importance. In this study we use constrained dynamical flux balance analysis (dFBA) to select ten regulatory deletant strains that are likely to have previously unexplored connections to the diauxic shift. We then analyzed these deletant strains using untargeted metabolomics, generating profiles which were then subsequently investigated to better understand the consequences of the gene deletions in the metabolic reconfiguration of the diauxic shift. We show that metabolic profiles can be utilised to not only gaining insight into cellular transformations such as the diauxic shift, but also on regulatory roles and biological consequences of regulatory gene deletion. We also conclude that untargeted metabolomics is a useful tool for guidance in high-throughput model improvement, and is a fast, sensitive and informative approach appropriate for future large-scale functional analyses of genes. Moreover, it is well-suited for automated approaches due to relative simplicity of processing and the potential to make massively high-throughput

High-throughput metabolomics for the design and validation of a diauxic shift model.

Acknowledgements: This work was partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation, and the Swedish Research Council Formas (2020-01690).Funder: The Wallenberg AI, Autonomous Systems and Software Program (WASP)Saccharomyces cerevisiae is a very well studied organism, yet ∼20% of its proteins remain poorly characterized. Moreover, recent studies seem to indicate that the pace of functional discovery is slow. Previous work has implied that the most probable path forward is via not only automation but fully autonomous systems in which active learning is applied to guide high-throughput experimentation. Development of tools and methods for these types of systems is of paramount importance. In this study we use constrained dynamical flux balance analysis (dFBA) to select ten regulatory deletant strains that are likely to have previously unexplored connections to the diauxic shift. We then analyzed these deletant strains using untargeted metabolomics, generating profiles which were then subsequently investigated to better understand the consequences of the gene deletions in the metabolic reconfiguration of the diauxic shift. We show that metabolic profiles can be utilised to not only gaining insight into cellular transformations such as the diauxic shift, but also on regulatory roles and biological consequences of regulatory gene deletion. We also conclude that untargeted metabolomics is a useful tool for guidance in high-throughput model improvement, and is a fast, sensitive and informative approach appropriate for future large-scale functional analyses of genes. Moreover, it is well-suited for automated approaches due to relative simplicity of processing and the potential to make massively high-throughput

High-throughput metabolomics for the design and validation of a diauxic shift model

Abstract Saccharomyces cerevisiae is a very well studied organism, yet ∼20% of its proteins remain poorly characterized. Moreover, recent studies seem to indicate that the pace of functional discovery is slow. Previous work has implied that the most probable path forward is via not only automation but fully autonomous systems in which active learning is applied to guide high-throughput experimentation. Development of tools and methods for these types of systems is of paramount importance. In this study we use constrained dynamical flux balance analysis (dFBA) to select ten regulatory deletant strains that are likely to have previously unexplored connections to the diauxic shift. We then analyzed these deletant strains using untargeted metabolomics, generating profiles which were then subsequently investigated to better understand the consequences of the gene deletions in the metabolic reconfiguration of the diauxic shift. We show that metabolic profiles can be utilised to not only gaining insight into cellular transformations such as the diauxic shift, but also on regulatory roles and biological consequences of regulatory gene deletion. We also conclude that untargeted metabolomics is a useful tool for guidance in high-throughput model improvement, and is a fast, sensitive and informative approach appropriate for future large-scale functional analyses of genes. Moreover, it is well-suited for automated approaches due to relative simplicity of processing and the potential to make massively high-throughput

Genesis-DB: a database for autonomous laboratory systems

Acknowledgements: The authors gratefully acknowledge the members of the Ross King Group at Chalmers University for their thoughtful insights and discussions. They would also like to thank the members of the ThoughtWorks Engineering for Research Organization for their efforts and support.Funder: Chalmers AI Research CentreSummary: Artificial intelligence (AI)-driven laboratory automation—combining robotic labware and autonomous software agents—is a powerful trend in modern biology. We developed Genesis-DB, a database system designed to support AI-driven autonomous laboratories by providing software agents access to large quantities of structured domain information. In addition, we present a new ontology for modeling data and metadata from autonomously performed yeast microchemostat cultivations in the framework of the Genesis robot scientist system. We show an example of how Genesis-DB enables the research life cycle by modeling yeast gene regulation, guiding future hypotheses generation and design of experiments. Genesis-DB supports AI-driven discovery through automated reasoning and its design is portable, generic, and easily extensible to other AI-driven molecular biology laboratory data and beyond. Availability and implementation: Genesis-DB code and installation instructions are available at the GitHub repository https://github.com/TW-Genesis/genesis-database-system.git. The database use case demo code and data are also available through GitHub (https://github.com/TW-Genesis/genesis-database-demo.git). The ontology can be downloaded here: https://github.com/TW-Genesis/genesis-ontology/releases/download/v0.0.23/genesis.owl. The ontology term descriptions (including mappings to existing ontologies) and maintenance standard operating procedures can be found at: https://github.com/TW-Genesis/genesis-ontology

Genesis-DB: a database for autonomous laboratory systems

Acknowledgements: The authors gratefully acknowledge the members of the Ross King Group at Chalmers University for their thoughtful insights and discussions. They would also like to thank the members of the ThoughtWorks Engineering for Research Organization for their efforts and support.Funder: Chalmers AI Research CentreSummary: Artificial intelligence (AI)-driven laboratory automation—combining robotic labware and autonomous software agents—is a powerful trend in modern biology. We developed Genesis-DB, a database system designed to support AI-driven autonomous laboratories by providing software agents access to large quantities of structured domain information. In addition, we present a new ontology for modeling data and metadata from autonomously performed yeast microchemostat cultivations in the framework of the Genesis robot scientist system. We show an example of how Genesis-DB enables the research life cycle by modeling yeast gene regulation, guiding future hypotheses generation and design of experiments. Genesis-DB supports AI-driven discovery through automated reasoning and its design is portable, generic, and easily extensible to other AI-driven molecular biology laboratory data and beyond. Availability and implementation: Genesis-DB code and installation instructions are available at the GitHub repository https://github.com/TW-Genesis/genesis-database-system.git. The database use case demo code and data are also available through GitHub (https://github.com/TW-Genesis/genesis-database-demo.git). The ontology can be downloaded here: https://github.com/TW-Genesis/genesis-ontology/releases/download/v0.0.23/genesis.owl. The ontology term descriptions (including mappings to existing ontologies) and maintenance standard operating procedures can be found at: https://github.com/TW-Genesis/genesis-ontology