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    ABSTRACT: The review presents the state-of-the-art in the applications of in-situ product recovery (ISPR) in whole-cell biotechnology over the last 10 years. It summarizes various ISPR-integrated fermentation processes for the production of a wide spectrum of bio-based products. A critical assessment of the performance of various ISPR concepts with respect to the degree of product enrichment, improved productivity, reduced process flows and increased yields is provided. Requirements to allow a successful industrial implementation of ISPR are also discussed. Finally, supporting technologies such as online monitoring, mathematical modelling and use of recombinant microorganisms with ISPR are presented.
    No preview · Article · Jul 2014 · Biotechnology Advances
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    ABSTRACT: Petroleum substances are used in large quantities, primarily as fuels. They are complex mixtures whose major constituents are hydrocarbons derived from crude oil by distillation and fractionation. Determining the complete molecular composition of petroleum and its refined products is not feasible with current analytical techniques because of the huge number of molecular components. This complex nature of petroleum products, with their varied number of constituents, all of them exhibiting different fate and effect characteristics, merits a dedicated hazard and risk assessment approach. From a regulatory perspective they pose a great challenge in a number of REACH processes, in particular in the context of dossier and substance evaluation but also for priority setting activities. In order to facilitate the performance of hazard and risk assessment for petroleum substances the European oil company association, CONCAWE, has developed the PETROTOX and PETRORISK spreadsheet models.
    Full-text · Article · May 2014 · Environment International
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    ABSTRACT: Machine learning is a subdiscipline within artificial intelligence that focuses on algorithms that allow computers to learn solving a (complex) problem from existing data. This ability can be used to generate a solution to a particularly intractable problem, given that enough data are available to train and subsequently evaluate an algorithm on. Since MS-based proteomics has no shortage of complex problems, and since publicly available data are becoming available in ever growing amounts, machine learning is fast becoming a very popular tool in the field. We here therefore present an overview of the different applications of machine learning in proteomics that together cover nearly the entire wet- and dry-lab workflow, and that address key bottlenecks in experiment planning and design, as well as in data processing and analysis.
    Full-text · Article · Mar 2014 · Proteomics


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Trends in Ecology & Evolution 01/2016; 31(2):105-115. DOI:10.1016/j.tree.2015.12.003
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