Cultures of Expertise and the Public Interventions of Economists

The essays in this volume examine the economist as public intellectual. Rather than assessing the changing status of the public intellectual in culture or attempting to define the identity of the public intellectual, our approach is to study the public interventions of economists, that is, the encounters between economists and their publics. In the volume we constrain ourselves to the long twentieth century in the United States and the United Kingdom, fenced at one end by the Progressive Era and Fabianism and the ongoing economic crisis at the other. Economists then and now have been occupants of the public sphere, and to understand their encounters with the public we must appreciate the expectations they bring to the meeting and the institutional contexts that enable the encounters. The unifying claim of our collection is that economists’ public interventions have been of profound consequence for both the structure and the content of the public sphere.</jats:p

Draft Genome Sequence of Streptomyces phaeoluteigriseus DSM41896

The draft genome for the type strain Streptomyces phaeoluteigriseus DSM41896 (ISP 5182) is reported. It was classified as a member of the Streptomyces violaceusniger clade; however, a polyphasic study showed it was a separate species based on its distinct spore morphology and 16S rRNA sequence. The genome sequence confirms it as a separate species

MultiMetEval: comparative and multi-objective analysis of genome-scale metabolic models

Comparative metabolic modelling is emerging as a novel field, supported by the development of reliable and standardized approaches for constructing genome-scale metabolic models in high throughput. New software solutions are needed to allow efficient comparative analysis of multiple models in the context of multiple cellular objectives. Here, we present the user-friendly software framework Multi-Metabolic Evaluator (MultiMetEval), built upon SurreyFBA, which allows the user to compose collections of metabolic models that together can be subjected to flux balance analysis. Additionally, MultiMetEval implements functionalities for multi-objective analysis by calculating the Pareto front between two cellular objectives. Using a previously generated dataset of 38 actinobacterial genome-scale metabolic models, we show how these approaches can lead to exciting novel insights. Firstly, after incorporating several pathways for the biosynthesis of natural products into each of these models, comparative flux balance analysis predicted that species like Streptomyces that harbour the highest diversity of secondary metabolite biosynthetic gene clusters in their genomes do not necessarily have the metabolic network topology most suitable for compound overproduction. Secondly, multi-objective analysis of biomass production and natural product biosynthesis in these actinobacteria shows that the well-studied occurrence of discrete metabolic switches during the change of cellular objectives is inherent to their metabolic network architecture. Comparative and multi-objective modelling can lead to insights that could not be obtained by normal flux balance analyses. MultiMetEval provides a powerful platform that makes these analyses straightforward for biologists. Sources and binaries of MultiMetEval are freely available from https://github.com/PiotrZakrzewski/MetEv​al/downloads

Vanadium oxide monolayer catalysts. 3. A Raman spectroscopic and temperature-programmed reduction study of monolayer and crystal-type vanadia on various supports

Vanadium(V) oxide supported on 7-A1203, GO2, Cr2O3, Si02, Ti02, and Zr02 was studied by X-ray fluorescence, by X-ray diffraction, and especially by the combination of Raman spectroscopy and temperature-programmed reduction (TPR) for qualitative and quantitative structural analysis, respectively. Catalysts were prepared via ion-exchange and wet-impregnation methods. The V contents ranged from - 1 to 40 wt % V. At low surface concentrations only surface vanadate phases of two-dimensional character are observed for all carriers. According to Raman and TPR data the structure of these surface vanadate species is independent of the preparation technique. At medium and high surface concentrations, the webimpregnated samples already contain crystalline V20b At equal surface concentrations the ion-exchanged catalysts contain no V205 crystallites. An exception is Si02 on which also crystalline V206 is formed in both preparation techniques. Monolayer stability toward thermal treatment decreases in the order AZO&gt;3 Ti02&gt; Ce02,w hereas on heating ion-exchanged V/Si02 the crystalline V206 spreads out over the silica surface. The reducibilities of the ion-exchanged catalyats, as measured by TPR, can be used as a measure for the contact interaction between vanadia and the carrier oxides. At temperatures of 500-800 K, this interaction ranges from strong with titania to weak with silica as a carrier

plantiSMASH: automated identification, annotation and expression analysis of plant biosynthetic gene clusters

Plant specialized metabolites are chemically highly diverse, play key roles in host-microbe interactions, have important nutritional value in crops and are frequently applied as medicines. It has recently become clear that plant biosynthetic pathway-encoding genes are sometimes densely clustered in specific genomic loci: Biosynthetic gene clusters (BGCs). Here, we introduce plantiSMASH, a versatile online analysis platform that automates the identification of candidate plant BGCs. Moreover, it allows integration of transcriptomic data to prioritize candidate BGCs based on the coexpression patterns of predicted biosynthetic enzyme-coding genes, and facilitates comparative genomic analysis to study the evolutionary conservation of each cluster. Applied on 48 high-quality plant genomes, plantiSMASH identifies a rich diversity of candidate plant BGCs. These results will guide further experimental exploration of the nature and dynamics of gene clustering in plant metabolism. Moreover, spurred by the continuing decrease in costs of plant genome sequencing, they will allow genome mining technologies to be applied to plant natural product discovery.</p

Recent development of antiSMASH and other computational approaches to mine secondary metabolite biosynthetic gene clusters

Many drugs are derived from small molecules produced by microorganisms and plants, so-called natural products. Natural products have diverse chemical structures, but the biosynthetic pathways producing those compounds are often organized as biosynthetic gene clusters (BGCs) and follow a highly conserved biosynthetic logic. This allows for the identification of core biosynthetic enzymes using genome mining strategies that are based on the sequence similarity of the involved enzymes/genes. However, mining for a variety of BGCs quickly approaches a complexity level where manual analyses are no longer possible and require the use of automated genome mining pipelines, such as the antiSMASH software. In this review, we discuss the principles underlying the predictions of antiSMASH and other tools and provide practical advice for their application. Furthermore, we discuss important caveats such as rule-based BGC detection, sequence and annotation quality and cluster boundary prediction, which all have to be considered while planning for, performing and analyzing the results of genome mining studies

Nanochanneled Device and Related Methods

A nanochannel delivery device and method of manufacturing and use. The nanochannel delivery device comprises an inlet, an outlet, and a nanochannel. The nanochannel may be oriented parallel to the primary plane of the nanochannel delivery device. The inlet and outlet may be in direct fluid communication with the nanochannel

NRPSpredictor2-a web server for predicting NRPS adenylation domain specificity

The products of many bacterial non-ribosomal peptide synthetases (NRPS) are highly important secondary metabolites, including vancomycin and other antibiotics. The ability to predict substrate specificity of newly detected NRPS Adenylation (A-) domains by genome sequencing efforts is of great importance to identify and annotate new gene clusters that produce secondary metabolites. Prediction of A-domain specificity based on the sequence alone can be achieved through sequence signatures or, more accurately, through machine learning methods. We present an improved predictor, based on previous work (NRPSpredictor), that predicts A-domain specificity using Support Vector Machines on four hierarchical levels, ranging from gross physicochemical properties of an A-domain's substrates down to single amino acid substrates. The three more general levels are predicted with an F-measure better than 0.89 and the most detailed level with an average F-measure of 0.80. We also modeled the applicability domain of our predictor to estimate for new A-domains whether they lie in the applicability domain. Finally, since there are also NRPS that play an important role in natural products chemistry of fungi, such as peptaibols and cephalosporins, we added a predictor for fungal A-domains, which predicts gross physicochemical properties with an F-measure of 0.84. The service is available at http://nrps.informatik.uni-tuebingen.de/