Several independent gene clusters containing varying lengths of type I polyketide synthase genes were isolated from ' Streptomyces nanchangensis ' NS3226, a producer of nanchangmycin and meilingmycin. The former is a polyether compound similar to dianemycin and the latter is a macrolide compound similar to milbemycin, which shares the same macrolide ring as avermectin but has different side groups. Clusters A-H spanned about 133, 132, 104, 174, 122, 54, 37 and 59 kb, respectively. Two systems were developed for functional analysis of the gene clusters by gene disruption or replacement. (1) Streptomyces phage φC31 and its derived vectors can infect and lysogenize this strain. (2) pSET152, an Escherichia coli plasmid with φC31 attP site, and pHZ1358, a Streptomyces-Escherichia coli shuttle cosmid vector, both carrying oriT from RP4, can be mobilized from E. coli into NS3226 by conjugation. pHZ1358 was shown to be generally useful for generating mutant strains by gene disruption and replacement in NS3226 as well as in several other Streptomyces strains. A region in cluster A (" 133 kb) seemed to be involved in nanchangmycin production because replacement of several DNA fragments in this region by an apramycin resistance gene [aac3(IV)] gave rise to nanchangmycin non-producing mutants.

BackgroundAlthough commonly observed, malnutrition is poorly characterized and frequently underdiagnosed in patients with metastatic renal cell carcinoma (RCC). The ability of nutritional screening tools to predict overall survival (OS) in patients with RCC has not been adequately validated. The objective of this study was to investigate the performance of nutritional screening tools and their additional prognostic value in patients with metastatic RCC treated with targeted therapies.MethodsPatients were prospectively recruited from three tertiary hospitals between 2009 and 2013. Nutritional status was evaluated using the Geriatric Nutritional Risk Index (GNRI) and the Mini Nutritional Assessment–Short Form (MNA–SF). Their OS and early grade 3/4 adverse events were recorded as outcomes of interest, and their associations with nutritional status were assessed using Cox regression and logistic regression, respectively. The incremental value in prognostication was evaluated using concordance index and decision curve analyses.ResultsOf the 300 enrolled patients, 95 (31.7%) and 64 (21.3%) were classified as being at risk of malnutrition according to the GNRI and MNA–SF, respectively. Both GNRI and MNA–SF were independent predictors of OS in multivariate analyses and provided significant added benefit to Heng risk classification. Compared with the MNA–SF, the GNRI contributed a higher increment to the concordance index (0.041 vs. 0.016). Nutritional screening, however, was not associated with early grade 3/4 adverse events in multivariate analyses. Further investigations are needed using more comprehensive and accurate assessment tools.ConclusionsThis prospective study confirmed the importance of nutritional screening tools in survival prognostication in patients with metastatic RCC. The standardized and objective measurements would allow clinicians to identify metastatic RCC patients at risk of poor survival outcomes. Individualized nutritional assessment and intervention strategies may be included in the multidisciplinary treatment.

The vast accumulation of environmental data and the rapid development of geospatial visualization and analytical techniques make it possible for scientists to solicit information from local citizens to map spatial variation of geographic phenomena. However, data provided by citizens (referred to as citizen data in this article) suffer two limitations for mapping: bias in spatial coverage and imprecision in spatial location. This article presents an approach to minimizing the impacts of these two limitations of citizen data using geospatial analysis techniques. The approach reduces location imprecision by adopting a frequency-sampling strategy to identify representative presence locations from areas over which citizens observed the geographic phenomenon. The approach compensates for the spatial bias by weighting presence locations with cumulative visibility (the frequency at which a given location can be seen by local citizens). As a case study to demonstrate the principle, this approach was applied to map the habitat suitability of the black-and-white snub-nosed monkey (Rhinopithecus bieti) in Yunnan, China. Sightings of R. bieti were elicited from local citizens using a geovisualization platform and then processed with the proposed approach to predict a habitat suitability map. Presence locations of R. bieti recorded by biologists through intensive field tracking were used to validate the predicted habitat suitability map. Validation showed that the continuous Boyce index (B cont (0.1)) calculated on the suitability map was 0.873 (95% CI: [0.810, 0.917]), indicating that the map was highly consistent with the fieldobserved distribution of R. bieti. B cont (0.1) was much lower (0.173) for the suitability map predicted based on citizen data when location imprecision was not reduced and even lower (−0.048) when there was no compensation for spatial bias. This indicates that the proposed approach effectively minimized the impacts of location imprecision and spatial bias in citizen data and therefore effectively improved the quality of mapped spatial variation using citizen data. It further implies that, with the application of geospatial analysis techniques to properly account for limitations in citizen data, valuable information embedded in such data can be extracted and used for scientific mapping.