Carbon and nitrogen limitation increase chitosan antifungal activity in Neurospora crassa and fungal human pathogens

Chitosan permeabilizes plasma membrane and kills sensitive filamentous fungi and yeast. Membrane fluidity and cell energy determine chitosan sensitivity in fungi. A five-fold reduction of both glucose (main carbon (C) source) and nitrogen (N) increased 2-fold Neurospora crassa sensitivity to chitosan. We linked this increase with production of intracellular reactive oxygen species (ROS) and plasma membrane permeabilization. Releasing N. crassa from nutrient limitation reduced chitosan antifungal activity in spite of high ROS intracellular levels. With lactate instead of glucose, C and N limitation increased N. crassa sensitivity to chitosan further (4-fold) than what glucose did. Nutrient limitation also increased sensitivity of filamentous fungi and yeast human pathogens to chitosan. For Fusarium proliferatum, lowering 100-fold C and N content in the growth medium, increased 16-fold chitosan sensitivity. Similar results were found for Candida spp. (including fluconazole resistant strains) and Cryptococcus spp. Severe C and N limitation increased chitosan antifungal activity for all pathogens tested. Chitosan at 100 μg ml-1 was lethal for most fungal human pathogens tested but non-toxic to HEK293 and COS7 mammalian cell lines. Besides, chitosan increased 90% survival of Galleria mellonella larvae infected with C. albicans. These results are of paramount for developing chitosan as antifungal.

Delineating zones to increase geographical detail in individual response data files: An application to the Spanish 2011 Census of population

Due to confidentiality considerations, the microdata available from the 2011 Spanish Census have been codified at a provincial (NUTS 3) level except when the municipal (LAU 2) population exceeds 20,000 inhabitants (a requirement that is met by less than 5% of all municipalities). For the remainder of the municipalities within a given province, information is only provided for their classification in wide population intervals. These limitations, hampering territorially-focused socio-economic analyses, and more specifically, those related to the labour market, are observed in many other countries. This article proposes and demonstrates an automatic procedure aimed at delineating a set of areas that meet such population requirements and that may be used to re-codify the geographic reference in these cases, thereby increasing the territorial detail at which individual information is available. The method aggregates municipalities into clusters based on the optimisation of a relevant objective function subject to a number of statistical constraints, and is implemented using evolutionary computation techniques. Clusters are defined to fit outer boundaries at the level of labour market areas.