Rhamnolipids: diversity of structures, microbial origins and roles

Rhamnolipids are glycolipidic biosurfactants produced by various bacterial species. They were initially found as exoproducts of the opportunistic pathogen Pseudomonas aeruginosa and described as a mixture of four congeners: α-L-rhamnopyranosyl-α-L-rhamnopyranosyl-β-hydroxydecanoyl-β-hydroxydecanoate (Rha-Rha-C10-C10), α-L-rhamnopyranosyl-α-L-rhamnopyranosyl-β-hydroxydecanoate (Rha-Rha-C10), as well as their mono-rhamnolipid congeners Rha-C10-C10 and Rha-C10. The development of more sensitive analytical techniques has lead to the further discovery of a wide diversity of rhamnolipid congeners and homologues (about 60) that are produced at different concentrations by various Pseudomonas species and by bacteria belonging to other families, classes, or even phyla. For example, various Burkholderia species have been shown to produce rhamnolipids that have longer alkyl chains than those produced by P. aeruginosa. In P. aeruginosa, three genes, carried on two distinct operons, code for the enzymes responsible for the final steps of rhamnolipid synthesis: one operon carries the rhlAB genes and the other rhlC. Genes highly similar to rhlA, rhlB, and rhlC have also been found in various Burkholderia species but grouped within one putative operon, and they have been shown to be required for rhamnolipid production as well. The exact physiological function of these secondary metabolites is still unclear. Most identified activities are derived from the surface activity, wetting ability, detergency, and other amphipathic-related properties of these molecules. Indeed, rhamnolipids promote the uptake and biodegradation of poorly soluble substrates, act as immune modulators and virulence factors, have antimicrobial activities, and are involved in surface motility and in bacterial biofilm development

Preparing for low surface brightness science with the Vera C. Rubin Observatory: characterisation of tidal features from mock images

Tidal features in the outskirts of galaxies yield unique information about their past interactions and are a key prediction of the hierarchical structure formation paradigm. The Vera C. Rubin Observatory is poised to deliver deep observations for potentially of millions of objects with visible tidal features, but the inference of galaxy interaction histories from such features is not straightforward. Utilising automated techniques and human visual classification in conjunction with realistic mock images produced using the NEWHORIZON cosmological simulation, we investigate the nature, frequency and visibility of tidal features and debris across a range of environments and stellar masses. In our simulated sample, around 80 per cent of the flux in the tidal features around Milky Way or greater mass galaxies is detected at the 10-year depth of the Legacy Survey of Space and Time (30-31 mag / sq. arcsec), falling to 60 per cent assuming a shallower final depth of 29.5 mag / sq. arcsec. The fraction of total flux found in tidal features increases towards higher masses, rising to 10 per cent for the most massive objects in our sample (M*~10^{11.5} Msun). When observed at sufficient depth, such objects frequently exhibit many distinct tidal features with complex shapes. The interpretation and characterisation of such features varies significantly with image depth and object orientation, introducing significant biases in their classification. Assuming the data reduction pipeline is properly optimised, we expect the Rubin Observatory to be capable of recovering much of the flux found in the outskirts of Milky Way mass galaxies, even at intermediate redshifts (z<0.2)

The Seventeenth Data Release of the Sloan Digital Sky Surveys: Complete Release of MaNGA, MaStar, and APOGEE-2 Data

This paper documents the seventeenth data release (DR17) from the Sloan Digital Sky Surveys; the fifth and final release from the fourth phase (SDSS-IV). DR17 contains the complete release of the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, which reached its goal of surveying over 10,000 nearby galaxies. The complete release of the MaNGA Stellar Library accompanies this data, providing observations of almost 30,000 stars through the MaNGA instrument during bright time. DR17 also contains the complete release of the Apache Point Observatory Galactic Evolution Experiment 2 survey that publicly releases infrared spectra of over 650,000 stars. The main sample from the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), as well as the subsurvey Time Domain Spectroscopic Survey data were fully released in DR16. New single-fiber optical spectroscopy released in DR17 is from the SPectroscipic IDentification of ERosita Survey subsurvey and the eBOSS-RM program. Along with the primary data sets, DR17 includes 25 new or updated value-added catalogs. This paper concludes the release of SDSS-IV survey data. SDSS continues into its fifth phase with observations already underway for the Milky Way Mapper, Local Volume Mapper, and Black Hole Mapper surveys

Computer-based tools for decision support in agroforestry: Current state and future needs

Successful design of agroforestry practices hinges on the ability to pull together very diverse and sometimes large sets of information (i.e., biophysical, economic and social factors), and then implementing the synthesis of this information across several spatial scales from site to landscape. Agroforestry, by its very nature, creates complex systems with impacts ranging from the site or practice level up to the landscape and beyond. Computer-based Decision Support Tools (DST) help to integrate information to facilitate the decision-making process that directs development, acceptance, adoption, and management aspects in agroforestry. Computer-based DSTs include databases, geographical information systems, models, knowledge-base or expert systems, and ‘hybrid’ decision support systems. These different DSTs and their applications in agroforestry research and development are described in this paper. Although agroforestry lacks the large research foundation of its agriculture and forestry counterparts, the development and use of computer-based tools in agroforestry have been substantial and are projected to increase as the recognition of the productive and protective (service) roles of these tree-based practices expands. The utility of these and future tools for decision-support in agroforestry must take into account the limits of our current scientific information, the diversity of aspects (i.e. economic, social, and biophysical) that must be incorporated into the planning and design process, and, most importantly, who the end-user of the tools will be. Incorporating these tools into the design and planning process will enhance the capability of agroforestry to simultaneously achieve environmental protection and agricultural production goals