From community approaches to single-cell genomics: the discovery of ubiquitous hyperhalophilic Bacteroidetes generalists

The microbiota of multi-pond solar salterns around the world has been analyzed using a variety of culture-dependent and molecular techniques. However, studies addressing the dynamic nature of these systems are very scarce. Here we have characterized the temporal variation during 1 year of the microbiota of five ponds with increasing salinity (from 18% to >40%), by means of CARD-FISH and DGGE. Microbial community structure was statistically correlated with several environmental parameters, including ionic composition and meteorological factors, indicating that the microbial community was dynamic as specific phylotypes appeared only at certain times of the year. In addition to total salinity, microbial composition was strongly influenced by temperature and specific ionic composition. Remarkably, DGGE analyses unveiled the presence of most phylotypes previously detected in hypersaline systems using metagenomics and other molecular techniques, such as the very abundant Haloquadratum and Salinibacter representatives or the recently described low GC Actinobacteria and Nanohaloarchaeota. In addition, an uncultured group of Bacteroidetes was present along the whole range of salinity. Database searches indicated a previously unrecognized widespread distribution of this phylotype. Single-cell genome analysis of five members of this group suggested a set of metabolic characteristics that could provide competitive advantages in hypersaline environments, such as polymer degradation capabilities, the presence of retinal-binding light-activated proton pumps and arsenate reduction potential. In addition, the fairly high metagenomic fragment recruitment obtained for these single cells in both the intermediate and hypersaline ponds further confirm the DGGE data and point to the generalist lifestyle of this new Bacteroidetes group.

An authoring tool for decision support systems in context questions of ecological knowledge

Decision support systems (DSS) support business or organizational decision-making activities, which require the access to information that is internally stored in databases or data warehouses, and externally in the Web accessed by Information Retrieval (IR) or Question Answering (QA) systems. Graphical interfaces to query these sources of information ease to constrain dynamically query formulation based on user selections, but they present a lack of flexibility in query formulation, since the expressivity power is reduced to the user interface design. Natural language interfaces (NLI) are expected as the optimal solution. However, especially for non-expert users, a real natural communication is the most difficult to realize effectively. In this paper, we propose an NLI that improves the interaction between the user and the DSS by means of referencing previous questions or their answers (i.e. anaphora such as the pronoun reference in “What traits are affected by them?”), or by eliding parts of the question (i.e. ellipsis such as “And to glume colour?” after the question “Tell me the QTLs related to awn colour in wheat”). Moreover, in order to overcome one of the main problems of NLIs about the difficulty to adapt an NLI to a new domain, our proposal is based on ontologies that are obtained semi-automatically from a framework that allows the integration of internal and external, structured and unstructured information. Therefore, our proposal can interface with databases, data warehouses, QA and IR systems. Because of the high NL ambiguity of the resolution process, our proposal is presented as an authoring tool that helps the user to query efficiently in natural language. Finally, our proposal is tested on a DSS case scenario about Biotechnology and Agriculture, whose knowledge base is the CEREALAB database as internal structured data, and the Web (e.g. PubMed) as external unstructured information.

Empowering global software development with business intelligence

Context: Global Software Development (GSD) allows companies to take advantage of talent spread across the world. Most research has been focused on the development aspect. However, little if any attention has been paid to the management of GSD projects. Studies report a lack of adequate support for management’s decisions made during software development, further accentuated in GSD since information is scattered throughout multiple factories, stored in different formats and standards. Objective: This paper aims to improve GSD management by proposing a systematic method for adapting Business Intelligence techniques to software development environments. This would enhance the visibility of the development process and enable software managers to make informed decisions regarding how to proceed with GSD projects. Method: A combination of formal goal-modeling frameworks and data modeling techniques is used to elicitate the most relevant aspects to be measured by managers in GSD. The process is described in detail and applied to a real case study throughout the paper. A discussion regarding the generalisability of the method is presented afterwards. Results: The application of the approach generates an adapted BI framework tailored to software development according to the requirements posed by GSD managers. The resulting framework is capable of presenting previously inaccessible data through common and specific views and enabling data navigation according to the organization of software factories and projects in GSD. Conclusions: We can conclude that the proposed systematic approach allows us to successfully adapt Business Intelligence techniques to enhance GSD management beyond the information provided by traditional tools. The resulting framework is able to integrate and present the information in a single place, thereby enabling easy comparisons across multiple projects and factories and providing support for informed decisions in GSD management.