Lupinus mariae-josphi, a new lupin endemic of soils with active lime and high pH in South Eastern Spain, is nodulated by a new bacterial lineage within Bradyrhizobium genus

Lupinus mariae-josephi is a recently described species (Pascual, 2004) able to grow in soils with high pH and active lime content in the Valencia province (Spain). L. mariae-josephi endosymbionts are extremely slowgrowing bacteria with genetic and symbiotic characteristics that differentiate them from Bradyrhizobium strains nodulating Lupinus spp. native of the Iberian Peninsula and adapted to grow in acid soils. Cross-inoculation experiments revealed that all the endosymbiotic isolates from L. mariae-josephi tested are legume-host selective and are unable to nodulate species such as L. angustifolius, and L. luteus. In contrast, Bradyrhizobium strains from Lupinus spp. tested were able to nodulate L. mariae-josephi, although the nodules fixed nitrogen inefficiently. Phylogenetic analysis was performed with housekeeping genes (rrn, glnII, recA, atpD) and nodulation gene nodC. Housekeeping gene phylogeny revealed that L. mariae-josephi rhizobia form a strongly supported monophyletic group within Bradyrhizobium genus. This cluster also includes B. jicamae and certain strains of B. elkanii. Contrarily, isolates from other Lupinus spp. native of the Iberian Peninsula were grouped mainly within B. canariense and two B. japonicum lineages. Phylogenetic analysis of L. mariae-josephi isolates based on the nodC symbiotic gene defined a solid clade close to isolates from Algerian Retama spp. and to fast-growing rhizobia.

Qualitative operational risk software -development of a software tool for determining the root of operational risk

This article shows software that allows determining the statistical behavior of qualitative data originating surveys previously transformed with a Likert’s scale to quantitative data. The main intention is offer to users a useful tool to know statistics' characteristics and forecasts of financial risks in a fast and simple way. Additionally,this paper presents the definition of operational risk. On the other hand, the article explains different techniques to do surveys with a Likert’s scale (Avila, 2008) to know expert’s opinion with the transformation of qualitative data to quantitative data. In addition, this paper will show how is very easy to distinguish an expert’s opinion related to risk, but when users have a lot of surveys and matrices is very difficult to obtain results because is necessary to compare common data. On the other hand, statistical value representative must be extracted from common data to get weight of each risk. In the end, this article exposes the development of “Qualitative Operational Risk Software” or QORS by its acronym, which has been designed to determine the root of risks in organizations and its value at operational risk OpVaR (Jorion, 2008; Chernobai et al, 2008) when input data comes from expert’s opinion and their associated matrices.