Spatio-structural granularity of biological material entities

<p>Abstract</p> <p>Background</p> <p>With the continuously increasing demands on knowledge- and data-management that databases have to meet, ontologies and the theories of granularity they use become more and more important. Unfortunately, currently used theories and schemes of granularity unnecessarily limit the performance of ontologies due to two shortcomings: (i) they do not allow the integration of multiple granularity perspectives into one granularity framework; (ii) they are not applicable to cumulative-constitutively organized material entities, which cover most of the biomedical material entities.</p> <p>Results</p> <p>The above mentioned shortcomings are responsible for the major inconsistencies in currently used spatio-structural granularity schemes. By using the Basic Formal Ontology (BFO) as a top-level ontology and Keet's general theory of granularity, a granularity framework is presented that is applicable to cumulative-constitutively organized material entities. It provides a scheme for granulating complex material entities into their constitutive and regional parts by integrating various compositional and spatial granularity perspectives. Within a scale dependent resolution perspective, it even allows distinguishing different types of representations of the same material entity. Within other scale dependent perspectives, which are based on specific types of measurements (e.g. weight, volume, etc.), the possibility of organizing instances of material entities independent of their parthood relations and only according to increasing measures is provided as well. All granularity perspectives are connected to one another through overcrossing granularity levels, together forming an integrated whole that uses the <it>compositional object perspective </it>as an integrating backbone. This granularity framework allows to consistently assign structural granularity values to all different types of material entities.</p> <p>Conclusions</p> <p>The here presented framework provides a spatio-structural granularity framework for all domain reference ontologies that model cumulative-constitutively organized material entities. With its multi-perspectives approach it allows querying an ontology stored in a database at one's own desired different levels of detail: The contents of a database can be organized according to diverse granularity perspectives, which in their turn provide different <it>views </it>on its content (i.e. data, knowledge), each organized into different levels of detail.</p

Team climate, intention to leave and turnover among hospital employees: Prospective cohort study

<p>Abstract</p> <p>Background</p> <p>In hospitals, the costs of employee turnover are substantial and intentions to leave among staff may manifest as lowered performance. We examined whether team climate, as indicated by clear and shared goals, participation, task orientation and support for innovation, predicts intention to leave the job and actual turnover among hospital employees.</p> <p>Methods</p> <p>Prospective study with baseline and follow-up surveys (2–4 years apart). The participants were 6,441 (785 men, 5,656 women) hospital employees under the age of 55 at the time of follow-up survey. Logistic regression with generalized estimating equations was used as an analysis method to include both individual and work unit level predictors in the models.</p> <p>Results</p> <p>Among stayers with no intention to leave at baseline, lower self-reported team climate predicted higher likelihood of having intentions to leave at follow-up (odds ratio per 1 standard deviation decrease in team climate was 1.6, 95% confidence interval 1.4–1.8). Lower co-worker assessed team climate at follow-up was also association with such intentions (odds ratio 1.8, 95% confidence interval 1.4–2.4). Among all participants, the likelihood of actually quitting the job was higher for those with poor self-reported team climate at baseline. This association disappeared after adjustment for intention to leave at baseline suggesting that such intentions may explain the greater turnover rate among employees with low team climate.</p> <p>Conclusion</p> <p>Improving team climate may reduce intentions to leave and turnover among hospital employees.</p

Corrigendum: Whole genome analysis of a schistosomiasis-transmitting freshwater snail

This corrects the article DOI: 10.1038/ncomms15451

Adaptabilidade e estabilidade fenotípica de cultivares de alface do grupo crespa em cultivo hidropônico Adaptability and phenotypic stability of crisp lettuce cultivars in hydroponics

Considerando as diferentes estações climáticas em que a alface é cultivada durante o ano, é de se esperar a ocorrência de uma elevada interação genótipo x ambiente. Em função disto, neste trabalho estudou-se o desempenho produtivo, adaptabilidade e estabilidade fenotípica de seis cultivares de alface do grupo crespa (Deisy, Elba, Sabrina, Summer Green, Vera e Verônica), em sistema hidropônico NFT, instalado em casa-de-vegetação, na Universidade de Marília (SP). Foram conduzidos nove ensaios em três épocas (outono: Out n; inverno: Inv n e; primavera: Pri n), em delineamento inteiramente casualizado, com quatro repetições, avaliando-se quatro plantas por parcela. Ocorreram diferenças significativas entre ambientes (épocas de semeadura). Verificou-se que, de maneira geral, os três plantios realizados no outono apresentaram valores médios de produtividade (Out1 = 3,1; Out2 = 5,3 e; Out3 = 4,6 kg m²) superiores aos realizados no inverno (Inv1 = 3,6; Inv2 = 3,5 e; Inv = 3,7 kg m²) e primavera (Pri1 = 4,0; Pri2 = 3,6 e; Pri3 = 3,2 kg m²). Embora a interação cultivares x ambientes também tenha sido significativa, para produtividade, à exceção dos ambientes Inv3 e Pri1, não ocorreram diferenças significativas entre as cultivares dentro de cada época de plantio e, para número de folhas por planta, ocorreram diferenças significativas entre as cultivares somente no ambiente Out3. As cultivares Deyse e Verônica foram as únicas a mostrarem adaptabilidade a todos ambientes estudados para produtividade; todas as cultivares apresentaram instabilidade para essa mesma característica. Para o número de folhas por planta, somente a cultivar Summer Green apresentou comportamento altamente previsível (estável), no entanto, mostrou-se adaptada somente aos ambientes desfavoráveis.<br>Considering the climatic seasons in which lettuce is cultivated, probably a high interaction genotype x environment could be obtained. The yield, adaptability and phenotypic stability of six lettuce cultivars of the crisp group (Deyse, Elba, Sabrina, Summer Green, Vera and Verônica) were evaluated, on hydroponic 'NFT'. Nine experiments were carried out in three seasons (autumn= Out n, winter= Inv n and spring= Pri n), in a completely randomized design with four replications, evaluating four plants in each teatment. There occurred significant differences among environments (sowing date). Planting in autumn presented higher yield (Out1 = 3,1; Out2 = 5,3 and; Out3 = 4,6 kg m²) than winter (Inv1 = 3,6; Inv2 = 3,5 and; Inv = 3,7 kg m²) or spring (Pri1 = 4,0; Pri2 = 3,6 and; Pri3 = 3,2 kg m²). Although the significant differences in the interaction cultivars x environment for yield, except for Inv3 and Pri1, there were no significant differences among cultivars in each planting season. Significant differences were found among cultivars only in Out3, for the number of leaves in each plant. Cultivars Deyse and Verônica were the only ones well adapted in both environments for yield, the other cultivars being unstable. Only Summer Green presented behavior highly previsible (stable) for number of leaves per plant, nevertheless, showed adaptability only in unfavorable environments