Identification and characterization of the sRNA network in Neisseria meningitidis

Bacterial small regulatory RNAs (sRNAs) are posttranscriptional regulators involved in stress responses. These short non-coding transcripts are synthesised in response to a signal, and control gene expression of their regulons by modulating the translation or stability of the target mRNAs, often in concert with the RNA chaperone Hfq. Characterization of a Hfq knock out mutant in Neisseria meningitidis revealed that it has a pleiotropic phenotype, suggesting a major role for Hfq in adaptation to stresses and virulence and the presence of Hfq-dependent sRNA activity. Global gene expression analysis of regulated transcripts in the Hfq mutant revealed the presence of a regulated sRNA, incorrectly annotated as an open reading frame, which we renamed AniS. The synthesis of this novel sRNA is anaerobically induced through activation of its promoter by the FNR global regulator and through global gene expression analyses we identified at least two predicted mRNA targets of AniS. We also performed a detailed molecular analysis of the action of the sRNA NrrF,. We demonstrated that NrrF regulates succinate dehydrogenase by forming a duplex with a region of complementarity within the sdhDA region of the succinate dehydrogenase transcript, and Hfq enhances the binding of this sRNA to the identified target in the sdhCDAB mRNA; this is likely to result in rapid turnover of the transcript in vivo. In addition, in order to globally investigate other possible sRNAs of N. meningitdis we Deep-sequenced the transcriptome of this bacterium under both standard in vitro and iron-depleted conditions. This analysis revealed genes that were actively transcribed under the two conditions. We focused our attention on the transcribed non-coding regions of the genome and, along with 5’ and 3’ untranslated regions, 19 novel candidate sRNAs were identified. Further studies will be focused on the identification of the regulatory networks of these sRNAs, and their targets.

Nondestructive evaluation of concrete compression strength by means of Artificial Neural Network (ANN)

The evaluation of structural performance of existing concrete buildings, built according to standards and materials quite different to those available today, requires procedures and methods able to cover lack of data about mechanical material properties and reinforcement detailing. To this end detailed inspections and test on materials are required. As a consequence tests on drilled cores are required; on the other end, it is stated that non-destructive testing (NDT) cannot be used as the only mean to get structural information, but can be used in conjunction with destructive testing (DT) by a representative correlation between DT and NDT. The aim of this study is to verify the accuracy of some formulas of correlation available in literature between measured parameters, i.e. rebound index, ultrasonic pulse velocity and compressive strength (SonReb Method). To this end a relevant number of DT and NDT tests has been performed on many school buildings located in Cesena (Italy). The above relationships have been assessed on site correlating NDT results to strength of core drilled in adjacent locations. Nevertheless, concrete compressive strength assessed by means of NDT methods and evaluated with correlation formulas has the advantage of being able to be implemented and used for future applications in a much more simple way than other methods, even if its accuracy is strictly limited to the analysis of concretes having the same characteristics as those used for their calibration. This limitation warranted a search for a different evaluation method for the non-destructive parameters obtained on site. To this aim, the methodology of neural identification of compressive strength is presented. Artificial Neural Network (ANN) suitable for the specific analysis were chosen taking into account the development presented in the literature in this field. The networks were trained and tested in order to detect a more reliable strength identification methodology.

An Input-Process-Output Approach to Interorganizational Teams: The Influence of Work Group Diversity, Trust and Shared Leadership on Communication Network and Team Outputs.

The market’s challenges bring firms to collaborate with other organizations in order to create Joint Ventures, Alliances and Consortia that are defined as “Interorganizational Networks” (IONs) (Provan, Fish and Sydow; 2007). Some of these IONs are managed through a shared partecipant governance (Provan and Kenis, 2008): a team composed by entrepreneurs and/or directors of each firm of an ION. The research is focused on these kind of management teams and it is based on an input-process-output model: some input variables (work group’s diversity, intra-team's friendship network density) have a direct influence on the process (team identification, shared leadership, interorganizational trust, team trust and intra-team's communication network density), which influence some team outputs, individual innovation behaviors and team effectiveness (team performance, work group satisfaction and ION affective commitment). Data was collected on a sample of 101 entrepreneurs grouped in 28 ION’s government teams and the research hypotheses are tested trough the path analysis and the multilevel models. As expected trust in team and shared leadership are positively and directly related to team effectiveness while team identification and interorganizational trust are indirectly related to the team outputs. The friendship network density among the team’s members has got positive effects on the trust in team and on the communication network density, and also, through the communication network density it improves the level of the teammates ION affective commitment. The shared leadership and its effects on the team effectiveness are fostered from higher level of team identification and weakened from higher level of work group diversity, specifically gender diversity. Finally, the communication network density and shared leadership at the individual level are related to the frequency of individual innovative behaviors. The dissertation’s results give a wider and more precise indication about the management of interfirm network through “shared” form of governance.