Influence of fibre reinforcement on the post-cracking behaviour of a cement-stabilised sandy-clay subjected to indirect tensile stress

An experimental campaign was carried out to determine the influence of polypropylene fibre content and length on the post-cracking response of a sandy-clay stabilised with different cement contents. Three main sets of specimens were prepared: cement-stabilised specimens with two cement contents (5% and 10%); fibre-reinforced specimens with three fibre contents (0.1%, 0.2% and 0.3%) and cement-fibre-reinforced specimens combining the mentioned fibre and cement contents. Tensile tests on the fibres and indirect tensile tests and triaxial compression tests on the prepared specimens were conducted. Results show that the post-cracking behaviour is strongly affected by the combination of fibre and cement content as well as fibre length. Pull-out was the governing failure mode. Post-peak tension loss rate increased with fibre content, as a result of the loss of influence of the fibres on the post-peak behaviour. On the contrary, an increase in fibre content resulted in higher pre-peak strength gain rates and higher peak stresses.info:eu-repo/semantics/publishedVersio

Portuguese recommendations for the use of biological and targeted synthetic diseasemodifying antirheumatic drugs in patients with rheumatoid arthritis – 2020 update

Objective: To update the recommendations for the treatment of rheumatoid arthritis (RA) with biological and targeted synthetic disease-modifying antirheumatic drugs (bDMARDs and tsDMARDs), endorsed by the Portuguese Society of Rheumatology (SPR). Methods: These treatment recommendations were formulated by Portuguese rheumatologists taking into account previous recommendations, new literature evidence and consensus opinion. At a national meeting, in a virtual format, three of the ten previous recommendations were re-addressed and discussed after a more focused literature review. A first draft of the updated recommendations was elaborated by a team of SPR rheumatologists from the SPR rheumatoid arthritis study group, GEAR. The resulting document circulated among all SPR rheumatologists for discussion and input. The level of agreement with each of all the recommendations was anonymously voted online by all SPR rheumatologists. Results: These recommendations cover general aspects such as shared decision, treatment objectives, systematic assessment of disease activity and burden and its registry in Reuma.pt. Consensus was also achieved regarding specific aspects such as initiation of bDMARDs and tsDMARDs, assessment of treatment response, switching and definition of persistent remission. Conclusion: These recommendations may be used for guidance of treatment with bDMARDs and tsDMARDs in patients with RA. As more evidence becomes available and more therapies are licensed, these recommendations will be updated.info:eu-repo/semantics/publishedVersio

Data-driven reverse engineering of signaling pathways using ensembles of dynamic models

Signaling pathways play a key role in complex diseases such as cancer, for which the development of novel therapies is a difficult, expensive and laborious task. Computational models that can predict the effect of a new combination of drugs without having to test it experimentally can help in accelerating this process. In particular, network-based dynamic models of these pathways hold promise to both understand and predict the effect of therapeutics. However, their use is currently hampered by limitations in our knowledge of the underlying biochemistry, as well as in the experimental and computational technologies used for calibrating the models. Thus, the results from such models need to be carefully interpreted and used in order to avoid biased predictions. Here we present a procedure that deals with this uncertainty by using experimental data to build an ensemble of dynamic models. The method incorporates steps to reduce overfitting and maximize predictive capability. We find that by combining the outputs of individual models in an ensemble it is possible to obtain a more robust prediction. We report results obtained with this method, which we call SELDOM (enSEmbLe of Dynamic lOgic-based Models), showing that it improves the predictions previously reported for several challenging problems.JRB and DH acknowledge funding from the EU FP7 project NICHE (ITN Grant number 289384). JRB acknowledges funding from the Spanish MINECO project SYNBIOFACTORY (grant number DPI2014-55276-C5-2-R). AFV acknowledges funding from the Galician government (Xunta de Galiza) through the I2C postdoctoral fellowship ED481B2014/133-0. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.info:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio