http://docentes.fct.unl.pt/lblm/files/jbic-2015-20-821.pdf

Nucleation is a critical step determining the outcome of the entire crystallization process. Finding an effective nucleant for protein crystallization is of utmost importance for structural biology. The latter relies on good-quality crystals to solve the three-dimensional structures of macromolecules. In this study we show that crystalline barium sulfate (BaSO4) with an etched and/or ionic liquid (IL)-functionalized surface (1) can induce protein nucleation at concentrations well below the concentration needed to promote crystal growth under control conditions, (2) can shorten the nucleation time, (3) can increase the growth rate, and finally (4) may help to improve the protein crystal morphology. These effects were shown for lysozyme, RNase A, trypsin, proteinase K, myoglobin, and hemoglobin. Therefore, the use of BaSO4 particles enables us to reduce the amount of protein in crystallization trials and increases the chance of obtaining protein crystals of the desired quality. In the context of the underlying mechanism, it is shown that the protein-solid contact formation is governed by the interaction of the polar compartments of the biomacromolecule with the support. The tendency of a protein to concentrate near the solid surface is enhanced by both the hydrophobicity of the protein and that of the surface (tuned by the functionalizing IL). These mechanisms of interaction of biomacromolecules with inorganic hydrophilic solids correspond to the principles of amphiphilic IL-mineral interactions.

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Domain-Specific Languages (DSLs) developers aim to narrow the gap between the level of abstraction used by domain users and the one provided by the DSL, in order to help taming the increased complexity of computer systems and real-world problems. The quality in use of a DSL is essential for its successful adoption. We illustrate how a usability evaluation process can be weaved into the development process of a concrete DSL - FlowSL - used for specifying humanitarian campaign processes lead by an international Non-Governmental Organization. FlowSL is being developed following an agile process using Model-Driven Development (MDD) tools, to cope with vague and poorly understood requirements in the beginning of the development process.

An integration of undoped InOx and commercial ITO thin films into laboratory assembled light shutter devices is made. Accordingly, undoped transparent conductive InOx thin films, about 100 nm thick, are deposited by radiofrequency plasma enhanced reactive thermal evaporation (rf-PERTE) of indium teardrops with no intentional heating of the glass substrates. The process of deposition occurs at very low deposition rates (0.1–0.3 nm/s) to establish an optimized reaction between the oxygen plasma and the metal vapor. These films show the following main characteristics: transparency of 87% (wavelength, λ = 632.8 nm) and sheet resistance of 52 Ω/sq; while on commercial ITO films the transparency was of 92% and sheet resistance of 83 Ω/sq. The InOx thin film surface characterized by AFM shows a uniform grain texture with a root mean square surface roughness of Rq∼2.276 nm. In contrast, commercial ITO topography is characterized by two regions: one smoother with Rq∼0.973 nm and one with big grains (Rq∼3.617 nm). For the shutters assembled using commercial ITO, the light transmission coefficient (Tr) reaches the highest value (Trmax) of 89% and the lowest (Trmin) of 1.3% [13], while for the InOx shutters these values are 80.1% and 3.2%, respectively. Regarding the electric field required to achieve 90% of the maximum transmission in the ON state (Eon), the one presented by the devices assembled with commercial ITO coated glasses is 2.41 V/μm while the one presented by the devices assembled with InOx coated glasses is smaller, 1.77 V/μm. These results corroborate the device quality that depends on the base materials and fabrication process used.

{An integration of undoped InOx and commercial ITO thin films into laboratory assembled light shutter devices is made. Accordingly, undoped transparent conductive InOx thin films, about 100 nm thick, are deposited by radiofrequency plasma enhanced reactive thermal evaporation (rf-PERTE) of indium teardrops with no intentional heating of the glass substrates. The process of deposition occurs at very low deposition rates (0.1-0.3 nm/s) to establish an optimized reaction between the oxygen plasma and the metal vapor. These films show the following main characteristics: transparency of 87% (wavelength

Goal-oriented Requirements Engineering approaches have become popular in the Requirements Engineering community as they provide expressive model elements for requirements elicitation and analysis. However, as a common challenge, they are still struggling when it comes to managing the accidental complexity of their models. In this paper, we provide a set of metrics, which are formally specified and have tool support, to measure and analyze the complexity of goal models, in particular i* models. The aim is to identify refactoring opportunities to improve the modularity of those models, and consequently reduce their complexity. We evaluate these metrics by applying them to a set of well-known case studies from industry and academia. Our results allow the identification of refactoring opportunities in the evaluated models.

Helping users to find useful contacts or potentially interesting subjects is a challenge for social and productive
networks. The evidence of the content produced by users must be considered in this task, which may be simplified by the use of the meta-data associated with the content, i.e., the categorization supported by the network – descriptive keywords, or tags. In this paper we present a model that enables keyword discovery
methods through the interpretation of the network as a graph, solely relying on keywords that categorize or describe productive items. The model and keyword discovery methods presented in this paper avoid content analysis, and move towards a generic approach to the identification of relevant interests and, eventually,
contacts. The evaluation of the model and methods is executed by two experiments that perform frequency and classification analyses over the Flickr network. The results show that we can efficiently recommend keywords to users.

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