Phenanthroimidazole derivatives as fluorimetric chemosensors in aqueous medium

In recent years the research of sensors with good sensitivity and good selectivity in aqueous medium has been of great interest. Chemosensors soluble in aqueous media are very interesting, because of the importance in revealing a number of biological processes, disease states and environmental pollutions. 2,4,5-Triaryl-imidazoles are versatile compounds with application in medicine, due to their biological activity, and materials sciences, for their interesting optical properties. These properties can be tuned by careful selection of substituents at positions 2, 4 and 5: replacement of the aryl group by a heterocyclic group results in larger π-conjugated systems with improved optical properties for application in nonlinear optics, OLEDs, DNA intercalators, and chemosensors. In this communication, we report the synthesis of new phenanthroimidazoles, substituted at position 2 with (hetero)aryl groups of different electronic character, in order to evaluate their photophysical properties and chemosensory ability. The new derivatives were characterized by the usual techniques and a detailed photophysical study was undertaken. The evaluation of the compounds as fluorimetric chemosensors was carried out by performing titrations in acetonitrile and acetonitrile/water in the presence of relevant organic and inorganic anions, and of alkaline, alkaline-earth and transition metal cations.

The Data Mining applied for the prediction of highway roughness due to overloaded trucks

Currently, the quality of the Indonesian national road network is inadequate due to several constraints, including overcapacity and overloaded trucks. The high deterioration rate of the road infrastructure in developing countries along with major budgetary restrictions and high growth in traffic have led to an emerging need for improving the performance of the highway maintenance system. However, the high number of intervening factors and their complex effects require advanced tools to successfully solve this problem. The high learning capabilities of Data Mining (DM) are a powerful solution to this problem. In the past, these tools have been successfully applied to solve complex and multi-dimensional problems in various scientific fields. Therefore, it is expected that DM can be used to analyze the large amount of data regarding the pavement and traffic, identify the relationship between variables, and provide information regarding the prediction of the data. In this paper, we present a new approach to predict the International Roughness Index (IRI) of pavement based on DM techniques. DM was used to analyze the initial IRI data, including age, Equivalent Single Axle Load (ESAL), crack, potholes, rutting, and long cracks. This model was developed and verified using data from an Integrated Indonesia Road Management System (IIRMS) that was measured with the National Association of Australian State Road Authorities (NAASRA) roughness meter. The results of the proposed approach are compared with the IIRMS analytical model adapted to the IRI, and the advantages of the new approach are highlighted. We show that the novel data-driven model is able to learn (with high accuracy) the complex relationships between the IRI and the contributing factors of overloaded trucks

Imidazo-benzocrown ether functionalised amino acids: synthesis and fluorimetric chemosensing studies with metal cations

Comunicação em painel - P59

Micro- and mesoporous structures as drug delivery carriers for salicylic acid

The potential of salicylic acid (SA) encapsulated in porous materials as drug delivery carriers for cancer treatment was studied. Different porous structures, the microporous zeolite NaY, and the mesoporous SBA-15 and MCM-41 were used as hosts for the anti-inflammatory drug. Characterization with different techniques (FTIR, UV/vis, TGA, 1H NMR, and 13C CPMAS NMR) demonstrated the successful loading of SA into the porous hosts. The mesoporous structures showed to be very efficient to encapsulate the SA molecule. The obtained drug delivery systems (DDS) accommodated 0.74 mmol (341 mg/gZEO) in NaY and 1.07 mmol (493 mg/gZEO) to 1.23 mmol (566 mg/gZEO) for SBA-15 and MCM-41, respectively. Interactions between SA molecules and pore structures were identified. A fast and unrestricted liberation of SA at 10 min of the dissolution assay was achieved with 29.3, 46.6, and 50.1 µg/mL of SA from NaY, SBA-15, and MCM-41, respectively, in the in vitro drug release studies (PBS buffer pH 7.4, 37 °C). Kinetic modeling was used to determine the release patterns of the DDS. The porous structures and DDS were evaluated on Hs578T and MDA-MB-468 breast cancer cell lines viability. The porous structures are nontoxic to cancer cells. Cell viability reduction was only observed after the release of SA from MCM- 41 followed by SBA-15 in both breast cancer cell lines.

Abstract computation in schizophrenia detection through artificial neural network based systems

Schizophrenia stands for a long-lasting state of mental uncertainty that may bring to an end the relation among behavior, thought, and emotion; that is, it may lead to unreliable perception, not suitable actions and feelings, and a sense of mental fragmentation. Indeed, its diagnosis is done over a large period of time; continuos signs of the disturbance persist for at least 6 (six) months. Once detected, the psychiatrist diagnosis is made through the clinical interview and a series of psychic tests, addressed mainly to avoid the diagnosis of other mental states or diseases. Undeniably, the main problem with identifying schizophrenia is the difficulty to distinguish its symptoms from those associated to different untidiness or roles. Therefore, this work will focus on the development of a diagnostic support system, in terms of its knowledge representation and reasoning procedures, based on a blended of Logic Programming and Artificial Neural Networks approaches to computing, taking advantage of a novel approach to knowledge representation and reasoning, which aims to solve the problems associated in the handling (i.e., to stand for and reason) of defective information.

Thrombophilia screening: An artificial neural network approach

Thrombotic disorders have severe consequences for the patients and for the society in general, being one of the main causes of death. These facts reveal that it is extremely important to be preventive; being aware of how probable is to have that kind of syndrome. Indeed, this work will focus on the development of a decision support system that will cater for an individual risk evaluation with respect to the surge of thrombotic complaints. The Knowledge Representation and Reasoning procedures used will be based on an extension to the Logic Programming language, allowing the handling of incomplete and/or default data. The computational framework in place will be centered on Artificial Neural Networks.

Differential activation of the default mode network in jet lagged individuals

Long-term exposure to transmeridian flights has been shown to impact cognitive functioning. Nevertheless, the immediate effects of jet lag in the activation of specific brain networks have not been investigated. We analyzed the impact of short-term jet lag on the activation of the default mode network (DMN). A group of individuals who were on a transmeridian flight and a control group went through a functional magnetic resonance imaging acquisition. Statistical analysis was performed to test for differences in the DMN activation between groups. Participants from the jet lag group presented decreased activation in the anterior nodes of the DMN, specifically in bilateral medial prefrontal and anterior cingulate cortex. No areas of increased activation were observed for the jet lag group. These results may be suggestive of a negative impact of jet lag on important cognitive functions such as introspection, emotional regulation and decision making in a few days after individuals arrive at their destination.

Integration of geometric consistency contributory factors in three-leg junctions collision prediction models of Portuguese two-lane national highways

This paper aims at developing a collision prediction model for three-leg junctions located in national roads (NR) in Northern Portugal. The focus is to identify factors that contribute for collision type crashes in those locations, mainly factors related to road geometric consistency, since literature is scarce on those, and to research the impact of three modeling methods: generalized estimating equations, random-effects negative binomial models and random-parameters negative binomial models, on the factors of those models. The database used included data published between 2008 and 2010 of 177 three-leg junctions. It was split in three groups of contributing factors which were tested sequentially for each of the adopted models: at first only traffic, then, traffic and the geometric characteristics of the junctions within their area of influence; and, lastly, factors which show the difference between the geometric characteristics of the segments boarding the junctionsâ area of influence and the segment included in that area were added. The choice of the best modeling technique was supported by the result of a cross validation made to ascertain the best model for the three sets of researched contributing factors. The models fitted with random-parameters negative binomial models had the best performance in the process. In the best models obtained for every modeling technique, the characteristics of the road environment, including proxy measures for the geometric consistency, along with traffic volume, contribute significantly to the number of collisions. Both the variables concerning junctions and the various national highway segments in their area of influence, as well as variations from those characteristics concerning roadway segments which border the already mentioned area of influence have proven their relevance and, therefore, there is a rightful need to incorporate the effect of geometric consistency in the three-leg junctions safety studies.

Assessment of national parks affected by naturbanization processes in Southern Europe

This paper assesses land-use changes related to naturbanization processes on three biosphere reserves in Southern Europe. A comparative analysis has been done on the National Parks in Peneda-Ger^es in North Portugal, C_evennes in South France and Sierra Nevada in South Spain, using Corine Land Cover data from 1990 until 2006. Results indicate that the process of land-use intensification is taking place in the frame of naturbanization dynamics that could jeopardize the role of Protected Areas. Focusing on the trends faced by National Parks and their surrounding territories, the analysis demonstrates, both in quantitative and spatial terms, the intensification processes of land-use changes and how it is important to know them for coping with increasing threats. The article concludes that in the current context of increasing stresses, a broader focus on nature protection, encompassing the wider countryside, is needed if the initiatives for biodiversity protection are to be effective.

Towards effective control of P2P traffic aggregates in network infrastructures

Nowadays, many P2P applications proliferate in the Internet. The attractiveness of many of these systems relies on the collaborative approach used to exchange large resources without the dependence and associated constraints of centralized approaches where a single server is responsible to handle all the requests from the clients. As consequence, some P2P systems are also interesting and cost-effective approaches to be adopted by content-providers and other Internet players. However, there are several coexistence problems between P2P applications and In- ternet Service Providers (ISPs) due to the unforeseeable behavior of P2P traffic aggregates in ISP infrastructures. In this context, this work proposes a collaborative P2P/ISP system able to underpin the development of novel Traffic Engi- neering (TE) mechanisms contributing for a better coexistence between P2P applications and ISPs. Using the devised system, two TE methods are described being able to estimate and control the impact of P2P traffic aggregates on the ISP network links. One of the TE methods allows that ISP administrators are able to foresee the expected impact that a given P2P swarm will have in the underlying network infrastructure. The other TE method enables the definition of ISP friendly P2P topologies, where specific network links are protected from P2P traffic. As result, the proposed system and associated mechanisms will contribute for improved ISP resource management tasks and to foster the deployment of innovative ISP-friendly systems.

Automated network resilience optimization using computational intelligence methods

This paper presents an automated optimization framework able to provide network administrators with resilient routing configurations for link-state protocols, such as OSPF or IS-IS. In order to deal with the formulated NP-hard optimization problems, the devised framework is underpinned by the use of computational in- telligence optimization engines, such as Multi-objective Evolutionary Algorithms (MOEAs). With the objective of demonstrating the framework capabilities, two il- lustrative Traffic Engineering methods are described, allowing to attain routing con- figurations robust to changes in the traffic demands and maintaining the network stable even in the presence of link failure events. The presented illustrative results clearly corroborate the usefulness of the proposed automated framework along with the devised optimization methods.

Modeling microbes: New methods for integrated metabolic and regulatory network reconstruction

PhD Thesis in Bioengineering

Changes in institutional governance and management of higher education in Laos: National University of Laos

PhD thesis in Educational Sciences (specialization in Politics of Education).

Adult learning and education policies in Germany, Portugal and Sweden: An analysis of national reports to CONFINTEA VI

The chapter presents a theoretical proposal of three analytical models of Adult Learning and Education (ALE) policies. Some analytical categories and the corresponding dimensions are organised according to the ALE rationale which is typical of each social policy model. Historical, cultural and educational features are mentioned in connexion with the different policy models and its interpretative capacity to making sense of policies and practices implemented in Germany, Portugal and Sweden. !e analysis includes the states of the art and the official representations of ALE produced by the respective national authorities through national reports which were presented to CONFINTEA VI (2009).

Influence of oxygen plasma treatment parameters on poly(vinylidene fluoride) electrospun fiber mats wettability

Electrospun poly(vinylidene fluoride) (PVDF) fiber mats find applications in an increasing number of areas, such as battery separators, filtration and detection membranes, due to their excellent properties. However, there are limitations due to the hydrophobic nature and low surface energy of PVDF. In this work, oxygen plasma treatment has been applied in order to modify the surface wettability of PVDF fiber mats and superhydrophilic PVDF electrospun membranes have been obtained. Further, plasma treatment does not significantly influences fiber average size (~400 ± 200 nm), morphology, electroactive -phase content (~80-85%) or the degree of crystallinity (Xc of 42 ± 2%), allowing to maintain the excellent physical-chemical characteristics of PVDF. Plasma treatment mainly induces surface chemistry modifications, such as the introduction of oxygen and release of fluorine atoms that significantly changes polymer membrane wettability by a reduction of the contact angle of the polymer fibers and an overall decrease of the surface tension of the membranes.