Update on the pharmacology of Spirulina (Arthrospira), an unconventional food

Spirulina (Arthrospira), a filamentous, unicellular alga, is a cyanobacterium grown in certain countries as food for human and animal consumption. It is also used to derive additives in pharmaceuticals and foods. This alga is a rich source of proteins, vitamins, amino acids, minerals, and other nutrients. Its main use, therefore, is as a food supplement. Over the last few years, however, it has been found to have many additional pharmacological properties. Thus, it has been experimentally proven, in vivo and in vitro that it is effective to treat certain allergies, anemia, cancer, hepatotoxicity, viral and cardiovascular diseases, hyperglycemia, hyperlipidemia, immunodeficiency, and inflammatory processes, among others. Several of these activities are attributed to Spirulina itself or to some of its components including fatty acids omega-3 or omega-6, beta-carotene, alpha-tocopherol, phycocyanin, phenol compounds, and a recently isolated complex, Ca-Spirulan (Ca-SP). This paper aims to update and critically review the results published over the last few years with regards to these properties. The conclusion is that even if this cyanobacterium has been one of the most extensively studied from the chemical, pharmacological and toxicological points of view, it is still necessary to expand the research in order to have more consistent data for its possible use in human beings.     

Fabrication of hybrid proton-exchange membranes using a brandnew high temperature ionic liquid as charge transporting and clay modifier

The search for more compatibility between ionic liquids (ILs) and polymer matrices in proton-exchange membrane fuel cells (PEMFCs) is one of the ways in which IL leaking from proton-exchange membranes could be minimized. In this work, it is presented the synthesis of an aromatic high temperature ionic liquid (HTIL), which, incorporated into an aromatic matrix such as sulfonated polyether ether ketone (sPEEK), is expected to diminish the IL leaking that normally affects PEMFC. Phenylethylammonium trifluoromethane sulfonate (PhetaTfO) was successfully synthesized and characterized. Its melting point of 88°C makes it to classify as a HTIL and it was employed as modifier of natural Montmorillonite, forming the phenylethylammonium intercalated montmorillonite (MmtPheta) and thus, ternary membranes containing PhetaTfO, MmtPheta, and sPEEK were prepared and characterized. Immersion tests demonstrated a higher compatibility of PhetaTfO with matrix when compared to the reference DemaTfO, which was reflected in up to 30% lower IL loss by the synthesized IL than the DemaTfO; X-rays diffraction (XRD) patterns demonstrated that the modified clay was properly dispersed inside the membranes, while dynamic mechanical analyses (DMA) results indicated a strong plasticizer effect along the increase of PhetaTfO content inside the membrane, while at the same time, the conductivity increased in an exponential manner, which permitted to identify an empiric exponential equation to evaluate the effect of concentration on ionic conductivity. The maximum conductivity obtained at IL concentrations of around 38 wt% was 0.2 mS/cm. It could expect high ionic conductivities of 10 mS/cm when the concentration of this IL is 60%; nevertheless, in order to achieve that, crosslinking treatments should be done to give the membranes enough mechanical resistance.     

When a Tritrophic Interaction Goes Wrong to the Third Level: Xanthoxylin From Trees Causes the Honeybee Larval Mortality in Colonies Affected by the River Disease

The “River Disease” (RD), a disorder impacting honeybee colonies located close to waterways with abundant riparian vegetation (including Sebastiania schottiana, Euphorbiaceae), kills newly hatched larvae. Forager bees from RD-affected colonies collect honeydew excretions from Epormenis cestri (Hemiptera: Flatidae), a planthopper feeding on trees of S. schottiana. First-instar honeybee larvae fed with this honeydew died. Thus, we postulated that the nectars of RD-affected colonies had a natural toxin coming from either E. cestri or S. schottiana. An untargeted metabolomics characterization of fresh nectars extracts from colonies with and without RD allowed to pinpoint xanthoxylin as one of the chemicals present in higher amounts in nectar from RD-affected colonies than in nectars from healthy colonies. Besides, xanthoxylin was also found in the aerial parts of S. schottiana and the honeydew excreted by E. cestri feeding on this tree. A larva feeding assay where xanthoxylin-enriched diets were offered to 1^st instar larvae showed that larvae died in the same proportion as larvae did when offered enriched diets with nectars from RD-colonies. These findings demonstrate that a xenobiotic can mimic the RD syndrome in honeybee larvae and provide evidence of an interspecific flow of xanthoxylin among three trophic levels. Further, our results give information that can be considered when implementing measures to control this honeybee disease.

     

Alternative technique for hydroxyapatite coatings

Flame Assisted Chemical Vapor Deposition (FACVD), a novel technique that shows an enormous potential in porous oxides deposition, was employed for the first time aiming to obtain hydroxyapatite (HA) coatings on 316 L stainless steel metallic substrates. Calcium acetate and ammonium phosphate diluted in ethanol were employed as precursor salts. A Ca/P molar ratio of 1.66 was employed in precursor solution, which is equivalent to stoichiometric hydroxyapatite. A porous coating, formed by an open and interconnected network, was observed by scanning electronic microscopy (SEM) and associated with homogenous reactions. Thickness of hydroxyapatite coating was 412 ± 3 μm. X-ray diffraction (XRD) analysis indicated the presence of crystalline coatings, mainly constituted by hydroxyapatite phase and traces of tricalcium phosphate (β-TCP). Carbonate in the hydroxyapatite coatings was identified by Fourier transform-infrared (FTIR) spectroscopy.     

A dynamic overproduce-and-choose strategy for the selection of classifier ensembles

The overproduce-and-choose strategy, which is divided into the overproduction and selection phases, has traditionally focused on finding the most accurate subset of classifiers at the selection phase, and using it to predict the class of all the samples in the test data set. It is therefore, a static classifier ensemble selection strategy. In this paper, we propose a dynamic overproduce-and-choose strategy which combines optimization and dynamic selection in a two-level selection phase to allow the selection of the most confident subset of classifiers to label each test sample individually. The optimization level is intended to generate a population of highly accurate candidate classifier ensembles, while the dynamic selection level applies measures of confidence to reveal the candidate ensemble with the highest degree of confidence in the current decision. Experimental results conducted to compare the proposed method to a static overproduce-and-choose strategy and a classical dynamic classifier selection approach demonstrate that our method outperforms both these selection-based methods, and is also more efficient in terms of performance than combining the decisions of all classifiers in the initial pool.     

LEGClust—A Clustering Algorithm Based on Layered Entropic Subgraphs

Hierarchical clustering is a stepwise clustering method usually based on proximity measures between objects or sets of objects from a given data set. The most common proximity measures are distance measures. The derived proximity matrices can be used to build graphs, which provide the basic structure for some clustering methods. We present here a new proximity matrix based on an entropic measure and also a clustering algorithm (LEGCIust) that builds layers of subgraphs based on this matrix and uses them and a hierarchical agglomerative clustering technique to form the clusters. Our approach capitalizes on both a graph structure and a hierarchical construction. Moreover, by using entropy as a proximity measure, we are able, with no assumption about the cluster shapes, to capture the local structure of the data, forcing the clustering method to reflect this structure. We present several experiments on artificial and real data sets that provide evidence on the superior performance of this new algorithm when compared with competing ones.     

LEGClust- a clustering algorithm based on layered entropic subgraphs.

Hierarchical clustering is a stepwise clustering method usually based on proximity measures between objects or sets of objects from a given data set. The most common proximity measures are distance measures. The derived proximity matrices can be used to build graphs, which provide the basic structure for some clustering methods. We present here a new proximity matrix based on an entropic measure and also a clustering algorithm (LEGClust) that builds layers of subgraphs based on this matrix, and uses them and a hierarchical agglomerative clustering technique to form the clusters. Our approach capitalizes on both a graph structure and a hierarchical construction. Moreover, by using entropy as a proximity measure we are able, with no assumption about the cluster shapes, to capture the local structure of the data, forcing the clustering method to reflect this structure. We present several experiments on artificial and real data sets that provide evidence on the superior performance of this new algorithm when compared with competing ones.     

Improving the schedulability of soft real-time open dynamic systems: The inheritor is actually a debtor

This paper presents the Clearing Fund Protocol, a three layered protocol designed to schedule soft real-time sets of precedence related tasks with shared resources. These sets are processed in an open dynamic environment. Open because new applications may enter the system at any time and dynamic because the schedulability is tested on-line as tasks request admission. Top-down, the three layers are the Clearing Fund, the Bandwidth Inheritance and two versions of the Constant Bandwidth Server algorithms. Bandwidth Inheritance applies a priority inheritance mechanism to the Constant Bandwidth Server. However, a serious drawback is its unfairness. In fact, a task executing in a server can potentially steal the bandwidth of another server without paying any penalty. The main idea of the Clearing Fund Algorithm is to keep track of processor-time debts contracted by lower priority tasks that block higher priority ones and are executed in the higher priority servers by having inherited the higher priority. The proposed algorithm reduces the undesirable effects of those priority inversions because the blocked task can finish its execution in its own server or in the server of the blocking task, whichever has the nearest deadline. If demanded, debts are paid back in that way. Inheritors are therefore debtors. Moreover, at certain instants in time, all existing debts may be waived and the servers are reset making a clear restart of the system. The Clearing Fund Protocol showed definite better performances when evaluated by simulations against Bandwidth Inheritance, the protocol it tries to improve.     

A prototype for cartographic human body analysis

A cartographic-oriented model uses algebraic map operations to perform spatial analysis of medical data relative to the human body. A prototype system uses 3D visualization techniques to deliver analysis results. A prototype implementation suggests the model might provide the basis for a medical application tool that introduces new information insight.