Influence of different reactor types on Nannochloropsis oculata microalgae culture for lipids and fatty acid production

Greenhouse gases emitted into the atmosphere by burning of fossil fuels cause global warming. One option is obtaining biodiesel. Nannochloropsis oculata was cultured under different light intensities and reactors at 25°C for 21 days with f/2 medium to assess their effects on cell density, lipid, and fatty acids (FAs). N. oculata improved cell density on fed-batch glass tubular reactor (7 L) at 200 μmol E m⁻² s⁻¹, yielding 3.5 × 10⁸ cells ml⁻¹, followed by fed-batch Erlenmeyer flask (1 L) at 650 μmol E m⁻² s⁻¹ with 1.7 × 10⁸ cells ml⁻¹. The highest total lipid contents (% g lipid × g dry biomass⁻¹) were 44.4 ± 0.8% for the reactor (1 L) at 650 μmol E m⁻² s⁻¹ and 35.2 ± 0.2% for the tubular reactor (7 L) at 200 μmol E m⁻² s⁻¹, until twice as high compared with the control culture (Erlenmeyer flask 1 L, 80 μmol E m⁻² s⁻¹) with 21.2 ± 1%. Comparing the total lipid content at 200 μmol E m⁻² s⁻¹, tubular reactor (7 L) and reactor 1 L achieved 35.2 ± 0.2% and 28.3 ± 1%, respectively, indicating the effect of shape reactor. The FAs were affected by high light intensity, decreasing SFAs to 2.5%, and increased monounsaturated fatty acids + polyunsaturated fatty acids to 2.5%. PUFAs (20:5n-3) and (20:4n-3) were affected by reactor shape, decreasing by half in the tubular reactor. In the best culture, fed-batch tubular reactor (7 L) at 200 μmol E m⁻² s⁻¹ contains major FAs (16:0; 38.06 ± 0.16%), (16:1n-7; 30.74 ± 0.58%), and (18:1n-9; 17.15 ± 0.91%).     

Biodegradability and improved mechanical performance of polyhydroxyalkanoates/agave fiber biocomposites compatibilized by different strategies

In this work, biocomposites made of polyhydroxyalkanoates (PHA) with natural fibers were produced via compression molding. In particular, polyhydroxybutyrate (PHB) and polyhydroxybutyrate-co-hydroxyvalerate (PHBV) were reinforced with 20 wt% of agave fibers. Different compatibilization strategies were investigated to improve the fiber-matrix interaction: fiber surface treatment in PHA solution, fiber surface treatment in maleated PHA solution, fiber propionylation, and extrusion with maleated PHA. The biocomposites were characterized in terms of morphology, mechanical properties, water absorption, and biodegradability by CO₂ production tracking. In general, fiber propionylation was the best strategy for mechanical properties enhancement and water uptake decreasing. Biocomposites with propionylated fibers showed improved flexural strength (170% for PHB and 84% for PHBV). The flexural modulus was also enhanced with propionylated fibers up to 19% and 18% compared to uncompatibilized biocomposites (PHB and PHBV, respectively). Tensile strength increased by 16% (PHB) and 14% (PHBV), and the water absorption was reduced using propionylated fibers going from 6.6% to 4.4% compared with biocomposites with untreated fibers. Most importantly, the impact strength was also improved for all biocomposites by up to 96% compared with the neat PHA matrices. Finally, it was found that the compatibilization did not negatively modify the PHA biodegradability.     

Enhanced Oxygen Diffusion Along Internal Oxide–Metal Matrix Interfaces in Ni–Al Alloys During Internal Oxidation

A study of the internal oxidation of dilute Ni–Al alloys in an NiO/Ni Rhines pack was performed at 800, 1000, and 1100°C. Considerable deviations from the classical internal oxidation model have been observed. The rate of internal oxidation depends not only on the concentration of the alloying element but also on its nature, which contributes to determining the size, shape, orientation and distribution of the internal oxide precipitates. For instance, the precipitates in the Ni–Al alloys are continuous rods, arranged in a cone-shaped configuration that extends from the surface to the internal oxide front. The observed depths of internal oxidation for the various concentrations of aluminum are discussed and related to the morphologies of the internal oxide precipitates. The apparent N^(s) _oD_o values determined from internal oxide penetrations increase with increasing solute content in the alloy. It is postulated that diffusivity of oxygen is enhanced along the internal oxide–metal matrix interface compared with that in the metal matrix.     

Decision support system for classifying archaeological pottery profiles based on Mathematical Morphology

We present a decision support system to help archaeologists in classifying wheel-made pottery pieces by its profile. A novel shape characterization method, using Mathematical Morphology, is introduced for this purpose. Each profile is represented as a vector, obtained by sampling the so called morphological curves (erosion, dilation, opening and closing), and Euclidean Distance is used as a similarity measure. We show results of our method applied to a profile database of Iberian Pottery from the upper valley of Guadalquivir River (Spain).     

Binarised regression tasks: methods and evaluation metrics

Some supervised tasks are presented with a numerical output but decisions have to be made in a discrete, binarised, way, according to a particular cutoff. This binarised regression task is a very common situation that requires its own analysis, different from regression and classification—and ordinal regression. We first investigate the application cases in terms of the information about the distribution and range of the cutoffs and distinguish six possible scenarios, some of which are more common than others. Next, we study two basic approaches: the retraining approach, which discretises the training set whenever the cutoff is available and learns a new classifier from it, and the reframing approach, which learns a regression model and sets the cutoff when this is available during deployment. In order to assess the binarised regression task, we introduce context plots featuring error against cutoff. Two special cases are of interest, the \( UCE \) and \( OCE \) curves, showing that the area under the former is the mean absolute error and the latter is a new metric that is in between a ranking measure and a residual-based measure. A comprehensive evaluation of the retraining and reframing approaches is performed using a repository of binarised regression problems created on purpose, concluding that no method is clearly better than the other, except when the size of the training data is small.     

A novel methodology to predict urban traffic congestion with ensemble learning

Urban traffic congestion prediction is a very hot topic due to the environmental and economical impacts that currently implies. In this sense, to be able to predict bottlenecks and to provide alternatives to the circulation of vehicles becomes an essential task for traffic management. A novel methodology, based on ensembles of machine learning algorithms, is proposed to predict traffic congestion in this paper. In particular, a set of seven algorithms of machine learning has been selected to prove their effectiveness in the traffic congestion prediction. Since all the seven algorithms are able to address supervised classification, the methodology has been developed to be used as a binary classification problem. Thus, collected data from sensors located at the Spanish city of Seville are analyzed and models reaching up to 83 % are generated.     

Parallel simulation of Population Dynamics P systems: updates and roadmap

Population Dynamics P systems are a type of multienvironment P systems that serve as a formal modeling framework for real ecosystems. The accurate simulation of these probabilistic models, e.g. with Direct distribution based on Consistent Blocks Algorithm, entails large run times. Hence, parallel platforms such as GPUs have been employed to speedup the simulation. In 2012, the first GPU simulator of PDP systems was presented. However, it was able to run only randomly generated PDP systems. In this paper, we present current updates made on this simulator, involving an input modu le for binary files and an output module for CSV files. Finally, the simulator has been experimentally validated with a real ecosystem model, and its performance has been tested with two high-end GPUs: Tesla C1060 and K40.     

Quantitative mass spectrometry for colorectal cancer proteomics

This review documents the uses of quantitative MS applied to colorectal cancer (CRC) proteomics for biomarker discovery and molecular pathway profiling. Investigators are adopting various labeling and label-free MS approaches to quantitate differential protein levels in cells, tumors, and plasma/serum. We comprehensively review recent uses of this technology to examine mouse models of CRC, CRC cell lines, their secretomes and subcellular fractions, CRC tumors, CRC patient plasma/serum, and stool samples. For biomarker discovery these approaches are uncovering proteins with potential diagnostic and prognostic utility, while in vitro cell culture experiments are characterizing proteomic and phosphoproteomic responses to disrupted signaling pathways due to mutations or to inhibition of drugable enzymes.