Predictive modelling of eutrophication in the Pozón de la Dolores lake (Northern Spain) by using an evolutionary support vector machines approach.

Eutrophication is a water enrichment in nutrients (mainly phosphorus) that generally leads to symptomatic changes and deterioration of water quality and all its uses in general, when the production of algae and other aquatic vegetations are increased. In this sense, eutrophication has caused a variety of impacts, such as high levels of Chlorophyll a (Chl-a). Consequently, anticipate its presence is a matter of importance to prevent future risks. The aim of this study was to obtain a predictive model able to perform an early detection of the eutrophication in water bodies such as lakes. This study presents a novel hybrid algorithm, based on support vector machines (SVM) approach in combination with the particle swarm optimization (PSO) technique, for predicting the eutrophication from biological and physical-chemical input parameters determined experimentally through sampling and subsequent analysis in a certificate laboratory. This optimization technique involves hyperparameter setting in the SVM training procedure, which significantly influences the regression accuracy. The results of the present study are twofold. In the first place, the significance of each biological and physical-chemical variables on the eutrophication is presented through the model. Secondly, a model for forecasting eutrophication is obtained with success. Indeed, regression with optimal hyperparameters was performed and coefficients of determination equal to 0.90 for the Total phosphorus estimation and 0.92 for the Chlorophyll concentration were obtained when this hybrid PSO-SVM-based model was applied to the experimental dataset, respectively. The agreement between experimental data and the model confirmed the good performance of the latter.

Improvement on binding of chondroitin sulfate derivatives to midkine by increasing hydrophobicity.

The interactions between chondroitin sulfate (CS) and a wide number of proteins modulate important biological processes. Here, the binding properties to midkine and pleiotrophin of sulfated, fully protected intermediates, typically obtained in the chemical synthesis of CS oligosaccharides, were tested for the first time. Using a fluorescence polarization competition experiment, we discovered that these synthetic precursors strongly bound these two closely related cytokines involved in cancer and inflammation. The relative binding affinities of these intermediates were significantly higher than those displayed by the corresponding fully deprotected oligosaccharides, indicating that the presence of hydrophobic protecting groups strongly enhanced the binding of CS-like derivatives to midkine. These compounds offer novel opportunities for the development of potent inhibitors/activators of CS-protein interactions with potential therapeutic applications.

Detection and quantitative analysis of two independent binding modes of a small ligand responsible for DC-SIGN clustering.

DC-SIGN (dendritic cell-specific ICAM-3 grabbing non-integrin) is a C-type lectin receptor (CLR) present, mainly in dendritic cells (DCs), as one of the major pattern recognition receptors (PRRs). This receptor has a relevant role in viral infection processes. Recent approaches aiming to block DC-SIGN have been presented as attractive anti-HIV strategies. DC-SIGN binds mannose or fucose-containing carbohydrates from viral proteins such as the HIV envelope glycoprotein gp120. We have previously demonstrated that multivalent dendrons bearing multiple copies of glycomimetic ligands were able to inhibit DC-SIGN-dependent HIV infection in cervical explant models. Optimization of glycomimetic ligands requires detailed characterization and analysis of their binding modes because they notably influence binding affinities. In a previous study we characterized the binding mode of DC-SIGN with ligand 1, which shows a single binding mode as demonstrated by NMR and X-ray crystallography. In this work we report the binding studies of DC-SIGN with pseudotrisaccharide 2, which has a larger affinity. Their binding was analysed by TR-NOESY and STD NMR experiments, combined with the CORCEMA-ST protocol and molecular modelling. These studies demonstrate that in solution the complex cannot be explained by a single binding mode. We describe the ensemble of ligand bound modes that best fit the experimental data and explain the higher inhibition values found for ligand 2.

Hybrid modelling based on support vector regression with genetic algorithms in forecasting the cyanotoxins presence in the Trasona reservoir (Northern Spain).

Cyanotoxins, a kind of poisonous substances produced by cyanobacteria, are responsible for health risks in drinking and recreational waters. As a result, anticipate its presence is a matter of importance to prevent risks. The aim of this study is to use a hybrid approach based on support vector regression (SVR) in combination with genetic algorithms (GAs), known as a genetic algorithm support vector regression (GA-SVR) model, in forecasting the cyanotoxins presence in the Trasona reservoir (Northern Spain). The GA-SVR approach is aimed at highly nonlinear biological problems with sharp peaks and the tests carried out proved its high performance. Some physical-chemical parameters have been considered along with the biological ones. The results obtained are two-fold. In the first place, the significance of each biological and physical-chemical variable on the cyanotoxins presence in the reservoir is determined with success. Finally, a predictive model able to forecast the possible presence of cyanotoxins in a short term was obtained.

[Serum microRNA-21 usefulness in inflammatory pathology of the colon]. / Utilidad de la determinación sérica de microRNA-21 en la enfermedad inflamatoria del colon.

BACKGROUND: MicroRNAs are 20-22 nucleotide molecular structures with post-transcriptional activity that are involved in the immune response, as well as in the inflammatory pathways of different cells and tissues. AIMS: We present herein a prospective study in which serum microRNA-21 expression was determined in patients diagnosed with acute appendicitis as a model of bowel inflammation. MATERIAL AND METHODS: A prospective cohort study of patients diagnosed with acute appendicitis was conducted. Serum microRNA-21 was analyzed through the PCR of blood samples taken from the patients prior to surgery. MicroRNA-21 values were compared with the analytic variables (leukocytes, hemoglobin, hematocrit, platelets, prothrombin activity, glucose, urea, and creatinine) and the anatomopathologic variables (normal appendix, phlegmonous, gangrenous, and perforated acute appendicitis). RESULTS: A total of 60 patients with acute appendicitis diagnosis were consecutively included in the study from June to October 2009. Sixty-six percent of the patients were men (40 men and 20 women), with a mean age of 26.2±14.8 years. The mean absolute level of microRNA-21 was 24.8±0.93, whereas the mean microRNA-21 gene expression was 1.04±0.28. No correlation between the analytic and anatomopathologic parameters evaluated was observed (P=.47). CONCLUSIONS: It is necessary to continue to search for the most appropriate microRNAs, so that their determination in serum can lead to greater precision in establishing the diagnosis and outcome of inflammatory disorders of the bowel.

A dynamically coherent pattern of rhythms that matches between distant species across the evolutionary scale.

We address the temporal organization of circadian and ultradian rhythms, crucial for understanding biological timekeeping in behavior, physiology, metabolism, and alignment with geophysical time. Using a newly developed five-steps wavelet-based approach to analyze high-resolution time series of metabolism in yeast cultures and spontaneous movement, metabolism, and feeding behavior in mice, rats, and quails, we describe a dynamically coherent pattern of rhythms spanning over a broad range of temporal scales (hours to minutes). The dynamic pattern found shares key features among the four, evolutionary distant, species analyzed. Specifically, a branching appearance given by splitting periods from 24 h into 12 h, 8 h and below in mammalian and avian species, or from 14 h down to 0.07 h in yeast. Scale-free fluctuations with long-range correlations prevail below ~ 4 h. Synthetic time series modeling support a scenario of coexisting behavioral rhythms, with circadian and ultradian rhythms at the center of the emergent pattern observed.

Biochemical methane potential (BMP) of agro-food wastes from the Cider Region (Spain).

An inventory of agro-food industry organic waste streams with a high potential for biogas transformation was studied in a logistically viable area (Cider Region, Asturias, Spain). Three industries were selected as the most viable ones: livestock, dairy and beverage. The potential for methane production from six wastes (beverage waste, BW; milled apple waste, MA; milk waste, MK; yogurt waste, YG; fats and oils from dairy wastewater treatment, F&O and cattle manure, CM) at five different substrate:inoculum ratios (0.25, 0.50, 0.75, 1.00 and 1.50) was evaluated in laboratory batch assays. Obtained methane yields ranged from 202-549 mL STP CH(4)·g VS waste(-1), and the methane content in biogas ranged from 58-76%. The ultimate practical biochemical methane potentials were slightly affected by the substrate:inoculum ratio. The estimation of the regional fluxes of waste and methane potentials suggests anaerobic digestion as a sustainable solution for the valorization of the organic wastes generated in this Region.

Evaluation of pre-treatment processes for increasing biodegradability of agro-food wastes.

Anaerobic digestion (AD) technology can be employed for treating sewage sludge, livestock waste or food waste. Generally, the hydrolysis stage is the rate-limiting step of the AD processes for solid waste degradation. Therefore, physical, chemical and biological pre-treatment methods or their combination are required, in order to reduce the rate of such a limiting step. In this study, four methods (mechanical shredding, acid hydrolysis, alkaline hydrolysis and sonication) were tested to improve methane production and anaerobic biodegradability of different agro-food wastes and their mixtures. The kinetics of anaerobic degradation and methane production ofpre-treated individual wastes and selected mixtures were investigated with batch tests. Sonication at lower frequencies (37 kHz) proved to give the best results with methane productivity enhancements of over 100% in the case of pig manure and in the range of 10-47% for the other wastes assayed. Furthermore, the ultimate methane production was proportional, in all the cases, to the specific energy input applied (Es). Sonication can, thus, enhance waste digestion and the rate and quantity of biogas generated. The behaviour of the other pre-treatments under the conditions assayed is not significant. Only a slight enhancement of biogas production (around 10%) was detected for whey and waste activated sludge (WAS) after mechanical shredding. The lack of effectiveness of chemical pre-treatments (acid and alkaline hydrolysis) can be justified by the inhibition of the methanogenic process due to the presence of high concentrations of sodium (up to 8 g l(-1) in some tests). Only in the case of WAS did the acid hydrolysis considerably increase the biodegradability of the sample (79%), because in this case no inhibition by sodium took place. Some hints of a synergistic effect have been observed when co-digestion of the mixtures was performed.

Diffractive and reactive scattering of H2 from Ru(0001): experimental and theoretical study.

We present a combined experimental and theoretical study of the diffraction of H(2) from Ru(0001) in the incident energy range 78-150 meV, and a theoretical study of dissociative chemisorption of H(2) in the same system. Pronounced out-of-plane diffraction was observed in the whole energy range studied. The energy dependence of the elastic diffraction intensities was measured along the two main symmetry directions for a fixed parallel translational energy. The data were compared with quantum dynamics calculations performed by using DFT-based, six-dimensional potential energy surfaces calculated with both the PW91 and RPBE functionals, as well as with a functional obtained from a weighted average of both (the MIX functional, which was earlier shown to perform quite well for H(2) + Cu(111)). Our results show that the PW91 functional describes the H(2) diffraction intensities more accurately than the RPBE and the MIX functionals, although the absolute values of these intensities are overestimated in the calculations. For the reaction probabilities a preference for one or the other functional cannot be given over the entire energy range probed by the sticking experiments. The PW91 functional yields too high reaction probabilities over the entire investigated energy range, but is better than RPBE at low collision energies (<0.1 eV). The RPBE functional gives too low reaction probabilities at low energy and somewhat too high reaction probabilities at high energy, but agrees better with experiment than PW91 for energies >0.1 eV. The results suggest that, in order to get a better description of both H(2) diffraction and dissociative chemisorption for this system, a specific reaction parameter functional for H(2) + Ru(0001) is needed that is a weighted average of functionals other than PW91 and RPBE. We speculate that differences between the H(2) + Ru(0001) system (early and low reaction barrier) and H(2) + Cu(111) (late and high reaction barrier) may well lead to fundamentally different specific reaction parameter functionals, and that including a reasonable accurate description of the van der Waals interaction might be important for H(2) + Ru(0001) which has barriers localised far away from the surface. Based on our results we advocate new, systematic combined theoretical and experimental studies of H(2) interacting with transition metals in early and late barrier systems, with the aim of determining whether specific reaction parameter functionals for these systems might differ in a systematic way.

Contribution of a method of assessing minimum audible angle in headphones.

OBJECTIVES: The main objective of this study was to test the feasibility of measuring minimum audible angle in headphones with different reference positions in the horizontal plane, and comparing different types of pre-recorded head-related transfer functions. The secondary objective was to assess spatial discrimination performance in simulated unilateral hearing loss by measuring the minimum audible angle under monaural conditions using headphones. MATERIALS AND METHODS: Minimum audible angle was assessed in 27 normal-hearing subjects, to test their spatial discrimination abilities, using 4 datasets of pre-recorded head-related transfer functions: 2 recorded on mannequins (KU100, KEMAR), and 2 individualized head-related transfer function datasets (TBM, PBM). Performance was evaluated at 3 reference positions (0°, 50° and 180°) in 1 binaural and 2 monaural conditions. RESULTS: KU100 generated minimum audible angle values smaller than KEMAR in frontal and lateral position P<0.005), with a suggestive difference (P<0.05) compared to TBM and PBM in the frontal and lateral planes. Comparison between binaural and monaural conditions showed significant differences in frontal position for MON-c (contralateral) and MON-i (ipsilateral) (P<0.001), in lateral position for MON-c only (P<0.001) and in posterior position for MON-c and MON-i (P<0.001). CONCLUSION: This study suggests that evaluation of spatial discrimination capacity using minimum audible angle with the KU100 head-related transfer dataset was reliable and robust.

PM10 concentration forecasting in the metropolitan area of Oviedo (Northern Spain) using models based on SVM, MLP, VARMA and ARIMA: A case study.

Atmospheric particulate matter (PM) is one of the pollutants that may have a significant impact on human health. Data collected over seven years in a city of the north of Spain is analyzed using four different mathematical models: vector autoregressive moving-average (VARMA), autoregressive integrated moving-average (ARIMA), multilayer perceptron (MLP) neural networks and support vector machines (SVMs) with regression. Measured monthly average pollutants and PM10 (particles with a diameter less than 10µm) concentration are used as input to forecast the monthly averaged concentration of PM10 from one to seven months ahead. Simulations showed that the SVM model performs better than the other models when forecasting one month ahead and also for the following seven months.

Machine learning techniques applied to the determination of road suitability for the transportation of dangerous substances.

This article describes a methodology to model the degree of remedial action required to make short stretches of a roadway suitable for dangerous goods transport (DGT), particularly pollutant substances, using different variables associated with the characteristics of each segment. Thirty-one factors determining the impact of an accident on a particular stretch of road were identified and subdivided into two major groups: accident probability factors and accident severity factors. Given the number of factors determining the state of a particular road segment, the only viable statistical methods for implementing the model were machine learning techniques, such as multilayer perceptron networks (MLPs), classification trees (CARTs) and support vector machines (SVMs). The results produced by these techniques on a test sample were more favourable than those produced by traditional discriminant analysis, irrespective of whether dimensionality reduction techniques were applied. The best results were obtained using SVMs specifically adapted to ordinal data. This technique takes advantage of the ordinal information contained in the data without penalising the computational load. Furthermore, the technique permits the estimation of the utility function that is latent in expert knowledge.

Biology-based mapping of vector-borne parasites by geographic information systems and remote sensing.

Application of growing degree day-water budget analysis and satellite climatology to vector-borne parasites will be reviewed to demonstrate the value of using the unique thermal-hydrological preferences and limits of tolerance of individual parasite-vector systems to define the environmental niche of disease agents in the landscape by modern geospatial analysis methods. Applications of geospatial modeling will be illustrated by examples on fascioliasis, malaria, leprosy and leishmaniasis.

Automated manometric method to assess anaerobic toxicity of chemicals.

Industrial additives eventually used for different purposes (antifoaming, cleaning, bactericides, antiscale, etc) are discharged to the wastewater treatment plant. The anaerobic toxicity of these commercial products is not provided by suppliers. A new manometric method is developed and tested to evaluate anaerobic toxicity or inhibition using four different commercial products. Antifoaming Cleron 6 (50-200 ppm), bactericide Divosan-forte (0.05-1.0% v/v), bleach (0.1-1.0% v/v) and cleaning agent Topax 66 (0.10-1.0% v/v). According to the different methods proposed in the literature, from the methane production rate, it is possible to calculate both methanogenic activity evolution and final substrate removal and quantify the potential inhibitory effect of commercial additives. The experimental method is simple and reliable.

A mathematical approach to selective scavenging of the different classes of typical atmospheric aerosols by rainout and health impact.

In this work, initially a general mathematical framework for wet deposition processes is developed and followed by an in-depth discussion of the scavenging of material below cloud due to rain. These processes are then integrated into an overall framework focussing mainly on precipitation scavenging by rain. This work studies the scavenging efficiencies of aerosol particles within a given rain regime as a function of time by below-cloud scavenging. The health impact of aerosol before and after rain is also considered by comparing the respirable dust fractions. The well-known equations of below-cloud scavenging are applied to eight different classes of atmospheric aerosols (marine background (MB), clean continental background (CCB), average background (AB), background and aged urban plume (BAUP), background and local sources (BLS), urban average (UA), urban and freeway (UF) and central power plant (CCP)) in two precipitation regimes (drizzle and heavy rain) with one drop size distribution (DSD). From this study it is inferred that respirable dust is scavenged with relative ease by rainout. Compared with the volume of respirable aerosol average urban environment (UA) before rain roughly 5.2% remains after 18 hours of drizzle and 4% after 18 hours of heavy rain. Over a long timescale, the results show that heavy rain is more efficient than drizzle in particle scavenging.

Structural basis for chitin recognition by defense proteins: GlcNAc residues are bound in a multivalent fashion by extended binding sites in hevein domains.

BACKGROUND: Many plants respond to pathogenic attack by producing defense proteins that are capable of reversible binding to chitin, a polysaccharide present in the cell wall of fungi and the exoskeleton of insects. Most of these chitin-binding proteins include a common structural motif of 30 to 43 residues organized around a conserved four-disulfide core, known as the 'hevein domain' or 'chitin-binding' motif. Although a number of structural and thermodynamic studies on hevein-type domains have been reported, these studies do not clarify how chitin recognition is achieved. RESULTS: The specific interaction of hevein with several (GlcNAc)(n) oligomers has been studied using nuclear magnetic resonance (NMR), analytical ultracentrifugation and isothermal titration microcalorimetry (ITC). The data demonstrate that hevein binds (GlcNAc)(2-4) in 1:1 stoichiometry with millimolar affinity. In contrast, for (GlcNAc)(5), a significant increase in binding affinity is observed. Analytical ultracentrifugation studies on the hevein-(GlcNAc)(5,8) interaction allowed detection of protein-carbohydrate complexes with a ratio of 2:1 in solution. NMR structural studies on the hevein-(GlcNAc)(5) complex showed the existence of an extended binding site with at least five GlcNAc units directly involved in protein-sugar contacts. CONCLUSIONS: The first detailed structural model for the hevein-chitin complex is presented on the basis of the analysis of NMR data. The resulting model, in combination with ITC and analytical ultracentrifugation data, conclusively shows that recognition of chitin by hevein domains is a dynamic process, which is not exclusively restricted to the binding of the nonreducing end of the polymer as previously thought. This allows chitin to bind with high affinity to a variable number of protein molecules, depending on the polysaccharide chain length. The biological process is multivalent.

Geographical Information Systems risk assessment models for zoonotic fascioliasis in the South American Andes region.

The WHO recognises Fasciola hepatica to be an important human health problem. The Andean countries of Peru, Bolivia and Chile are those most severely affected by this distomatosis, though areas of Ecuador, Colombia and Venezuela are also affected. As part of a multidisciplinary project, we present results of use of a Geographical Information Systems (GIS) forecast model to conduct an epidemiological analysis of human and animal fasciolosis in the central part of the Andes mountains. The GIS approach enabled us to develop a spatial and temporal epidemiological model to map the disease in the areas studied and to classify transmission risk into low, moderate and high risk areas so that areas requiring the implementation of control activities can be identified. Current results are available on a local scale for: (1) the northern Bolivian Altiplano, (2) Puno in the Peruvian Altiplano, (3) the Cajamarca and Mantaro Peruvian valleys, and (4) the Ecuadorian provinces of Azuay, Cotopaxi and Imbabura. Analysis of results demonstrated the validity of a forecast model that combines use of climatic data to calculate of forecast indices with remote sensing data, through the classification of Normalized Difference Vegetation Index (NDVI) maps.

Automated equipment for anaerobic sludge parameters determination.

Methanogenic activity, anaerobic biodegradability and toxicity are key parameters in the design and operation of anaerobic bioreactors. A large variety of methods exist for the determination of these parameters but a normalized method has not been established so far. This paper presents the development of an automated manometric system for the determination of these anaerobic sludge parameters. The system is based on monitoring the production of methane by using a pressure transducer that measures the pressure in a gas-collecting chamber of known adjustable volume, which is independent of the space where biogas production takes place. The evolution of pressure generated by the accumulation of methane relates to the conversion of COD. In this way, the methanogenic activity of the sludge can be determined, as well as the biodegradability of solids and liquid, as well as the methanogenic toxicity of compounds. The equipment permits gas sampling, as well as extraction and introduction of liquid, without losing the anaerobic conditions. Various assays have been conducted to test the reliability and reproducibility of the obtained results, showing a high level of both. The methanogenic activities obtained in the assays ranged between 0.1 and 1.8 g COD g(-1) VSS d(-1), and the biodegradability of the organic compounds tested ranged between 20 and 90%.

The inhibitory effect of ionizing radiation on Fc and C3 receptors on mouse and human leukocytes, and the protective potential of human albumin.

The effect that ionizing radiation has in vitro on Fc and C3 receptors was evaluated at various doses and measured by means of erythrocytes coated with antibody (EA) and erythrocytes coated with antibody and complement (EAC) rosettes on human peripheral blood leukocytes (PBL) and on mouse bone marrow cells (BMC) and PBL. We found that the number of cells with either EA and EAC rosettes decreased as the radiation doses increased, and that they were almost absent when the highest doses were employed. We obtained evidence that albumin is a natural source of radio-protection for Fc and C3 receptors, and we showed that by increasing the amount of this molecule we could completely protect receptors for EA and EAC in vitro. Finally, the possible therapeutic value of the administration of human albumin to patients undergoing radiotherapy is discussed.

Air quality modeling in the Oviedo urban area (NW Spain) by using multivariate adaptive regression splines.

The aim of this research work is to build a regression model of air quality by using the multivariate adaptive regression splines (MARS) technique in the Oviedo urban area (northern Spain) at a local scale. To accomplish the objective of this study, the experimental data set made up of nitrogen oxides (NO x ), carbon monoxide (CO), sulfur dioxide (SO2), ozone (O3), and dust (PM10) was collected over 3 years (2006-2008). The US National Ambient Air Quality Standards (NAAQS) establishes the limit values of the main pollutants in the atmosphere in order to ensure the health of healthy people. Firstly, this MARS regression model captures the main perception of statistical learning theory in order to obtain a good prediction of the dependence among the main pollutants in the Oviedo urban area. Secondly, the main advantages of MARS are its capacity to produce simple, easy-to-interpret models, its ability to estimate the contributions of the input variables, and its computational efficiency. Finally, on the basis of these numerical calculations, using the MARS technique, conclusions of this research work are exposed.