Interactions between different biological forms of aquatic macrophytes in a eutrophic tropical reservoir in Northeastern Brazil / Interacciones entre las diferentes formas biológicas de los macrófitos acuáticos en un reservorio eutrófico tropical del Noreste de Brasil

Abstract: The aquatic plants and biological processes have different interactions and their knowledge may contribute to the understanding of environmental dynamics in wetlands. The aim of this study was to report the type of interactions that different biological forms of macrophytes stand in the eutrophic tropical reservoir of Penha reservoir, Northeastern Brazil. Data collection was captured every two months from October 2009 to October 2010, considering the hydrological cycle in one-year period. For this, twelve perpendicular transects (separated by 10 m) at the reservoir's water edge were defined; each transect had two plots of 625 cm² (25 x 25 cm, separated by one meter) from which samples were obtained. Plants were collected and transported in identified plastic bags for subsequent quantification of the dry weight biomass; additionally, pressed samples were made in the field for identification purposes. The relative interaction index (RII) was used to identify the existence of positive and/or negative interactions between the biomass of the biological forms of aquatic plants. Student's t-tests were used to analyze variations in the abiotic data and biomass over time, and to determine differences between the dry and rainy seasons. Pearson and Spearman correlation coefficients were calculated to determine correlations between the biological forms and the biomass of the macrophytes, as well as environmental variables and RII. In the dry season, the environment was mainly composed of floating macrophytes (1 013.98 g/m²), with mats of submerged macrophytes (98.18 g/m²) that demonstrated a range of positive (RII= 1.0) to negative (RII= -0.2) interactions. The biomass of emergent macrophytes increased throughout the dry season (4.87 to 106.91 g/m²) due to the nurse plant effect that served as a substratum (RII= 1.0). During the rainy season the biomass of submerged macrophytes was reduced by 97 % due to direct and indirect relationships (RII= -1.0) to other macrophytes. Rainfall and emergent plants contributed to a reduction in the biomass of floating macrophytes (19.16 g/m²). The emergence of a third group of plants (emergent) lead floating plants to occupy other areas and excluded submerged plants. Overall, the interactions among plants within ecosystems were not definite due to stand composition and seasonality. Rev. Biol. Trop. 65 (3): 1095-1104. Epub 2017 September 01.

Resumen: El conocimiento de las interacciones de las plantas acuáticas contribuye a la comprensión de la dinámica del ambiente (embalse Penha, Noreste Brasil). El objetivo del estudio fue reportar las posibles interacciones positivas y/o negativas que ocurren en los matorrales de macrófitos con distintos tipos de plantas, en un reservorio tropical eutrofizado. La recolección de los datos se hizo cada dos meses, desde Octubre 2009 hasta Octubre 2010, se consideró un ciclo hidrológico de un año. Fueron muestreados doce transectos perpendiculares a la orilla, separados 10 m entre ellos. En cada transecto se recolectaron dos cuadrículas de 625 cm² (25 x 25 cm) distantes 1 m uno del otro. Las plantas fueron retiradas y puestas en bolsas plásticas numeradas de acuerdo con el punto de recolección, para cuantificación del peso seco de la biomasa. En el campo se hizo el prensaje de las muestras para la identificación de las macrófitas. Se utilizó el índice relativo de interacciones (RII) para identificar la existencia de interacciones positivas y/o negativas entre las formas biológicas de las plantas acuáticas. Se uso el t Student para evaluar las variaciones de los datos abióticos y biomasa a lo largo del tiempo y determinar las diferencias entre las estaciones de lluvia y seca. Fueron calculados los coeficientes de correlación de Pearson y Spearman para determinar las correlaciones entre las formas biológicas y la biomasa de macrófitas, las variables ambientales y RII. En el período seco, el ambiente estaba compuesto principalmente por plantas flotantes (1 013.98 g/m²) y con la presencia de macrófitos sumergidos (98.18 g/m²), que demostraron interacciones variando de positivas (índice relativo de interacción - RII= 1.0) hasta negativas (RII= -0.2). La biomasa de macrófitos emersos aumentó a lo largo de la estación seca (4.87 to 106.91 g/m²) debido al efecto nodriza que sirve como substrato (RII= 1.0). En la estación lluviosa la biomasa de macrófitos sumergida se redujo 97 % debido a interacciones directas y indirectas (RII= -1.0) con otros macrófitos. La lluvia y los matorrales de plantas acuáticas contribuyen a la reducción en la biomasa de los macrófitos flotantes (19.16 g/m²). La aparición de un tercer grupo de plantas (emersas) llevan las flotantes a ocupar otros sitios y la exclusión de los macrófitos sumergidos. La interacción entre las plantas en el ecosistema evaluado no son rígidas debido a la composición de los matorrales y la estacionalidad.

Drug interaction: focusing on response surface models / 대한마취과학회지

Anesthesiologists have been aware of the importance of optimal drug combination long ago and performed many investigations about the combined use of anesthetic agents. There are 3 classes of drug interaction: additive, synergistic, and antagonistic. These definitions of drug interaction suggest that a zero interaction model should exist to be used as a reference in classifying the interaction of drug combinations. The Loewe additivity has been used as a universal reference model for classifying drug interaction. Most anesthetic drugs follow the sigmoid E(max) model (Hill equation); this model will be used for modeling response surface. Among lots of models for drug interaction in the anesthetic area, the Greco model, Machado model, Plummer model, Carter model, Minto model, Fidler model, and Kong model are adequate to be applied to the data of anesthetic drug interaction. A model with a single interaction parameter does not accept an inconsistency in the classes of drug interactions. To solve this problem, some researchers proposed parametric models which have a polynomial interaction function to capture synergy, additivity, and antagonism scattered all over the surface of drug combinations. Inference about truth must be based on an optimal approximating model. Akaike information criterion (AIC) is the most popular approach to choosing the best model among the aforementioned models. Whatever the good qualities of a chosen model, it is uncertain whether the chosen model is the best model. A more robust inference can be extracted from averaging several models that are considered relevant.

Analysis of the rocuronium-vecuronium interaction on tetanic fade based on a generalized response surface model with varying relative potencies / 대한마취과학회지

BACKGROUND: Some studies have shown that rocuronium and vecuronium have additive, or synergistic effects on muscle relaxation based on the Loewe additivity. Therefore, we performed a fit of tetanic fade data to a generalized response surface model with varying relative potencies proposed by Kong and Lee (KLGRS) to evaluate the usefulness of KLGRS for capturing the interspersed drug interactions and to characterize the interaction between the two drugs. METHODS: Left phrenic nerve-hemidiaphragms (Male Sprague-Dawley rats, 150-250 g) were mounted in Krebs solution. Supramaximal electrical stimulation (0.2 ms, rectangular) of 50 Hz for 1.9 s to the phrenic nerve evoked tetanic contractions that were measured with a force transducer. Each preparation was exposed to one of 4 vecuronium concentrations (0.0, 1.5, 2.5, and 3.0 microM), or one of 4 rocuronium concentrations (0.0, 3.0, 4.5, and 5.5 microM). Subsequently the adequate amount of rocuronium was added to a vecuronium bath and that of vecuronium was added to a rocuronium until an 80-90% increase in tetanic fade was achieved. We then fitted the modified KLGRS models to the above data, after which we selected the best model, based on 5 methods for determining goodness of fit. Using this method, we obtained the response surface, as well as contour plots for the response surface (i.e. isoboles), the polynomial function and the interaction index. RESULTS: The model with the constant relative potency ratio and 8 parameters was found to best describe the results, and this model reflected well the characteristics of the raw data. In addition, the two drugs showed a synergistic interaction in almost every area and an antagonistic one in a very narrow area. CONCLUSIONS: KLGRS was found to be a useful method of analyzing data describing interspersed drug interactions. The interaction between rocuronium and vecuronium was found to be synergistic.