Abstract Most methods for estimating the morphometric values of water bodies use equations derived from hypsographic curves or digital terrain models (DTMs) that relate depth, volume (V), and area (A), and that model the uncertainty inherent in the complex underwater morphology. This work tests the performance (precision and processing time) of an algorithm to calculate morphometric parameters of a lake that uses bathymetry and topography of the surrounding water body area. The projection of the water surface height (H) on each DTM pixel generates a water column with intrinsic attributes such as volume and area. The process is replicated among all cells and estimates the total area and volume of the water body. If the V or A is the input data, an algorithm that iterates height values is used to generate the new data, which is compared to the entered value that functions as a reference. If the difference between the reference value and the calculated value is less than an error threshold, the iteration stops, and the maximum and average depths are calculated. The raster and the shape that represent the body of water are created. The crosscomparison of H-V-A showed that there is an error between 0.0034% and 0.000039% when any of the parameters are used as input data. Performance tests determined that pixel dimensions are directly proportional to the processing time for each iteration. The results of the implementation of this algorithm were satisfactory since, for the DTM of Bustillos Lake, Chihuahua, Mexico, the simulation took less than 17 seconds in at most 22 iterations.

Resumen La mayoría de los métodos para estimar los valores morfométricos de cuerpos de agua utilizan ecuaciones derivadas de curvas hipsográficas o de modelos digitales del terreno (DTM) que relacionan la profundidad, el volumen (V) y el área (A), y así modelan la incertidumbre inherente a la compleja morfología acuática. Este trabajo prueba el desempeño (precisión y tiempo de procesamiento) de un algoritmo para calcular los parámetros morfométricos de un lago que utiliza batimetría y topografía del área circundante al cuerpo de agua. La proyección de la altura de la superficie del agua (H) en cada pixel DTM genera una columna de agua con atributos intrínsecos como volumen y superficie. El proceso se replica entre todas las células y estima el área total y el volumen del cuerpo de agua. Si V o A son los datos de entrada, se utiliza un algoritmo que itera los valores de altura para generar los nuevos datos, que se comparan con el valor introducido que funciona como referencia. Si la diferencia entre el valor de referencia y el valor calculado es menor que un umbral de error, la iteración se detiene y se calculan las profundidades máxima y media. Se crean el ráster y el shape que representan el cuerpo de agua. La comparación cruzada de H-V-A mostró que hay un error entre 0.0034% y 0.000039% cuando cualquiera de los parámetros se usa como datos de entrada. Las pruebas de rendimiento determinaron que las dimensiones de los pixeles son directamente proporcionales al tiempo de procesamiento de cada iteración. Los resultados de la implementación de este algoritmo fueron satisfactorios ya que, para el DTM del lago Bustillos, Chihuahua, México, la simulación tomó menos de 17 segundos en un máximo de 22 iteraciones.

Highly-potent antiretroviral therapy is necessary to avoid viral replication in HIV patients; however, it can favor the appearance of resistance mutations. The mutations 41L, 67N, 70R, 210W, 215Y/F, 219E/Q, 44D and 118I are defined as nucleoside analogous mutations (NAMs), because they affect the efficacy of all nucleoside reverse transcriptase inhibitors (NRTI). The mutation most frequently associated with non-nucleoside reverse transcriptase inhibitors (NNRTIs) is 103N. 33W/F, 82A/F/L/T, 84V and 90M are called protease inhibitor resistance-associated mutations (PRAM), because they are associated with resistance to several protease inhibitors (PI). This study evaluated the development of resistance mutations and examine the susceptibility of HIV with these mutations to antiretrovirals in HIV-1 patients who have failed one or more therapy regimes. Analyses were made of 101 genotypic tests of patients with therapeutic failure to 2 or 3-drug regimens with NRTI, NNRTI or PI. We used the Stanford database to define the susceptibility profile of the viruses. The samples were divided into three treatment-failure groups: first (F), second (S) and multi-failure (MF) to antiretroviral regimens, and we correlated these groups with resistance profiles and principal mutations. There was a significant increase in resistance mutations V82A/F/L/T, I84V, L90M, M41L, K70R, L210W, T215Y/F and K219Q/E in MF. We also found significantly higher resistance to zidovudine, didanosine, stavudine and abacavir in MF. There was no increase in resistance to tenofovir (p=0.28) and lopinavir (p=0.079) in MF. A high degree of resistance to NNRTIs was observed in all groups. Increased resistance mutations will affect future therapeutic options for HIV patients in Brazil because it results in a significant increase in resistance to antiretroviral drugs.

Genotype testing for HIV-1 drug resistance is useful for selecting antiretroviral drug regimens for patients experiencing therapeutic failure, but the optimal means for interpreting the test results is unknown because many HIV-1 protease and reverse transcriptase (RT) mutations contribute to drug resistance. This study identified common combinations of resistance mutations related to antiretroviral resistance profiles. From April 2002 to March 2004, 101 protease and RT sequences were determined for HIV-1 isolates from patients who were failing antiretroviral therapy. The resistance profile was evaluated using the Stanford Database program. Male patients predominated (76.2%), the median age was 38 years, the average CD4 count was 279.21 cells/mm³ and the average viral load was 4.49 log. In relation to protease inhibitors (IP) 31 mutation patterns were detected, 49 mutation patterns were detected in Nucleoside RT Inhibitors (NRTI), and 17 patterns were found in the Non Nucleoside RT Inhibitors (NNRTI). K65R was detected in 5.9% of the isolates. The most frequent mutations were: L90M, M184V and K103N related to PI's, NRTI's and NNRTI's, respectively. The best antiretroviral susceptibility was found to be Lopinavir in the PI class and Tenofovir in the NRTI class. The top six mutation patterns accounted for 49% of the resistance to PI's, for 38.5% of NRTI resistance, and the top two mutation patterns accounted for 40.9% of resistance to NNRTI's.