Techniques for monthly rainfall regionalization in southwestern Colombia.

The knowledge of rainfall regimes is a relevant requirement for many activities such as water resources planning, risk management, agriculture activities management, and other hydrologic applications. The present study has consisted of validating four techniques (one linear, one non-linear, and two hybrids) that allow identifying homogenous regions. We take the monthly rainfall in the Southwestern Colombia (Nariño), an area of 33,268 km2 characterized by complex topography and local factors that can influence the rainfall behavior, to test all techniques. The results showed overall the best performance for the approach related to non-linear principal component analysis and self-organizing map. However, in all mainly prevail two regions: the Andean Region and Pacific Region with a bimodal and unimodal regime, respectively. The bimodal one dominates over the Andes mountains range and the unimodal one the coastal zone. The application of non-linear approaches provided a better understanding of the seasonality of rainfall, and the results may be useful for water resource management.

Missing data estimation in extreme rainfall indices for the Metropolitan area of Cali - Colombia: An approach based on artificial neural networks.

Changes observed in the current climate and projected for the future significantly concern researchers, decision-makers, and the general public. Climate indices of extreme rainfall events are a trend assessment tool to detect climate variability and change signals, which have an average reliability at least in the short term and given climatic inertia. This paper shows 12 climate indices of extreme rainfall events for annual and seasonal scales for 12 climate stations between 1969 to 2019 in the Metropolitan area of Cali (southwestern Colombia). The construction of the indices starts from daily rainfall time series, which although have between 0.5% and 5.4% of missing data, can affect the estimation of the indices. Here, we propose a methodology to complete missing data of the extreme event indices that model the peaks in the time series. This methodology uses an artificial neural network approach known as Non-Linear Principal Component Analysis (NLPCA). The approach reconstructs the time series by modulating the extreme values of the indices, a fundamental feature when evaluating extreme rainfall events in a region. The accuracy in the indices estimation shows values close to 1 in the Pearson's Correlation Coefficient and in the Bi-weighting Correlation. Moreover, values close to 0 in the percent bias and RMSE-observations standard deviation ratio. The database provided here is an essential input in future evaluation studies of extreme rainfall events in the Metropolitan area of Cali, the third most crucial urban conglomerate in Colombia with more than 3.9 million inhabitants.

Solving the SSVEP Paradigm Using the Nonlinear Canonical Correlation Analysis Approach.

This paper presents the implementation of nonlinear canonical correlation analysis (NLCCA) approach to detect steady-state visual evoked potentials (SSVEP) quickly. The need for the fast recognition of proper stimulus to help end an SSVEP task in a BCI system is justified due to the flickering external stimulus exposure that causes users to start to feel fatigued. Measuring the accuracy and exposure time can be carried out through the information transfer rate-ITR, which is defined as a relationship between the precision, the number of stimuli, and the required time to obtain a result. NLCCA performance was evaluated by comparing it with two other approaches-the well-known canonical correlation analysis (CCA) and the least absolute reduction and selection operator (LASSO), both commonly used to solve the SSVEP paradigm. First, the best average ITR value was found from a dataset comprising ten healthy users with an average age of 28, where an exposure time of one second was obtained. In addition, the time sliding window responses were observed immediately after and around 200 ms after the flickering exposure to obtain the phase effects through the coefficient of variation (CV), where NLCCA obtained the lowest value. Finally, in order to obtain statistical significance to demonstrate that all approaches differ, the accuracy and ITR from the time sliding window responses was compared using a statistical analysis of variance per approach to identify differences between them using Tukey's test.

A Data Analytics/Big Data Framework for Advanced Metering Infrastructure Data.

The Advanced Metering Infrastructure (AMI) data represent a source of information in real time not only about electricity consumption but also as an indicator of other social, demographic, and economic dynamics within a city. This paper presents a Data Analytics/Big Data framework applied to AMI data as a tool to leverage the potential of this data within the applications in a Smart City. The framework includes three fundamental aspects. First, the architectural view places AMI within the Smart Grids Architecture Model-SGAM. Second, the methodological view describes the transformation of raw data into knowledge represented by the DIKW hierarchy and the NIST Big Data interoperability model. Finally, a binding element between the two views is represented by human expertise and skills to obtain a deeper understanding of the results and transform knowledge into wisdom. Our new view faces the challenges arriving in energy markets by adding a binding element that gives support for optimal and efficient decision-making. To show how our framework works, we developed a case study. The case implements each component of the framework for a load forecasting application in a Colombian Retail Electricity Provider (REP). The MAPE for some of the REP's markets was less than 5%. In addition, the case shows the effect of the binding element as it raises new development alternatives and becomes a feedback mechanism for more assertive decision making.

The Power of Big Data and Data Analytics for AMI Data: A Case Study.

In recent years, there has been a transformation in the value chain of different industrial sectors, like the electricity networks, with the appearance of smart grids. Currently, the underlying knowledge in raw data coming from numerous devices can mark a significant competitive advantage for utilities. It is the case of the Advanced Metering Infrastructure (AMI). Such technology gets user consumption characteristics at levels of detail that were previously not possible. In this context, the terms big data and data analytics become relevant, which are tools that allow using large volumes of information and the generation of valuable knowledge from raw data that can support data-driven decisions for operating on the grid. This paper presents the results of the big data implementation and data analytics techniques in a case study with smart metering data from the city of London. Implemented big data and data analytic techniques to show how to understand user consumption patterns on a broader horizon, the relationships with seasonal variables identify behaviors related to specific events and atypical consumptions. This knowledge helps support decision making about improving demand response programs and, in general, the planning and operation of the Smart Grid.

[Colonic balantidiasis: report of a fatal case and review of the literature]. / Balantidiasis colónica: reporte de un caso fatal y revisión de la literatura.

Balantidiasis is a zoonosis produced by Balantidium coli, which inhabits the large intestine of the pig and man. Infection is uncommon in humans and mainly affects the colon. It occurs more frequently in developing countries, tropical and subtropical regions. Colonic balantidiasis can occur in most cases asymptomatically and reach in the most severe cases such as dysenteric diarrhea that can be complicated by low digestive bleeding and even perforation. We present the case of a 72-year-old man, from the Peruvian highlands, who was a farmer and breeder of swine and sheep, who came for 3 months of illness, initially characterized by liquid stools with bloodless mucus, abdominal pain, nausea, vomiting and in the last month of illness he presents dysenteric diarrhea. Colonoscopy was performed due to suspicion of infectious colitis, Balantidium coli trophozoites were found in the fresh sample and colonic tissue biopsy. Patient receives treatment with amebicide and antibacterial without clinical improvement, presenting as a complication multiple perforation in the sigmoid colon, treated with resection and terminal colostomy. Finally, the patient died despite receiving medical and surgical treatment.

Estimation of missing data of monthly rainfall in southwestern Colombia using artificial neural networks.

The success of many projects linked to the management and planning of water resources depends mainly on the quality of the climatic and hydrological data that is provided. Nevertheless, the missing data are frequently found in hydroclimatic variables due to measuring instrument failures, observation recording errors, meteorological extremes, and the challenges associated with accessing measurement areas. Hence, it is necessary to apply an appropriate fill of missing data before any analysis. This paper is intended to present the filling of missing data of monthly rainfall of 45 gauge stations located in southwestern Colombia. The series analyzed covers 34 years of observations between 1983 and 2016, available from the Instituto de Hidrología, Meteorología y Estudios Ambientales (IDEAM). The estimation of missing data was done using Non-linear Principal Component Analysis (NLPCA); a non-linear generalization of the standard Principal Component Analysis Method via an Artificial Neural Networks (ANN) approach. The best result was obtained using a network with a [45-44-45] architecture. The estimated mean squared error in the imputation of missing data was approximately 9.8 mm. month-1, showing that the NLPCA approach constitutes a powerful methodology in the imputation of missing rainfall data. The estimated rainfall dataset helps reduce uncertainty for further studies related to homogeneity analyses, conglomerates, trends, multivariate statistics and meteorological forecasts in regions with information deficits such as southwestern Colombia.

Identificación de la delimitación administrativa de la malaria usando redes neuronales artificiales / Boundary Delimitiation of Malaria using Artificial Neural Networks / Identificação da Delimitação Administrativa da Malária usando Redes Neurais Artificiais

Resumen La metodología de clustering fue utilizada para agrupar tres barrios en Quibdó teniendo en cuenta factores que favorecen el desarrollo de la malaria. Los mapas auto-organizados de Kohonen fueron utilizados para el análisis de las características más significativas en la clasificación. Los clusters detectados fueron comparados con la clasificación geográfica de las casas, encontrando, que los mapas auto-organizados de Kohonen clasifican las casas por las condiciones ambientales propicias para el desarrollo del mosquito más que por la clasificación administrativa de la ciudad.

Resumo A Metodologia de Clustering foi usada para agrupar três bairros em Quibdo, Colômbia, levando em consideração fatores que favorecem o desenvolvimento da malária. Mapas auto-organizados de Kohonen foram utilizados para a análise das características mais significativas no agrupamento. Os Clusters detectados foram comparados com o agrupamento geo-gráfico de casas, mostrando que os mapas auto-organizados de Kohonen agrupam as casas pelas condições ambientais favoráveis ao desenvolvimento do mosquito e não pelo agrupamento administrativo da cidade.

Abstract Clustering methodology was used to group three neighborhoods in Quibdo taking into account factors that favor the development of malaria. The Kohonen self-organizing maps were used for the analysis of the most significant features in the standings. The detected clusters were compared with the geographical classification of houses, finding that the Kohonen self-organizing maps households classified by environmental conditions conducive to development rather than the administrative classification of the city.

Assessment of simulated high-dose partial-body irradiation by PCC-R assay.

The estimation of the dose and the irradiated fraction of the body is important information in the primary medical response in case of a radiological accident. The PCC-R assay has been developed for high-dose estimations, but little attention has been given to its applicability for partial-body irradiations. In the present work we estimated the doses and the percentage of the irradiated fraction in simulated partial-body radiation exposures at high doses using the PCC-R assay. Peripheral whole blood of three healthy donors was exposed to doses from 0-20 Gy, with 6°Co gamma radiation. To simulate partial body irradiations, irradiated and non-irradiated blood was mixed to obtain proportions of irradiated blood from 10-90%. Lymphocyte cultures were treated with Colcemid and Calyculin-A before harvest. Conventional and triage scores were performed for each dose, proportion of irradiated blood and donor. The Papworth's u test was used to evaluate the PCC-R distribution per cell. A dose-response relationship was fitted according to the maximum likelihood method using the frequencies of PCC-R obtained from 100% irradiated blood. The dose to the partially irradiated blood was estimated using the Contaminated Poisson method. A new D0 value of 10.9 Gy was calculated and used to estimate the initial fraction of irradiated cells. The results presented here indicate that by PCC-R it is possible to distinguish between simulated partial- and whole-body irradiations by the u-test, and to accurately estimate the dose from 10-20 Gy, and the initial fraction of irradiated cells in the interval from 10-90%.

Conocimientos, actitudes y prácticas de las mujeres embarazadas en Iquitos, Perú, acerca de la anemia, la uncinariasis y el uso de antihelmínticos / Knowledge, attitudes and practices of the women embarrassed in Iquitos, Peru, about the anemia, the uncinariasis and the use of antihelmintics

Un estudio sobre conocimientos, actitudes y prácticas (CAP) fue desarrollado en julio de 2001, acerca de 99 gestantes atendidas por la clínica prenatal del Hospital Apoyo de Iquitos, Perú, la anemia, la uncinariasis y el uso de antihelmínticos. 55 por ciento sabían lo que la anemia era. Aunque el 53 por ciento sabían lo que eran parásitos, solo el 21 por ciento podían identificar un parásito por algún nombre. El 60 por ciento de la mujeres creían que tomar medicamentos durante el embarazo podría causar daños al bebé; sin embargo, de estas mujeres el 70 por ciento reportaron que quisieran ser tratadas contra parásitos mientras que están embarazadas si estuvieran infectadas. Está claro que el conocimiento de parásitos y la anemia en la población estudiada estaba limitada. Los resultados de este estudio necesitan ser combinados con evidencia en la prevalencia y la severidad de la anemia e infecciones parasitarias junto con la información sobre la dieta y los factores de riesgos con el objetivo de desarrollar un efectivo programa prenatal de salud y nutrición.

Plasma homocysteine concentrations in a population of rhesus monkeys (Macaca mulatta): reference ranges and accompanying plasma concentrations of folate and vitamin B12.

The circulating level of homocysteine (Hcy) is recognized as a major independent risk factor for cardiovascular disease in humans. Nonhuman primates are being investigated to see whether they will be accurate models for Hcy indicators of cardiovascular dysfunction. Normal reference values are available in humans for Hcy as classified by age, gender, ethnic origin, and biological factors, however similar information in nonhuman primates had not been published previously. The purpose of this report is to provide normal Hcy values in a large group of nonhuman primates in light of age, gender, and physiologic state (pregnancy and lactation) and to compare these values to the same parameters in humans to highlight similar and dissimilar trends. In addition, plasma levels of folic acid and vitamin B(12), which are determinants of Hcy status in humans, are presented. Samples obtained from a troop of 149 rhesus monkeys (Macaca mulatta) fed a high protein commercial diet were analyzed for Hcy by using high-performance liquid chromatography. Folate and vitamin B(12) levels were determined by using an autoanalyzer. Results (mean [95% confidence interval]) for the entire troop were: Hcy, 4.5 (4.2-4.9) micromol/L; folic acid, 8.6 (8.0-9.1) micromol/L; and vitamin B(12), 673 (611-741) pmol/L. Quantitative values are similar to published values for another species of wild-caught macaques. Similar to trends noted for humans, male monkeys had higher Hcy values than did female animals, pregnant animals had lower values than did nonpregnant ones, and Hcy levels were inversely proportional to plasma folate and vitamin B(12) concentrations. However, homocysteine levels in rhesus monkeys did not vary consistently with age, whereas they increase with age in humans.

Dosificación de drogas hipertensinógenas en orina de pacientes hipertensos / Dosification of hipertensionogen drugs from the urine of hipertensive patients

Se presentan los resultados del estudio realizado en una gran Unidad de las FAR a 84 combatientes en los que se dosificó la presencia de drogas hipertensinógenas en la orina. Los pacientes se clasificaron en 4 grupos de acuerdo con las cifras tensionales y la presencia o no de alteraciones del pulso. Para la dosificación de las drogas se utilizó el método de cromatografía de capa fina. Se analizan las posibles causas de los hallazgos detectados y se hacen recomendaciones para la solución del problema. Se propone incluir la categoría de hipertensión ficticia (autoprovocada) a la actual clasificación de la entidad

Dosificación de drogas hipertensinógenas en orina de pacientes hipertensos

Se presentan los resultados del estudio realizado en una gran unidad de las FAR a 84 combatientes en los que se dosificó la presencia de drogas hipertensinógenas en la orina. Los pacientes se clasificaron en 4 grupos de acuerdo con las cifras tensionales y la presencia o no de alteraciones de pulso. Para la dosificación de las drogas se utilizó el método de cromatografía de capa fina. Se analizan las posibles causas de los hallazgos detectados y se hacen recomendaciones para la solución del problema. Se propone incluir la categoría de de hipertensión ficticia (provocada) a la actual clasificación de la entidad(AU)