Effect of the RGB Wavelengths of LED Light on Growth Rates of Nile Tilapia Fry in Biofloc Technology (BFT) Systems

This research evaluates the effect of wavelengths of the light on growth rates of Nile tilapia fry in the order of improving sustainability in aquaculture production. For this purpose, four tanks of water with tilapias were studied. Three tanks were illuminated with LED lamps each one with monochromatic peak wavelengths (): Blue light (BL) tank with = 451.67 nm, Green light (GL) with = 513.33 nm and Red light (RL) tank with = 627.27 nm. All tanks were illuminated with a light intensity of 0.832 ⁄2, and they had a photoperiod of 18L:6D throughout the study. Besides, the fourth tank was illuminated only by Natural light (NL) tank, which had the function of witness tank. Each treatment included the fourth, were randomly assigned to 150L tanks that were stocked with 122 Nile tilapia fry. The Nile tilapia fry had an initial average weight of 0.24 ± 0.01 , and were grown for 73 days. The average final weight for BL, GL, RL and NL treatments were 15.54 g, 16.84 g, 17.27 g and 16.22 g, respectively. The results suggest that Nile tilapia fry was positively influenced by the red light wavelength, which was represented in the greatest mass gain

Effects of Colored Light on Growth and Nutritional Composition of Tilapia, and Biofloc as a Food Source

Light stimulation and biofloc technology can be combined to improve the efficiency and sustainability of tilapia production. A 73-day pilot experiment was conducted to investigate the effect of colored light on growth rates and nutritional composition of the Nile tilapia fingerlings (Oreochromis niloticus) in biofloc systems. The effect of colored light on the nutritional composition of bioflocs as a food source for fish was measured. Three groups were illuminated in addition to natural sunlight with colored light using RGB light emitting diodes (LEDs) with peak wavelengths ( ) of 627.27 nm for red (R), 513.33 nm for green (G), and 451.67 nm for blue (B) light. LED light intensity was constant (0.832 mW/cm2), and had an 18-h photoperiod of light per day throughout the study. The control group was illuminated only with natural sunlight (natural). Tilapia had an average initial weight of 0.242 g. There was a significant effect of colored light on tilapia growth and composition. The R group showed the best growth rate, highest survival, and highest lipid content. The B group showed homogeneous growth with the lowest growth rate and lipid content, but the highest protein level. On the other hand, the biofloc composition was influenced by the green light in the highest content of lipids, protein, and nitrogen-free extract

Características microbiológicas de infecciones en pacientes pediátricos con cáncer del eje cafetero colombiano, 2014-2016

Objetivo: Describir características microbiológicas de las infecciones en pacientes pediátricos con cáncer en un centro de referencia del Eje Cafetero Colombiano 2014 – 2016. Materiales y métodos. Estudio descriptivo, corte transversal de pacientes oncológicos menores de 18 años hospitalizados en una institución de referencia en Colombia; los cuales hayan presentado una infección y se les haya realizado un cultivo con antibiograma. La ficha de recolección validada por expertos es reportada a través de WHONET 5.6. Análisis estadísticos descriptivos fueron realizados con STATA 14.2, versión oficial. Resultados. 2691 cultivos de 596 pacientes fueron incluidos. 53,86% correspondió a sexo masculino, la edad media fue de 8,86 años. El 76,89% de los cultivos se realizaron en el área de hospitalización. El 74,36% de los cultivos fueron de sangre, con un 22,04% de reportes positivos. La segunda muestra con mayor número de cultivos fue orina con 16,16%, con 25,75% de cultivos positivos. El mayor porcentaje de cultivos positivos fue para heces y biopsias con 71,21% y 53,13%, respectivamente. Se presentó una tasa de positividad global del 25.75%. Los tres principales microorganismos aislados fueron Gram negativos. Los microorganismos de mayor aislamiento fueron K. pneumoniae (24,4%), E. coli (21,06%) y P. aeruginosa (17,46%). Conclusión. Se presenta un patrón de infecciones similar al reportado en adultos, siendo los Gram negativos los más comunes. Recomendamos establecer políticas de dispensación de antibióticos y mejorar la vigilancia epidemiológica.Objective: To describe microbiological characteristics of infections in pediatric patients with cancer in a reference center of the Colombian coffee region, 2014-2016. Materials and methods. Cross-sectional, descriptive study including underaged oncological inpatients managed in a reference institution in Colombia; who have been dated an infection with a subsequent antibiogram done. The collection sheet validated by experts is reported through WHONET 5.6. Descriptive statistical analyzes were performed with STATA 14.2, official version. Results. 2691 cultures from 596 patients were included. 53.86% were male, the mean age was 8.86 years. 76.89% of the cultures were performed in the hospitalization area. 74.36% of the cultures were blood, with 22.04% positive reports. The second sample with the highest number of cultures was urine with 16.16%, with 25.75% positive cultures. The highest percentage of positive cultures was for feces and biopsies with 71.21% and 53.13%, respectively. An overall positivity rate of 25.75% was presented. The three main microorganisms isolated were Gram negative. The most isolated microorganisms were K. pneumoniae (24.4%), E. coli (21.06%) and P. aeruginosa (17.46%). Conclusion. Infection pattern similar to the one reported in adults, being the Gram-negatives the most prevalent agents. We recommend establishing antibiotic dispensing policies thus achieving microbiological risk control and improving epidemiological surveillance

Digital Holographic Interferometry without Phase Unwrapping by a Convolutional Neural Network for Concentration Measurements in Liquid Samples

Convolutional neural networks (CNNs) and digital holographic interferometry (DHI) can be combined to improve the calculation efficiency and to simplify the procedures of many DHI applications. In DHI, for the measurements of concentration differences between liquid samples, two or more holograms are compared to find the difference phases among them, and then to estimate the concentration values. However, liquid samples with high concentration difference values are difficult to calculate using common phase unwrapping methods as they have high spatial frequencies. In this research, a new method to skip the phase unwrapping process in DHI, based on CNNs, is proposed. For this, images acquired by Guerrero-Mendez et al. (Metrology and Measurement Systems 24, 19–26, 2017) were used to train the CNN, and a multiple linear regression algorithm was fitted to estimate the concentration values for liquid samples. In addition, new images were recorded to evaluate the performance of the proposed method. The proposed method reached an accuracy of 0.0731%, and a precision of ±0.0645. The data demonstrated a high repeatability of 0.9986, with an operational range from 0.25 gL−1 to 1.5 gL−1. The proposed method was performed with liquid samples in a cylindrical glass

Convolutional Neural Network for Measurement of Suspended Solids and Turbidity

The great potential of the convolutional neural networks (CNNs) provides novel and alternative ways to monitor important parameters with high accuracy. In this study, we developed a soft sensor model for dynamic processes based on a CNN for the measurement of suspended solids and turbidity from a single image of the liquid sample to be measured by using a commercial smartphone camera (Android or IOS system) and light-emitting diode (LED) illumination. For this, an image dataset of liquid samples illuminated with white, red, green, and blue LED light was taken and used to train a CNN and fit a multiple linear regression (MLR) by using different color lighting, we evaluated which color gives more accurate information about the concentration of suspended particles in the sample. We implemented a pre-trained AlexNet model, and an MLR to estimate total suspended solids (TSS), and turbidity values in liquid samples based on suspended particles. The proposed technique obtained high goodness of fit (R2 = 0.99). The best performance was achieved using white light, with an accuracy of 98.24% and 97.20% for TSS and turbidity, respectively, with an operational range of 0–800 mgL−1, and 0–306 NTU. This system was designed for aquaculture environments and tested with both commercial fish feed and paprika. This motivates further research with different aquatic environments such as river water, domestic and industrial wastewater, and potable water, among others

Convolutional Neural Network for Measurement of Suspended Solids and Turbidity

The great potential of the convolutional neural networks (CNNs) provides novel and alternative ways to monitor important parameters with high accuracy. In this study, we developed a soft sensor model for dynamic processes based on a CNN for the measurement of suspended solids and turbidity from a single image of the liquid sample to be measured by using a commercial smartphone camera (Android or IOS system) and light-emitting diode (LED) illumination. For this, an image dataset of liquid samples illuminated with white, red, green, and blue LED light was taken and used to train a CNN and fit a multiple linear regression (MLR) by using different color lighting, we evaluated which color gives more accurate information about the concentration of suspended particles in the sample. We implemented a pre-trained AlexNet model, and an MLR to estimate total suspended solids (TSS), and turbidity values in liquid samples based on suspended particles. The proposed technique obtained high goodness of fit (R2 = 0.99). The best performance was achieved using white light, with an accuracy of 98.24% and 97.20% for TSS and turbidity, respectively, with an operational range of 0–800 mgL−1, and 0–306 NTU. This system was designed for aquaculture environments and tested with both commercial fish feed and paprika. This motivates further research with different aquatic environments such as river water, domestic and industrial wastewater, and potable water, among others