Efecto del enriquecimiento ambiental sobre la ansiedad: estudio experimental en un modelo animal

La ansiedad es unroblema de salud mundial que amerita el estudio de nuevas alternativas para su tratamiento terapéutico, entre ellas el enriquecimiento ambiental (EA). El presente trabajo estudió el efecto del EA sobre la ansiedad. 20 ratones C57BL/6 fueron divididos en 2 grupos: Grupo Control (GC, n=10) Grupo Experimental (GE, n=10). El GE recibió EA basado en estímulos sensoriales (olfativos, gustativos, visuales y táctiles) y nichos oscuros, mientras que el GC fue hospedado individualmente en un ambiente restringido de EA. Los ratones fueron evaluados en el Laberinto Elevado en Cruz (LEC), Caja Luz/Oscuridad (CLO) y mediante la prueba preferencia por sacarosa (PS). El EA causó hipermotilidad en LEC con preferencia por los desplazamientos entre brazos cerrados (p<0.05); aumentó el tiempo unitario de permanencia en ambos compartimientos de CLO (p<0.05) e incrementó significativamente el consumo de sacarosa en PS (p<0.05). En conclusión; la técnica del EA basado en incluir nichos oscuros induce hiperactividad con niveles de ansiedad disminuidos, permitiendo la expresión de conductas instintivas.

QUALITY AND MICROBIOLOGICAL SAFETY OF A DAILY MORTALITY COMPOST PRODUCED IN A VETERINARY FACULTY AT SÃO PAULO STATE

Introduction: Daily and outbursts mortality composting have been identified as one of the finest methods for final disposal of animal corpses, but the probable threat of pathogens transmission truly limits its use. Materials and Methods: In this study we evaluated the quality and microbiological biosafety of a compost produced in daily mortality experimental unit composting at the Universidade Estadual Paulista in the state of Sao Paulo, Brazil. Settled compost sample was evaluated in order to determine the presence and counting of coliforms and Salmonella sp. and the pathotypes of E. coli STEC, EPEC and EHEC using culture and molecular techniques. The occurrence of frequent soil borne phytopathogenic fungi was also estimated using selective and differential microbiological culture media. Results and Discussion: The occurrence of pathogenic E. coli, Salmonella sp and phytopathogenic fungi were negative. Coliforms level was 3.05 log10/g. Concussions: The results showed that daily mortality composting method is effective to reduce pathogenic microorganisms, however, in order to add the product on crops or plants such as vegetables that are for direct human consumption, additional tests must be performed to assess the presence of viral pathogens and endospores forming bacteria.

Supervised evolutionary learning: Use of gradient histogram & particle swarm algorithm to detection & tracking pedestrian in sequence of infrared images

Recently, tracking and pedestrian detection from various images have become one of the major issues in the field of image processing and statistical identification. In this regard, using evolutionary learning-based approaches to improve performance in different contexts can greatly influence the appropriate response. There are problems with pedestrian tracking/identification, such as low accuracy for detection, high processing time, and uncertainty in response to answers. Researchers are looking for new processing models that can accurately monitor one's position on the move. In this study, a hybrid algorithm for the automatic detection of pedestrian position is presented. It is worth noting that this method, contrary to the analysis of visible images, examines pedestrians' thermal and infrared components while walking and combines a neural network with maximum learning capability, wavelet kernel (Wavelet transform), and particle swarm optimization (PSO) to find parameters of learner model. Gradient histograms have a high effect on extracting features in infrared images. As well, the neural network algorithm can achieve its goal (pedestrian detection and tracking) by maximizing learning. The proposed method, despite the possibility of maximum learning, has a high speed in education, and results of various data sets in this field have been analyzed. The result indicates a negligible error in observing the infrared sequence of pedestrian movements, and it is suggested to use neural networks because of their precision and trying to boost the selection of their hyperparameters based on evolutionary algorithms.

Patrimonio paleontológico y colección de fósiles de la Universidad de Santander, Colombia

En Colombia el registro fósil hace parte del Patrimonio Paleontológico de la nación, permite explicar el origen, la diversidad y distribución de diferentes formas de vida. El Servicio Geológico Colombiano se encarga de conservar y custodiar el Patrimonio Geológico y Paleontológico de la nación y es quien realiza el Inventario Nacional Geológico y Paleontológico. Con el presente trabajo se pretende realizar una breve descripción de las normatividades y procesos que rigen la colecta y mantenimiento de estos especímenes, así como dar a conocer los primeros registros fósiles con los que cuenta la Universidad de Santander que forman parte de la colección geológica de esta institución. La Universidad de Santander realiza su aporte a este patrimonio con el registro de 25 especímenes fósiles colectados en salidas de campo de los cursos de Biología y Paleontología del programa de Geología en las formaciones Rosablanca y Tibú-Mercedes de los municipios de Zapatoca y San Andrés (Santander) del periodo Cretácico inferior y de la Formación Los Pinos en el municipio de Samacá (Boyacá) del periodo Cretácico superior.

Using neural network for drowsiness detection based on EEG signals and optimization in the selection of its features using genetic algorithm

One of the major causes of traffic accidents is driver’s drowsiness. For this reason, detecting whether the driver's eyes are open or closed is one of the critical factors in reducing road deaths. One way to detect whether your eyes are open or closed is to use EEG signals. EEG signals are obtained from the recording of electrical activity in the human brain. The present study uses a neural network that is applied to the driver's EEG signals to detect whether the eye is open or closed. The data of the EEG signals used in this paper consist of 14 features that are based on a statistical population of 600 people. Various neural network algorithms have been implemented for clustering these data into two classes of open or closed eyes, which are described in this paper. Perceptron neural network and radial base neural network (RBF) are two types of networks used in this paper. Also, in order to improve the execution speed and reduce the occupied space of the microcontroller, the genetic algorithm method has been used to optimize the fitting function of Fisher’s discriminant rate, in which the optimized function provides better results in the less occupied time and space.

Fusión, clasificación y tracción simultáneas de obstáculos en movimiento mediante LIDAR y cámara usando algoritmo bayesiano

In the near future, preventing collisions with fixed or moving, alive, and inanimate obstacles will appear to be a severe challenge due to the increased use of Unmanned Ground Vehicles (UGVs). Light Detection and Ranging (LIDAR) sensors and cameras are usually used in UGV to detect obstacles. The definite tracing and classification of moving obstacles is a significant dimension in developed driver assistance systems. It is believed that the perceived model of the situation can be improved by incorporating the obstacle classification. The present study indicated a multi-hypotheses monitoring and classifying approach, which allows solving ambiguities rising with the last methods of associating and classifying targets and tracks in a highly volatile vehicular situation. This method was tested through real data from various driving scenarios and focusing on two obstacles of interest vehicle, pedestrian.

Using different types of neural networks in detection the body's readiness for blood donation and determining the value of each of its parameters using genetic algorithm

Blood transfusion is of great importance in the medical field, as blood donation centers are responsible for collecting and distributing blood and blood products. Artificial neural network is a data processing system getting ideas from the human brain and designs a data structure that acts like a neuron using programming science and by creating a network between these neurons and combining large amounts of data with smart algorithms and their rapid processing, the network is trained. In this study, data is extracted from the blood transfusion service center and the perceptron neural network, RBF neural network, Fisher’s discrimination ratio and genetic algorithm were examined, and finally the highest possible accuracy from the neural network was achieved.

Experimental and numerical study of upstream slope stability in an earth dam reservoir under rapid drawdown conditions

The rapid ‎drawdown of the dam reservoir is one of the most common situations occurring in the lifetime of a dam. For this reason, one of the main factors in the design of the upstream slope is the rapid drainage of the reservoir. In this case, the upstream slope is in a critical condition and the slope may be unstable. When the water surface in the reservoir is drawdown suddenly, the water level in the dam body does not decrease at the same time as the reservoir water level. The analysis of seepage from the earth dam body and calculation of the water loss play an important role in calculating the amount of pore water pressure, and, consequently, the stability analysis of the dam body. In addition, any seepage analysis is dependent on the hydraulic properties of the dam materials. In order to investigate the effect of hydraulic conductivity on the rapid drawdown of water level and the seepage, an experimental model was constructed of an earth dam. By accurate measurement of hydraulic parameters of the materials in saturated and unsaturated media, the flow through this model was modeled using a disk penetrometer by seep/w software. The results were then compared with the observed data.

Distribución espacial de Exogyra boussingaulti de la formación Tibú-Mercedes (Cretácico: Aptiano-Albiano) en el Municipio de San Andrés (Santander: Colombia)

Exogyra boussingaulti fue un bivalvo reclinante de sedimentos blandos, encontrado en la formación Tibú-Mercedes del Cretácico inferior en el Municipio de San Andrés (Santander: Colombia). Sobre la ecología de E. boussingaulti se conoce muy poco en aspectos de reproducción, dispersión o depredación, por esta razón, se determinó la distribución espacial de esta especie mediante el cálculo de tres índices de dispersión a partir de los datos obtenidos de una cuadricula elaborada en un bloque rodado de 2 m2. Con base en los resultados, fue posible inferir que E. boussingaulti presentaba una distribución en parches no concentrados determinados por su ciclo reproductivo y limitados por la competencia por alimento a nivel intraespecífico. De igual forma este artículo se presenta como una estrategia de enseñanza que contribuye con el proceso educativo de los estudiantes de ciencias geológicas y con la solución a preguntas pequeñas en paleobiología.

Polyester fabric modification through fabrication of nano-copper compounds

This research introduces a new method of polyester fabric surface modification in order to achieve distinctive features. The copper sulfate, sodium hydroxide and cetyl trimethylammonium bromide (CTAB) were used to synthesize copper nanoparticles, and loaded on the polyester fabric surface. The optimal sample was considered by SEM-EDX, FT-IR and XRD devices. SEM images showed copper nanoparticles in shape of nano-plates with 150 to 600 nm in length and thickness of about 30 nm. The best results obtained on the modified fabric processed at boil for 120 min. According to the results of experiments CuO and Cu0 were synthesized on the fabric.