Dynamic glossary en la comprensión lectora en inglés en estudiantes de una institución educativa primaria, Lima - 2014

La comprensión lectora es una de las competencias curriculares que genera mayor preocupación en el país debido a los bajos resultados que obtienen los estudiantes peruanos en las pruebas de rendimiento nacionales e internacionales. La literatura especializada destaca la estrecha vinculación del vocabulario con el desarrollo de la comprensión lectora. Esta investigación tuvo como propósito determinar de qué manera la aplicación del Dynamic glossary mejora significativamente la comprensión lectora en el idioma inglés en los estudiantes del 5to grado de la institución educativa privada San Agustín, distrito de San Isidro, Lima – 2014. La investigación es cuasi experimental cuya muestra estuvo constituida por 32 estudiantes de quinto grado”A” de educación primaria entre 10 y 11 años de edad, de los cuales 14 estudiantes son mujeres y 18 estudiantes son varones. Para evaluar la comprensión lectora se empleó la Prueba Diagnóstica y la Prueba Final respectivamente elaboradas por las autoras que evalúan la comprensión lectora en los tres niveles: literal, inferencial y crítico. En la investigación se encontró que existe diferencia significativa entre el nivel de comprensión lectora general en el idioma inglés del grupo experimental y del grupo de control

A comparison of back propagation and Generalized Regression Neural Networks performance in neutron spectrometry

The process of unfolding the neutron energy spectrum has been subject of research for many years. Monte Carlo, iterative methods, the bayesian theory, the principle of maximum entropy are some of the methods used. The drawbacks associated with traditional unfolding procedures have motivated the research of complementary approaches. Back Propagation Neural Networks (BPNN), have been applied with success in neutron spectrometry and dosimetry domains, however, the structure and learning parameters are factors that highly impact in the networks performance. In ANN domain, Generalized Regression Neural Network (GRNN) is one of the simplest neural networks in term of network architecture and learning algorithm. The learning is instantaneous, requiring no time for training. Opposite to BPNN, a GRNN would be formed instantly with just a 1-pass training on the development data. In the network development phase, the only hurdle is to optimize the hyper-parameter, which is known as sigma, governing the smoothness of the network. The aim of this work was to compare the performance of BPNN and GRNN in the solution of the neutron spectrometry problem. From results obtained it can be observed that despite the very similar results, GRNN performs better than BPNN

Performance of artificial neural networks and genetical evolved artificial neural networks unfolding techniques

With the Bonner spheres spectrometer neutron spectrum is obtained through an unfolding procedure. Monte Carlo methods, Regularization, Parametrization, Least-squares, and Maximum Entropy are some of the techniques utilized for unfolding. In the last decade methods based on Artificial Intelligence Technology have been used. Approaches based on Genetic Algorithms and Artificial Neural Networks have been developed in order to overcome the drawbacks of previous techniques. Nevertheless the advantages of Artificial Neural Networks still it has some drawbacks mainly in the design process of the network, vg the optimum selection of the architectural and learning ANN parameters. In recent years the use of hybrid technologies, combining Artificial Neural Networks and Genetic Algorithms, has been utilized to. In this work, several ANN topologies were trained and tested using Artificial Neural Networks and Genetically Evolved Artificial Neural Networks in the aim to unfold neutron spectra using the count rates of a Bonner sphere spectrometer. Here, a comparative study of both procedures has been carried out

Neutron spectrometry using artificial neural networks for a bonner sphere spectrometer with 3He detector

Neutron spectra unfolding and dose equivalent calculation are complicated tasks in radiation protection, are highly dependent of the neutron energy, and a precise knowledge on neutron spectrometry is essential for all dosimetry-related studies as well as many nuclear physics experiments. In previous works have been reported neutron spectrometry and dosimetry results, by using the ANN technology as alternative solution, starting from the count rates of a Bonner spheres system with a LiI(Eu) thermal neutrons detector, 7 polyethylene spheres and the UTA4 response matrix with 31 energy bins. In this work, an ANN was designed and optimized by using the RDANN methodology for the Bonner spheres system used at CIEMAT Spain, which is composed of a He neutron detector, 12 moderator spheres and a response matrix for 72 energy bins. For the ANN design process a neutrons spectra catalogue compiled by the IAEA was used. From this compilation, the neutrons spectra were converted from lethargy to energy spectra. Then, the resulting energy ?uence spectra were re-binned by using the MCNP code to the corresponding energy bins of the He response matrix before mentioned. With the response matrix and the re-binned spectra the counts rate of the Bonner spheres system were calculated and the resulting re-binned neutrons spectra and calculated counts rate were used as the ANN training data set

Induction of Lysosome Membrane Permeabilization as a Therapeutic Strategy to Target Pancreatic Cancer Stem Cells

Despite significant efforts to improve pancreatic ductal adenocarcinoma (PDAC) clinical outcomes, overall survival remains dismal. The poor response to current therapies is partly due to the existence of pancreatic cancer stem cells (PaCSCs), which are efficient drivers of PDAC tumorigenesis, metastasis and relapse. To find new therapeutic agents that could efficiently kill PaCSCs, we screened a chemical library of 680 compounds for candidate small molecules with anti-CSC activity, and identified two compounds of a specific chemical series with potent activity in vitro and in vivo against patient-derived xenograft (PDX) cultures. The anti-CSC mechanism of action of this specific chemical series was found to rely on induction of lysosomal membrane permeabilization (LMP), which is likely associated with the increased lysosomal mass observed in PaCSCs. Using the well characterized LMP-inducer siramesine as a tool molecule, we show elimination of the PaCSC population in mice implanted with tumors from two PDX models. Collectively, our approach identified lysosomal disruption as a promising anti-CSC therapeutic strategy for PDAC

Conocimientos y habilidades sobre lactancia materna, 2006

An intervention study was made to 30 pregnant women from 10 doctor´s office from polyclinic Jimmy Hirtzel, Bayamo municipality, Granma province to determine knowledge and abilities about breastfeeding before, and after of applying an educative program. A survey was applied with different variables: age, intelectual level and occupation. It was evaluated knowledge and abilities using a written exam, then there was applied an educative program created for this purpose and finally a verifying exam. There were used as measures absolute numbers and per cent, together with chi squared to related samples with a significance level of α = 0.05. Before the application of the program prevailed pregnant women evaluated with bad qualification in 53.3% in the group from 20 to 34 years old. After the intervention all pregnant women (100%) had acquired good Knowledge and abilities. All pregnant women received information from their own physician during pregnancy.Se realizó un estudio de intervención a 30 embarazadas de 10 consultorios médicos del Policlínico “Jimmy Hirtzel”, con el objetivo de determinar los conocimientos y habilidades sobre lactancia materna de las mismas, antes y después de aplicado un programa educativo. Se les llenó un cuestionario donde se recogieron las variables: edad, nivel intelectual y ocupación; se les evaluó sus conocimientos y habilidades sobre lactancia materna a través de un examen escrito; luego se les aplicó un programa educativo elaborado al efecto, y finalmente, un examen comprobatorio de los conocimientos y habilidades adquiridos. Se utilizaron como medidas de resumen los números absolutos y por ciento y se analizaron las pruebas de Chi Cuadrado para muestras relacionadas con un nivel de significación (α = 0.05). Se concluyó que antes de aplicar el programa educativo predominaron las embarazadas evaluadas de mal en un 53.3%; las del grupo de 20 a 34 años. Después de la intervención, el 100% había adquirido buenos conocimientos y habilidades. Las 30 embarazadas recibieron la mayor información de su médico de familia, durante el embarazo

A neutron spectrum unfolding code based on generalized regression artificial neural networks

The most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. Novel methods based on Artificial Neural Networks have been widely investigated. In prior works, back propagation neural networks (BPNN) have been used to solve the neutron spectrometry problem, however, some drawbacks still exist using this kind of neural nets, i.e. the optimum selection of the network topology and the long training time. Compared to BPNN, it's usually much faster to train a generalized regression neural network (GRNN). That's mainly because spread constant is the only parameter used in GRNN. Another feature is that the network will converge to a global minimum, provided that the optimal values of spread has been determined and that the dataset adequately represents the problem space. In addition, GRNN are often more accurate than BPNN in the prediction. These characteristics make GRNNs to be of great interest in the neutron spectrometry domain. This work presents a computational tool based on GRNN capable to solve the neutron spectrometry problem. This computational code, automates the pre-processing, training and testing stages using a k-fold cross validation of 3 folds, the statistical analysis and the post-processing of the information, using 7 Bonner spheres rate counts as only entrance data. The code was designed for a Bonner Spheres System based on a LiI(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation

Generalized Regression Neural Networks with Application in Neutron Spectrometry

The aim of this research was to apply a generalized regression neural network (GRNN) to predict neutron spectrum using the rates count coming from a Bonner spheres system as the only piece of information. In the training and testing stages, a data set of 251 different types of neutron spectra, taken from the International Atomic Energy Agency compilation, were used. Fifty-one predicted spectra were analyzed at testing stage. Training and testing of GRNN were carried out in the MATLAB environment by means of a scientific and technological tool designed based on GRNN technology, which is capable of solving the neutron spectrometry problem with high performance and generalization capability. This computational tool automates the pre-processing of information, the training and testing stages, the statistical analysis, and the post-processing of the information. In this work, the performance of feed-forward backpropagation neural networks (FFBPNN) and GRNN was compared in the solution of the neutron spectrometry problem. From the results obtained, it can be observed that despite very similar results, GRNN performs better than FFBPNN because the former could be used as an alternative procedure in neutron spectrum unfolding methodologies with high performance and accuracy

Effectiveness of a cognitive behavioral intervention in patients with medically unexplained symptoms: cluster randomized trial

BACKGROUND: Medically unexplained symptoms are an important mental health problem in primary care and generate a high cost in health services.Cognitive behavioral therapy and psychodynamic therapy have proven effective in these patients. However, there are few studies on the effectiveness of psychosocial interventions by primary health care. The project aims to determine whether a cognitive-behavioral group intervention in patients with medically unexplained symptoms, is more effective than routine clinical practice to improve the quality of life measured by the SF-12 questionary at 12 month. METHODS/DESIGN: This study involves a community based cluster randomized trial in primary healthcare centres in Madrid (Spain). The number of patients required is 242 (121 in each arm), all between 18 and 65 of age with medically unexplained symptoms that had seeked medical attention in primary care at least 10 times during the previous year. The main outcome variable is the quality of life measured by the SF-12 questionnaire on Mental Healthcare. Secondary outcome variables include number of consultations, number of drug (prescriptions) and number of days of sick leave together with other prognosis and descriptive variables. Main effectiveness will be analyzed by comparing the percentage of patients that improve at least 4 points on the SF-12 questionnaire between intervention and control groups at 12 months. All statistical tests will be performed with intention to treat. Logistic regression with random effects will be used to adjust for prognostic factors. Confounding factors or factors that might alter the effect recorded will be taken into account in this analysis. DISCUSSION: This study aims to provide more insight to address medically unexplained symptoms, highly prevalent in primary care, from a quantitative methodology. It involves intervention group conducted by previously trained nursing staff to diminish the progression to the chronicity of the symptoms, improve quality of life, and reduce frequency of medical consultations. TRIAL REGISTRATION: The trial was registered with ClinicalTrials.gov, number NCT01484223 [http://ClinicalTrials.gov].S

Association Between Preexisting Versus Newly Identified Atrial Fibrillation and Outcomes of Patients With Acute Pulmonary Embolism

Background Atrial fibrillation (AF) may exist before or occur early in the course of pulmonary embolism (PE). We determined the PE outcomes based on the presence and timing of AF. Methods and Results Using the data from a multicenter PE registry, we identified 3 groups: (1) those with preexisting AF, (2) patients with new AF within 2 days from acute PE (incident AF), and (3) patients without AF. We assessed the 90-day and 1-year risk of mortality and stroke in patients with AF, compared with those without AF (reference group). Among 16 497 patients with PE, 792 had preexisting AF. These patients had increased odds of 90-day all-cause (odds ratio [OR], 2.81; 95% CI, 2.33-3.38) and PE-related mortality (OR, 2.38; 95% CI, 1.37-4.14) and increased 1-year hazard for ischemic stroke (hazard ratio, 5.48; 95% CI, 3.10-9.69) compared with those without AF. After multivariable adjustment, preexisting AF was associated with significantly increased odds of all-cause mortality (OR, 1.91; 95% CI, 1.57-2.32) but not PE-related mortality (OR, 1.50; 95% CI, 0.85-2.66). Among 16 497 patients with PE, 445 developed new incident AF within 2 days of acute PE. Incident AF was associated with increased odds of 90-day all-cause (OR, 2.28; 95% CI, 1.75-2.97) and PE-related (OR, 3.64; 95% CI, 2.01-6.59) mortality but not stroke. Findings were similar in multivariable analyses. Conclusions In patients with acute symptomatic PE, both preexisting AF and incident AF predict adverse clinical outcomes. The type of adverse outcomes may differ depending on the timing of AF onset.info:eu-repo/semantics/publishedVersio