PLANEACIÓN DIDÁCTICA GENERAL DE LA ASIGNATURA: ECONOMÍA

GUÍA DIDÁCTICA / PLANEACIÓN DIDÁCTICA (NMS

Association of VAV2 and VAV3 polymorphisms with cardiovascular risk factors

Hypertension, diabetes and obesity are cardiovascular risk factors closely associated to the development of renal and cardiovascular target organ damage. VAV2 and VAV3, members of the VAV family proto-oncogenes, are guanosine nucleotide exchange factors for the Rho and Rac GTPase family, which is related with cardiovascular homeostasis. We have analyzed the relationship between the presence of VAV2 rs602990 and VAV3 rs7528153 polymorphisms with cardiovascular risk factors and target organ damage (heart, vessels and kidney) in 411 subjects. Our results show that being carrier of the T allele in VAV2 rs602990 polymorphism is associated with an increased risk of obesity, reduced levels of ankle-brachial index and diastolic blood pressure and reduced retinal artery caliber. In addition, being carrier of T allele is associated with increased risk of target organ damage in males. On the other hand, being carrier of the T allele in VAV3 rs7528153 polymorphism is associated with a decreased susceptibility of developing a pathologic state composed by the presence of hypertension, diabetes, obesity or cardiovascular damage, and with an increased risk of developing altered basal glycaemia. This is the first report showing an association between VAV2 and VAV3 polymorphisms with cardiovascular risk factors and target organ damage

A population-based controlled experiment assessing the epidemiological impact of digital contact tracing

While Digital contact tracing (DCT) has been argued to be a valuable complement to manual tracing in the containment of COVID-19, no empirical evidence of its effectiveness is available to date. Here, we report the results of a 4-week population-based controlled experiment that took place in La Gomera (Canary Islands, Spain) between June and July 2020, where we assessed the epidemiological impact of the Spanish DCT app Radar Covid. After a substantial communication campaign, we estimate that at least 33% of the population adopted the technology and further showed relatively high adherence and compliance as well as a quick turnaround time. The app detects about 6.3 close-contacts per primary simulated infection, a significant percentage being contacts with strangers, although the spontaneous follow-up rate of these notified cases is low. Overall, these results provide experimental evidence of the potential usefulness of DCT during an epidemic outbreak in a real population

HISTORIA DE MÉXICO

GUÍA DIDÁCTICA / PLANEACIÓN DIDÁCTICA (NMS

Valorizing the Human Capital Within Organizations : A Competency Based Approach

Changes in the business environment and in the nature of work itself require the implementation of integrated and flexible methodologies in competencies’ definition in order to valorize the human capital and achieve organizational targets in a future-oriented perspective. However, extant research suggests that the available approaches to competency definition are more focused on describing past behaviors than on anticipating future requirements. Therefore, this study endeavors to provide a competency-based model that supports the top management in the identification of the competencies employees should posses, highlighting crucial competencies that can translate the strategy and vision of the organization into behaviors, skills, and terms that people can easily understand and implement. The results of our explorative case study led us to identify a set of competencies (digital/analytical/technical/adaptive/combinative/proactive), classified following the Knowledge Skills Attitudes (KSA) model, that collectively lead to a successful definition of future-oriented competencies.© 2019 Springer. This is a post-peer-review, pre-copyedit version of an article published in Advances in Human Factors, Business Management and Society. AHFE 2018. The final authenticated version is available online at: https://doi.org/10.1007/978-3-319-94709-9_6fi=vertaisarvioitu|en=peerReviewed

Identificación morfofisiologica de hongos en genotipos de maíz

El cultivo de maíz es la base de la alimentación para México, el objetivo de este trabajo fue identificar la micobiota en cuatro genotipos de maíz de Saltillo, Coahuila y cuatro de Tepalcingo, Morelos. Se realizó de acuerdo a la prueba papel secante y congelamiento, se tomaron 1000 semillas de maíz por cada genotipo, las cuales se desinfectaron con hipoclorito de sodio al 3 % (2 veces) y posteriormente se enjuagaron con agua destilada por 1 min. (2 veces). La siembra fue realizada en charolas de plástico 18.5 x 25 cm, sobre papel secante estéril previamente humedecido, las charolas se mantuvieron a temperatura ambiente de 26 °C ± 2 °C durante 11 días en la cámara bioclimática del Laboratorio de Fitopatología de la Universidad Autónoma Agraria Antonio Narro. Terminado el periodo de la incubación, se procedió a contar y aislar el número de las colonias de hongos por su color por repetición, para su posterior purificación e identificación, así como las semillas sanas, es decir, aquellas que no presentaron crecimiento de micelio y la incidencia reportándose como porcentaje de semilla colonizada, analizando los datos en el programa de la Universidad de Nuevo León versión 2.5. Se observó diferencia estadística entre la incidencia de hongos en los genotipos de maíz P>F 0.00, con un coeficiente de variación del 22.77 %, los genotipos de Tepalcingo con una media del 77.925% y Saltillo 87.725%, reportando por primera vez a Acremonium sp. en Saltillo, Coahuila y Tepalcingo, Morelos, México

ANTAGONISMO DE Trichoderma spp. EN HONGOS ASOCIADOS AL DAÑO DE Diatraea saccharalis Fabricius. (LEPIDOPTERA : CRAMBIDAE ) EN MAIZ

El objetivo del presente trabajo, fue evaluar in vitro, mediante cultivo dual la capacidad antagónica de las cepas de Trichoderma asperellum T11, Trichoderma harzianum T1 4, y Trichoderma longibrachiatum T1 40 sobre hongos asociados; Alternaria arborescens, Bipolaris shoemakeri, Bipolaris victoriae, Epicocum sorghinum, Exserohilum longirostratum, Fusarium brevicatenulatum, Penicillium polonicum, Phaeocytostroma ambiguum y Fusarium equiseti, el muestreo se realizó en Tepalcingo, Morelos en tallos de maíz y el experimento se estableció en el laboratorio de Fitopatología de la Universidad Autónoma Agraria Antonio Narro en el mes de abril de 2018. La evaluación se realizó bajo un diseño factorial AxB, con nueve niveles en A y tres en B con cuatro repeticiones por tratamiento, siendo A las cepas de hongos fitopatógenos y B las tres cepas de Trichoderma, se colocó en el extremo de la placa de Petri un explante de PDA con micelio de Trichoderma spp. de 5 mm de diámetro, y en el extremo opuesto un explante del hongo fitopatógeno, las siembras fueron incubadas a 25 ± 2 ºC por 120 h, evaluándose las medias del Porcentaje de Inhibición en el Crecimiento del Micelio (PICM) mediante la fórmula de Fakhrunnisa modificada y se determinó el área de desarrollo de los hongos mediante el software GeoGebra Classic versión 5.0.473.0-d, los resultados se analizaron con el programa (FAUANL) versión 2.5, mediante Tukey con nivel de significancia de 0.05, con efectos de antagonismo mayores del 67.74%

Automatic Selection of Molecular Descriptors using Random Forest: Application to Drug Discovery

The optimal selection of chemical features (molecular descriptors) is an essential pre-processing step for the efficient application of computational intelligence techniques in virtual screening for identification of bioactive molecules in drug discovery. The selection of molecular descriptors has key influence in the accuracy of affinity prediction. In order to improve this prediction, we examined a Random Forest (RF)-based approach to automatically select molecular descriptors of training data for ligands of kinases, nuclear hormone receptors, and other enzymes. The reduction of features to use during prediction dramatically reduces the computing time over existing approaches and consequently permits the exploration of much larger sets of experimental data. To test the validity of the method, we compared the results of our approach with the ones obtained using manual feature selection in our previous study (Perez-Sanchez et al., 2014). The main novelty of this work in the field of drug discovery is the use of RF in two different ways: feature ranking and dimensionality reduction, and classification using the automatically selected feature subset. Our RF-based method out-performs classification results provided by Support Vector Machine (SVM) and Neural Networks (NN) approaches

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