Computer Science Engineering

En busca de respuestas y haciendo investigación nos encontramos que nuestra realidad educativa está inmersa en múltiples problemas dentro y fuera de las aulas de clase (lecturas de sociología UPN-2,000), donde todos y todas los que intervienen en el proceso de enseñanza-aprendizaje tenemos un compromiso para mejorar la calidad de la educación, como docente sé que puedo contribuir con un granito de arena ayudando y comprometiéndome con cada uno de mis alumnos y para ello hago lo siguiente: 1. Hacer un diagnóstico, 2. Elaborar un plan de intervención Basado en la "Pedagogía de la Ternura" tomando en primer momento este concepto de Restrepo 2000. El cual hace referencia a:

Much of the HIV (Human Immunodeficiency Virus) success is due to its evolving capabilities. Understanding viral evolution and its relation to pharmacology is of utmost importance in fighting diseases caused by the HIV. Although the mutations conferring drug resistance are mostly known, the dynamics of the appearance chain of those mutations remains poorly understood. Here we apply a Temporal Nodes Bayesian Network (TNBN) to data extracted from the HIV Stanford database to explore the probabilistic relationships between mutations and antiretrovirals. We aim to unveil existing mutation networks and establish their probabilistic formation sequence. The model predictive accuracy is evaluated in terms of relative Brier score, relative time error and total number of intervals. Robustness of the model is hinted by consistency between two model instances. The learned models capture known relationships, qualitatively providing some measure of validity. Finally, two previously unseen mutational networks are retrieved and their probabilistic temporal sequentiation uncovered. We demonstrate the usefulness of TNBN for studying drug-mutation and mutation-mutation networks and expect to impact the combat against HIV infection by facilitating better treatment planning.

Objective: The human immunodeficiency virus (HIV) is one of the fastest evolving organisms in the planet. Its remarkable variation capability makes HIV able to escape from multiple evolutionary forces naturally or artificially acting on it, through the development and selection of adaptive mutations. Although most drug resistance mutations have been well identified, the dynamics and temporal patterns of appearance of these mutations can still be further explored. The use of models to predict mutational pathways as well as temporal patterns of appearance of adaptive mutations could greatly benefit clinical management of individuals under antiretroviral therapy. Methods and Material: We apply a temporal nodes Bayesian network (TNBN) model to data extracted from the Stanford HIV drug resistance database in order to explore the probabilistic relationships between drug resistance mutations and antiretroviral drugs unveiling possible mutational pathways and establishing their probabilistic-temporal sequence of appearance.

Evolution is an important aspect of viral diseases such as influenza, hepatitis and the human immunodeficiency virus (HIV). This evolution impacts the development of successful vaccines and antiviral drugs, as mutations increase drug resistance. Although mutations providing drug resistance are mostly known, the dynamics of the occurrence of those mutations remains poorly understood. A common graphical model to handle temporal information are Dynamic Bayesian Networks. However, other options to address this problem exist. This is the case of Temporal Nodes Bayesian Networks. In this paper we used both approaches for modeling the relationships between antiretroviral drugs and HIV mutations, in order to analyze temporal occurrence of specific mutations in HIV that may lead to drug resistance. We compare the strengths and limitations of each of these two temporal approaches for this particular problem and show that the obtained models were able to capture some mutational pathways already known (obtained by clinical experimentation).

Much of the HIV (Human Immunodeciency Virus) success is due to its fast evolution which makes it drug ressistant. Understanding viral evolution and its relation to pharmacology is of utmost importance in ghting diseases caused by the HIV. Although the mutations conferring drug resistance are mostly known, the dynamics of the appearance chain of those mutations remains poorly understood. Here, we apply a Temporal Nodes Bayesian Network (TNBN) to data extracted from the HIV Stanford database to explore the probabilistic relationships between mutations and antiretrovirals. We aim to unveil existing mutation networks and establish their probabilistic formation sequence. The model predictive accuracy is evaluated in terms of the relative Brier score, relative temporal error, and total number of intervals. We developed models for the protease, an important protein of the HIV. The learned models captured well known relationships, which qualitatively provide a validity measure. In our exper...

Evolution is an important aspect of viral diseases such as influenza, hepatitis and the human immunodeficiency virus (HIV). This evolution impacts the development of successful vaccines and antiviral drugs, as mutations increase drug resistance. Although mutations providing drug resistance are mostly known, the dynamics of the occurrence of those mutations remains poorly understood. A common graphical model to handle temporal information are Dynamic Bayesian Networks. However, other options to address this problem exist. This is the case of Temporal Nodes Bayesian Networks. In this paper we used both approaches for modeling the relationships between antiretroviral drugs and HIV mutations, in order to analyze temporal occurrence of specific mutations in HIV that may lead to drug resistance. We compare the strengths and limitations of each of these two temporal approaches for this particular problem and show that the obtained models were able to capture some mutational pathways alread...

Much of the HIV (Human Immunodeficiency Virus) success is due to its evolving capabilities. Understanding viral evolution and its relation to pharmacology is of utmost importance in fighting diseases caused by the HIV. Although the mutations conferring drug resistance are mostly known, the dynamics of the appearance chain of those mutations remains poorly understood. Here we apply a Temporal Nodes Bayesian Network (TNBN) to data extracted from the HIV Stanford database to explore the probabilistic relationships between mutations and antiretrovirals. We aim to unveil existing mutation networks and establish their probabilistic formation sequence. The model predictive accuracy is evaluated in terms of relative Brier score, relative time error and total number of intervals. Robustness of the model is hinted by consistency between two model instances. The learned models capture known relationships, qualitatively providing some measure of validity. Finally, two previously unseen mutational networks are retrieved and their probabilistic temporal sequentiation uncovered. We demonstrate the usefulness of TNBN for studying drug-mutation and mutation-mutation networks and expect to impact the combat against HIV infection by facilitating better treatment planning.

Objective: The human immunodeficiency virus (HIV) is one of the fastest evolving organisms in the planet. Its remarkable variation capability makes HIV able to escape from multiple evolutionary forces naturally or artificially acting on it, through the development and selection of adaptive mutations. Although most drug resistance mutations have been well identified, the dynamics and temporal patterns of appearance of these mutations can still be further explored. The use of models to predict mutational pathways as well as temporal patterns of appearance of adaptive mutations could greatly benefit clinical management of individuals under antiretroviral therapy. Methods and Material: We apply a temporal nodes Bayesian network (TNBN) model to data extracted from the Stanford HIV drug resistance database in order to explore the probabilistic relationships between drug resistance mutations and antiretroviral drugs unveiling possible mutational pathways and establishing their probabilistic-temporal sequence of appearance.