Evaluacion de una solución de virtualización de los servidores de la red de datos del GADM del cantón Riobamba.

El presente trabajo de titulación tuvo como objetivo la evaluación de una solución de virtualización para los servidores del Gobierno Autónomo Descentralizado Municipal del Cantón Riobamba (GADMR). En primer lugar, se estudiaron los conceptos de virtualización, tipos y técnicas, además de algunos hipervisores que permitieron realizar un análisis previo. A continuación, se realizó un análisis de los servidores ubicados en el Data Center y los usuarios que tienen acceso a ellos, se determinó que los servidores a virtualizar son los de correo bajo el sistema operativo CentOS y, antivirus ESET NOD32 bajo el sistema operativo Windows Server debido a los requerimientos presentados en los mismos. Se realizó una comparación entre las herramientas VMware, Máquina Virtual Basada en el Núcleo (KVM) y Xen; de acuerdo a las características y los requerimientos planteados por el GADMR se escogió la herramienta VMware para posteriormente implementar un escenario de pruebas que constó de un servidor instalado previamente VMware vSphere donde se virtualizaron los servicios antes mencionados. Para la realización de pruebas se utilizó la herramienta JMeter de Apache para cargar, analizar y medir el desempeño de los servidores virtualizados la misma que permitió generar una variedad de reportes acorde a las necesidades. La muestra utilizada para las pruebas tuvo un 95% de fiabilidad y un margen de error de un 5%. Con los resultados de las pruebas y el análisis de los parámetros correspondientes se concluye que la herramienta óptima para su implementación es VMware vSphere por las funcionalidades que brinda, facilita su administración consolidando servidores, minimiza recursos, reduce espacio físico y principalmente cumple con los requerimientos del Departamento de Gestión de Tecnologías de la Información. Se recomienda adquirir un servidor con mayores características y prestaciones para requerimientos que se podrían presentar a futuro.This present paper aims to evaluate the virtualization solution for Decentralized Autonomous Government of Riobamba Municipality (GADMR). The concepts, kinds and techniques of virtualization and some hypervisors were studied to carry out a previous analysis. After that, a server analysis located on the Center Data and the users who access was carried out, it was determined that the servers to be virtualized are of mail not often used, operative system CentOS and ESET NOD32 antivirus under the operating system Windows Server due to the requirements presented of these ones. A comparison among the VMware tools, Kernel-Based Virtual Machine (KVM) and Xen was carried out. According to the characteristics and the requirements applied by GADMR, the VMware tool was chosen in order to implement a scenario of tests that contains a server installed previously VMware vSphere. Here, we could virtualize the services mentioned above. JMeter tool of Apache was used to make tests in order to charge, analyze and measure the performance of the virtualized servers. This tool allowed us to generate a variety of reports according to the necessities. The sample used for the tests reached a 95% of reliability and an error margin of 5%. From the results of the tests and the analysis of the corresponding parameters, we conclude that the best tool to be implemented is VMware vSphere due to its functionalities. It makes easy its management consolidating servers, minimizes resources, reduce room and first of all, meet the requirements of Department of Technology Management. We recommended to get a server with high characteristics and ouput for future requeriments

Analyses of chondrogenic induction of adipose mesenchymal stem cells by combined co-stimulation mediated by adenoviral gene transfer

INTRODUCTION: Adipose-derived stem cells (ASCs) have the potential to differentiate into cartilage under stimulation with some reported growth and transcriptional factors, which may constitute an alternative for cartilage replacement approaches. In this study, we analyzed the in vitro chondrogenesis of ASCs transduced with adenoviral vectors encoding insulin-like growth factor-1 (IGF-1), transforming growth factor beta-1 (TGF-β1), fibroblast growth factor-2 (FGF-2), and sex-determining region Y-box 9 (SOX9) either alone or in combinations. METHODS: Aggregate cultures of characterized ovine ASCs were transduced with 100 multiplicity of infections of Ad.IGF-1, Ad.TGF-β1, Ad.FGF-2, and Ad.SOX9 alone or in combination. These were harvested at various time points for detection of cartilage-specific genes expression by quantitative real-time PCR or after 14 and 28 days for histologic and biochemical analyses detecting proteoglycans, collagens (II, I and X), and total sulfated glycosaminoglycan and collagen content, respectively. RESULTS: Expression analyses showed that co-expression of IGF-1 and FGF-2 resulted in higher significant expression levels of aggrecan, biglycan, cartilage matrix, proteoglycan, and collagen II (all P ≤0.001 at 28 days). Aggregates co-transduced with Ad.IGF-1/Ad.FGF-2 showed a selective expression of proteoglycans and collagen II, with limited expression of collagens I and × demonstrated by histological analyses, and had significantly greater glycosaminoglycan and collagen production than the positive control (P ≤0.001). Western blot analyses for this combination also demonstrated increased expression of collagen II, while expression of collagens I and × was undetectable and limited, respectively. CONCLUSION: Combined overexpression of IGF-1/FGF-2 within ASCs enhances their chondrogenic differentiation inducing the expression of chondrogenic markers, suggesting that this combination is more beneficial than the other factors tested for the development of cell-based therapies for cartilage repair

Animal Models of Rheumatoid Arthritis

Autoimmunity is a condition in which the host organizes an immune response against its own antigens. Rheumatoid arthritis (RA) is an autoimmune disease of unknown etiology, characterized by the presence of chronic inflammatory infiltrates, the development of destructive arthropathy, bone erosion, and degradation of the articular cartilage and subchondral bone. There is currently no treatment that resolves the disease, only the use of palliatives, and not all patients respond to pharmacologic therapy. According to RA multifactorial origin, several in vivo models have been used to evaluate its pathophysiology as well as to identify the usefulness of biomarkers to predict, to diagnose, or to evaluate the prognosis of the disease. This chapter focuses on the most common in vivo models used for the study of RA, including those related with genetic, immunological, hormonal, and environmental interactions. Similarly, the potential of these models to understand RA pathogenesis and to test preventive and therapeutic strategies of autoimmune disorder is also highlighted. In conclusion, of all the animal models discussed, the CIA model could be considered the most successful by generating arthritis using type II collagen and adjuvants and evaluating therapeutic compounds both intra-articularly and systemically

Breast Cancer Detection by Means of Artificial Neural Networks

Breast cancer is a fatal disease causing high mortality in women. Constant efforts are being made for creating more efficient techniques for early and accurate diagnosis. Classical methods require oncologists to examine the breast lesions for detection and classification of various stages of cancer. Such manual attempts are time consuming and inefficient in many cases. Hence, there is a need for efficient methods that diagnoses the cancerous cells without human involvement with high accuracies. In this research, image processing techniques were used to develop imaging biomarkers through mammography analysis and based on artificial intelligence technology aiming to detect breast cancer in early stages to support diagnosis and prioritization of high-risk patients. For automatic classification of breast cancer on mammograms, a generalized regression artificial neural network was trained and tested to separate malignant and benign tumors reaching an accuracy of 95.83%. With the biomarker and trained neural net, a computer-aided diagnosis system is being designed. The results obtained show that generalized regression artificial neural network is a promising and robust system for breast cancer detection. The Laboratorio de Innovacion y Desarrollo Tecnologico en Inteligencia Artificial is seeking collaboration with research groups interested in validating the technology being developed

Study protocol and rationale of the “Cogniaction project” a cross-sectional and randomized controlled trial about physical activity, brain health, cognition, and educational achievement in schoolchildren

Background: Education and health are crucial topics for public policies as both largely determine the future wellbeing of the society. Currently, several studies recognize that physical activity (PA) benefits brain health in children. However, most of these studies have not been carried out in developing countries or lack the transference into the education field. The Cogni-Action Project is divided into two stages, a cross-sectional study and a crossover-randomized trial. The aim of the first part is to establish the associations of PA, sedentarism, and physical fitness with brain structure and function, cognitive performance and academic achievement in Chilean schoolchildren (10–13 years-old). The aim of the second part is to determinate the acute effects of three PA protocols on neuroelectric indices during a working memory and a reading task. Methods: PA and sedentarism will be self-reported and objectively-assessed with accelerometers in a representative subsample, whilst physical fitness will be evaluated through the ALPHA fitness test battery. Brain structure and function will be assessed by magnetic resonance imaging (MRI) in a randomized subsample. Cognitive performance will be assessed through the NeuroCognitive Performance Test, and academic achievement by school grades. In the second part 32 adolescents (12–13 year-old) will be cross-over randomized to these condition (i) “Moderate-Intensity Continuous Training” (MICT), (ii) “Cooperative High-Intensity Interval Training” (C-HIIT), and (iii) Sedentary condition. Neuroelectric indices will be measures by electroencephalogram (EEG) and eye-tracking, working memory by n-back task and reading comprehension by a reading task

Biological Activity and Implications of the Metalloproteinases in Diabetic Foot Ulcers

Inadequate metabolic control predisposes diabetic patient to a series of complications on account of diabetes mellitus (DM). Among the most common complications of DM is neuropathy, which causes microvascular damage by hyperglycemia in the lower extremities which arrives characterized by a delayed closing. The global prevalence of diabetic neuropathy (DN) was 66% of people with diabetes in 2015, representing the principal cause of total or partial lower extremities amputation, with 22.6% of the patients with DN. Matrix metalloproteinases (MMPs) are involved in healing. The function that these mainly play is the degradation during inflammation that has as consequence the elimination of the extracellular matrix (ECM), the disintegration of the capillary membrane to give way to angiogenesis and cellular migration for the remodeling of damaged tissue. The imbalance in MMPs may increase the chronicity of a wound, what leads to chronic foot ulcers and amputation. This chapter focuses on the role of MMPs in diabetic wound healing

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 and Safety of the Switch from Remicade® to CT-P13 in Patients with Inflammatory Bowel Disease

BACKGROUND AND AIMS: To evaluate the clinical outcomes in patients with IBD after switching from Remicade® to CT-P13 in comparison with patients who maintain Remicade®. METHODS: Patients under Remicade® who were in clinical remission with standard dosage at study entry were included. The ''switch cohort'' [SC] comprised patients who made the switch from Remicade® to CT-P13, and the ''non-switch'' cohort [NC] patients remained under Remicade®. RESULTS: A total of 476 patients were included: 199 [42%] in the SC and 277 [58%] in the NC. The median follow-up was 18 months in the SC and 23 months in the NC [p < 0.01]. Twenty-four out of 277 patients relapsed in the NC; the incidence of relapse was 5% per patient-year. The cumulative incidence of relapse was 2% at 6 months and 10% at 24 months in this group. Thirty-eight out of 199 patients relapsed in the SC; the incidence rate of relapse was 14% per patient-year. The cumulative incidence of relapse was 5% at 6 months and 28% at 24 months. In the multivariate analysis, the switch to CT-P13 was associated with a higher risk of relapse (HR = 3.5, 95% confidence interval [CI] = 2-6). Thirteen percent of patients had adverse events in the NC, compared with 6% in the SC [p < 0.05]. CONCLUSIONS: Switching from Remicade® to CT-P13 might be associated with a higher risk of clinical relapse, although this fact was not supported in our study by an increase in objective markers of inflammation. The nocebo effect might have influenced this result. Switching from Remicade® to CT-P13 was safe

Towards precision medicine: defining and characterizing adipose tissue dysfunction to identify early immunometabolic risk in symptom-free adults from the GEMM family study

Interactions between macrophages and adipocytes are early molecular factors influencing adipose tissue (AT) dysfunction, resulting in high leptin, low adiponectin circulating levels and low-grade metaflammation, leading to insulin resistance (IR) with increased cardiovascular risk. We report the characterization of AT dysfunction through measurements of the adiponectin/leptin ratio (ALR), the adipo-insulin resistance index (Adipo-IRi), fasting/postprandial (F/P) immunometabolic phenotyping and direct F/P differential gene expression in AT biopsies obtained from symptom-free adults from the GEMM family study. AT dysfunction was evaluated through associations of the ALR with F/P insulin-glucose axis, lipid-lipoprotein metabolism, and inflammatory markers. A relevant pattern of negative associations between decreased ALR and markers of systemic low-grade metaflammation, HOMA, and postprandial cardiovascular risk hyperinsulinemic, triglyceride and GLP-1 curves was found. We also analysed their plasma non-coding microRNAs and shotgun lipidomics profiles finding trends that may reflect a pattern of adipose tissue dysfunction in the fed and fasted state. Direct gene differential expression data showed initial patterns of AT molecular signatures of key immunometabolic genes involved in AT expansion, angiogenic remodelling and immune cell migration. These data reinforce the central, early role of AT dysfunction at the molecular and systemic level in the pathogenesis of IR and immunometabolic disorders

4to. Congreso Internacional de Ciencia, Tecnología e Innovación para la Sociedad. Memoria académica

Este volumen acoge la memoria académica de la Cuarta edición del Congreso Internacional de Ciencia, Tecnología e Innovación para la Sociedad, CITIS 2017, desarrollado entre el 29 de noviembre y el 1 de diciembre de 2017 y organizado por la Universidad Politécnica Salesiana (UPS) en su sede de Guayaquil. El Congreso ofreció un espacio para la presentación, difusión e intercambio de importantes investigaciones nacionales e internacionales ante la comunidad universitaria que se dio cita en el encuentro. El uso de herramientas tecnológicas para la gestión de los trabajos de investigación como la plataforma Open Conference Systems y la web de presentación del Congreso http://citis.blog.ups.edu.ec/, hicieron de CITIS 2017 un verdadero referente entre los congresos que se desarrollaron en el país. La preocupación de nuestra Universidad, de presentar espacios que ayuden a generar nuevos y mejores cambios en la dimensión humana y social de nuestro entorno, hace que se persiga en cada edición del evento la presentación de trabajos con calidad creciente en cuanto a su producción científica. Quienes estuvimos al frente de la organización, dejamos plasmado en estas memorias académicas el intenso y prolífico trabajo de los días de realización del Congreso Internacional de Ciencia, Tecnología e Innovación para la Sociedad al alcance de todos y todas