Estudio, diseño e implementación de un sistema de monitorización basado en dron

En este trabajo de final de grado se detalla el estudio y desarrollo de un sistema de monitorización basado en dron. El sistema consta de dos partes, un nodo, que permanece estático en la zona de monitorización y se encarga de la adquisición de señales, y un UAV, que se pilota de manera autónoma hasta la ubicación del nodo y obtiene los datos adquiridos de manera inalámbrica

Serum markers improve current prediction of metastasis development in early-stage melanoma patients: a machine learning-based study

Metastasis development represents an important threat for melanoma patients, even when diagnosed at early stages and upon removal of the primary tumor. In this scenario, determination of prognostic biomarkers would be of great interest. Serum contains information about the general status of the organism and therefore represents a valuable source for biomarkers. Thus, we aimed to define serological biomarkers that could be used along with clinical and histopathological features of the disease to predict metastatic events on the early-stage population of patients. We previously demonstrated that in stage II melanoma patients, serum levels of dermcidin (DCD) were associated with metastatic progression. Based on the relevance of the immune response on the cancer progression and the recent association of DCD with local and systemic immune response against cancer cells, serum DCD was analyzed in a new cohort of patients along with interleukin 4 (IL-4), IL-6, IL-10, IL-17A, interferon gamma (IFN-gamma), transforming growth factor-beta (TGF- beta), and granulocyte-macrophage colony-stimulating factor (GM-CSF). We initially recruited 448 melanoma patients, 323 of whom were diagnosed as stages I-II according to AJCC. Levels of selected cytokines were determined by ELISA and Luminex, and obtained data were analyzed employing machine learning and Kaplan-Meier techniques to define an algorithm capable of accurately classifying early-stage melanoma patients with a high and low risk of developing metastasis. The results show that in early-stage melanoma patients, serum levels of the cytokines IL-4, GM-CSF, and DCD together with the Breslow thickness are those that best predict melanoma metastasis. Moreover, resulting algorithm represents a new tool to discriminate subjects with good prognosis from those with high risk for a future metastasis.We are grateful to the Basque Biobank for providing the serum samples. We are also most grateful to Drs Arantza Arrieta and Natalia Maruri (Cruces University Hospital) for their technical support with the serum marker detection. This work was supported by grants from the Basque Government (KK2016-036 and KK2017-041 to MDB), UPV/EHU (GIU17/066 to MDB), H2020-ESCEL JTI (15/01 to MDB), and MINECO (PCIN-2015-241 to MDB

Study of cFS (Core Flight System) to deploy OBC (On Board Computer) software for IoT missions

This thesis exposes the study, development, integration and validation of an OBC software of a satellite, using cFS. The OBC will be in charge of retrieving data from all sensors and subsystems as well as of transmitting it to the desired actuators or even the ground stations. The mission of the satellite is to transmit data from IoT stations to control centres through a constellation. At the current stage of the project, the developments are focused on integration in HitL simulations

Estudio, diseño e implementación de un sistema de monitorización basado en dron

En este trabajo de final de grado se detalla el estudio y desarrollo de un sistema de monitorización basado en dron. El sistema consta de dos partes, un nodo, que permanece estático en la zona de monitorización y se encarga de la adquisición de señales, y un UAV, que se pilota de manera autónoma hasta la ubicación del nodo y obtiene los datos adquiridos de manera inalámbrica

Estudio, diseño e implementación de un sistema de monitorización basado en dron

En este trabajo de final de grado se detalla el estudio y desarrollo de un sistema de monitorización basado en dron. El sistema consta de dos partes, un nodo, que permanece estático en la zona de monitorización y se encarga de la adquisición de señales, y un UAV, que se pilota de manera autónoma hasta la ubicación del nodo y obtiene los datos adquiridos de manera inalámbrica

Serum markers improve current prediction of metastasis development in early‐stage melanoma patients: a machine learning‐based study

Metastasis development represents an important threat for melanoma patients, even when diagnosed at early stages and upon removal of the primary tumor. In this scenario, determination of prognostic biomarkers would be of great interest. Serum contains information about the general status of the organism and therefore represents a valuable source for biomarkers. Thus, we aimed to define serological biomarkers that could be used along with clinical and histopathological features of the disease to predict metastatic events on the early-stage population of patients. We previously demonstrated that in stage II melanoma patients, serum levels of dermcidin (DCD) were associated with metastatic progression. Based on the relevance of the immune response on the cancer progression and the recent association of DCD with local and systemic immune response against cancer cells, serum DCD was analyzed in a new cohort of patients along with interleukin 4 (IL-4), IL-6, IL-10, IL-17A, interferon gamma (IFN-gamma), transforming growth factor-beta (TGF- beta), and granulocyte-macrophage colony-stimulating factor (GM-CSF). We initially recruited 448 melanoma patients, 323 of whom were diagnosed as stages I-II according to AJCC. Levels of selected cytokines were determined by ELISA and Luminex, and obtained data were analyzed employing machine learning and Kaplan-Meier techniques to define an algorithm capable of accurately classifying early-stage melanoma patients with a high and low risk of developing metastasis. The results show that in early-stage melanoma patients, serum levels of the cytokines IL-4, GM-CSF, and DCD together with the Breslow thickness are those that best predict melanoma metastasis. Moreover, resulting algorithm represents a new tool to discriminate subjects with good prognosis from those with high risk for a future metastasis.We are grateful to the Basque Biobank for providing the serum samples. We are also most grateful to Drs Arantza Arrieta and Natalia Maruri (Cruces University Hospital) for their technical support with the serum marker detection. This work was supported by grants from the Basque Government (KK2016-036 and KK2017-041 to MDB), UPV/EHU (GIU17/066 to MDB), H2020-ESCEL JTI (15/01 to MDB), and MINECO (PCIN-2015-241 to MDB