Glucose-Insulin regulator for type 1 diabetes using high order neural networks

In this paper a Glucose-Insulin regulator for Type 1 Diabetes using artificial neural networks (ANN) is proposed. This is done using a discrete recurrent high order neural network in order to identify and control a nonlinear dynamical system which represents the pancreas? beta-cells behavior of a virtual patient. The ANN which reproduces and identifies the dynamical behavior system, is configured as series parallel and trained on line using the extended Kalman filter algorithm to achieve a quickly convergence identification in silico. The control objective is to regulate the glucose-insulin level under different glucose inputs and is based on a nonlinear neural block control law. A safety block is included between the control output signal and the virtual patient with type 1 diabetes mellitus. Simulations include a period of three days. Simulation results are compared during the overnight fasting period in Open-Loop (OL) versus Closed- Loop (CL). Tests in Semi-Closed-Loop (SCL) are made feedforward in order to give information to the control algorithm. We conclude the controller is able to drive the glucose to target in overnight periods and the feedforward is necessary to control the postprandial period

Diseño de un regulador no lineal por bloques para el sistema glucosa-insulina utilizando redes neuronales de alto orden

En este trabajo se propone un algoritmo de control en lazo cerrado para el control automático de la diabetes tipo 1 basado en la identificación de sistemas no lineales con redes neuronales artificiales y mediante la regulación basada en la forma controlable no lineal por bloques. Como paciente virtual se utiliza el modelo de Hovorka al que se conecta el algoritmo de control utilizando prealimentación procedente de la terapia prescrita con insulina y un módulo de seguridad para evitar las hipoglucemias. El identificador neuronal es entrenado en línea con un filtro de Kalman extendido con una función de activación definida por la tangente hiperbólica. El controlador no lineal por bloques se basa en la estructura de la red neuronal, cuya salida es la propuesta de dosificación de insulina antes de prealimentación y módulo de seguridad. El algoritmo presenta un peso que se interpreta como una ganancia de controlabilidad. La glucosa del paciente está condicionada al valor de la ganancia, se definen tres ensayos con diferentes valores: ensayo A (10?3); ensayo B (7 · 10?4) y ensayo C (9 · 10?3). El valor del peso de controlabilidad condiciona la cantidad de insulina propuesta por el controlador de forma directa en el paciente virtual medio del modelo de Hovorka

Co-occurrence of colistin-resistance genes mcr-1 and mcr-3 among multidrug-resistant Escherichia coli isolated from cattle, Spain, September 2015

Los genes de resistencia a colistina mcr-3 y mcr-1 se detectaron en un aislado de Escherichia coli de heces de ganado en un matadero español en 2015. Las secuencias de ambos genes se hibridaron a la misma banda de plásmidos de aproximadamente 250 kb, aunque la resistencia a la colistina no fue movilizable . El aislado producía betalactamasas de espectro extendido y pertenecía al serotipo O9: H10 y al tipo de secuencia ST533. Aquí informamos un gen mcr-3 detectado en Europa después de informes anteriores de Asia y los Estados Unidos.Colistin resistance genes mcr-3 and mcr-1 have been detected in an Escherichia coli isolate from cattle faeces in a Spanish slaughterhouse in 2015. The sequences of both genes hybridised to same plasmid band of ca 250 kb, although colistin resistance was non-mobilisable. The isolate was producing extended-spectrum beta-lactamases and belonged to serotype O9:H10 and sequence type ST533. Here we report an mcr-3 gene detected in Europe following earlier reports from Asia and the United States.• Ministerio de Economía, Industria y Competitividad. Proyecto AGL2016- 74882-C3 • Ministerio de Agricultura y Pesca (España) y Comunidad Autónoma de Comunidad Autónoma de Madrid. Ayuda S2013 / ABI-2747 • Junta de Extremadura y Fondo Europeo de Desarrollo Regional. Ayuda GR15075 e IB16073 • Fundación para la Ciencia y la Tecnología (Portugal). Ayudas UID / MAR / 04292/2013 • Fundación Tatiana de Guzmán El Bueno (España). Beca doctoral para María del Rocío Iglesias Parro • Instituto Nacional de Agricultura y Alimentación. Investigación y Tecnología (INIA). Beca doctoral para María del Rocío Iglesias Parro • Ministerio de Economía, Industria y Competitividad. Beca FPI2014-020, para Narciso Martín QuijadapeerReviewe

Linear closed-loop control using Luenberger observer applied to insulin administration for type 1 diabetes

In this work the design of a Luenberger observer is proposed to estimate the unmeasurable state space variables from Hovorka?s model. This model is linearized and evaluated in an operation point where Luenberger observer is designed using the Ackermann methodology. The observer is employed to estimate the unmeasurable variables of virtual patients which are generated by Bergman?s model. Once the unmeasurable state variables are obtained by the Luenberger observer using only the input-output information of the Bergman?s model, a control algorithm based on eigenvalues relocation trough Ackermann methodology for linear systems is applied. In this methodology, a constant feedback gain vector is obtained in order to compute the control signal (insulin) to be applied to virtual patient and keep on normoglycemia rank. The carbohydrates ingestion is considered as the main disturbances. In order to assess the proposed methodology, two tests are designed: the first one consists of changing the reference signal in order to evaluate the control sensitivity; and the second one includes different proportions of prandial insulin used in open-loop to try the controller response under distinct operation conditions. The results are obtained via simulation using Simulink of Matlab

Metodólogia activa-participativa en la enseñanza de literatura, con estudiantes de noveno grado "A", Instituto Nacional España, ciudad y Municipio Malpaisillo, León, I semestre 2012

Tesis (Lic. en Ciencias de la Educación con Mención en Lengua y Literatura)-Universidad Nacional Autónoma de Nicaragua, León.UNAN-Leó

Luenberger observer with nonlinear structure applied to diabetes type 1

In this work a Luenberger observer (LO) for type 1 diabetes is established using the Hovorka?s model (HM). The HM is linearized around an operating point and the eigenvalues are calculated. The LO is designed relocating the HM eigenvalues through the Ackermann?s methodology for linear observers where the proposed LO keeps the nonlinear structure of the model system. The LO is parameterized and tuned with the mean from six virtual patients of HM. Once the observer performance is reliable estimating the state space variables for HM, the virtual patients are changed by patients of Bergman?s model in order to test the observer behavior under unknown dynamics. These estimated variables constitute the ones corresponding to HM. The variables are estimated by the data computational processing which correspond to the insulin (input) and glucose (output) of the virtual patients. The estimated variables by the LO are very similar for virtual patients generated by both models, where the parameter FIT is used to quantify the performance of the observer. The computational implementation of the LO is useful tool to estimate the unmeasured variables in diabetic patients so they can be used in the artificial pancreas

Linear time-varying Luenberger observer applied to diabetes

We present a linear time-varying Luenberger observer (LTVLO) using compartmental models to estimate the unmeasurable states in patients with type 1 diabetes. The LTVLO proposed is based on the linearization in an operation point of the virtual patient (VP), where a linear time-varying system is obtained. LTVLO gains are obtained by selection of the asymptotic eigenvalues where the observability matrix is assured. The estimation of the unmeasurable variables is done using Ackermann’s methodology. The Lyapunov approach is used to prove the stability of the time-varying proposal. In order to evaluate the proposed methodology, we designed three experiments: A) VP obtained with Bergman’s minimal model, B) VP obtained with Hovorka’s model, and C) real patient data set. For both experiments A) and B), it is applied a meal plan to the VP, where the dynamic response of each state model is compared to the response of each variable of the time-varying observer. Once the observer is obtained in experiment B), the proposal is applied to experiment C) with data extracted from real patients and the unmeasurable state space variables are obtained with the LTVLO. LTVLO methodology has the feature of being updated each time instant to estimate the states under a known structure. The results are obtained using simulation with M atlabTM and SimulinkTM. The LTVLO estimates the unmeasurable states from in silico patients with high accuracy by means of the update of Luenberger gains at each iteration. The accuracy of the estimated state space variables is validated through the fit parameter

Luenberger observer with nonlinear structure applied to diabetes type 1

In this work a Luenberger observer (LO) for type 1 diabetes is established using the Hovorka?s model (HM). The HM is linearized around an operating point and the eigenvalues are calculated. The LO is designed relocating the HM eigenvalues through the Ackermann?s methodology for linear observers where the proposed LO keeps the nonlinear structure of the model system. The LO is parameterized and tuned with the mean from six virtual patients of HM. Once the observer performance is reliable estimating the state space variables for HM, the virtual patients are changed by patients of Bergman?s model in order to test the observer behavior under unknown dynamics. These estimated variables constitute the ones corresponding to HM. The variables are estimated by the data computational processing which correspond to the insulin (input) and glucose (output) of the virtual patients. The estimated variables by the LO are very similar for virtual patients generated by both models, where the parameter FIT is used to quantify the performance of the observer. The computational implementation of the LO is useful tool to estimate the unmeasured variables in diabetic patients so they can be used in the artificial pancreas

Investigadoras en la UNAM : trabajo académico, productividad y calidad de vida

La implementación de los sistemas de estímulos económicos por rendimiento académico introdujo modificaciones sustantivas en las formas de trabajo y en la cultura laboral en la Universidad. En este libro las autoras analizan cómo se articulan las lógicas de la productividad, que provienen de dichos sistemas, con la condición de género, para crear diferencias sutiles que profundizan, aún más, las desigualdades ya existentes entre investigadores e investigadoras de la UNAM