A multi-objective approach to estimate parameters of compartmental epidemiological models. Application to Ebola Virus Disease epidemics.

In this work, we propose a novel methodology to adjust parameters of compartmental epidemiological models. It is based on solving a multi-objective optimization problem that consists in fitting some of the model outputs to real observations. First, according to the available data of the considered epidemic, we define a multi-objective optimization problem where the model parameters are the optimization variables. Then, this problem is solved by considering a particular optimization algorithm called ParWASF-GA (ParallelWeighting Achievement Scalarizing Function Genetic Algorithm). Finally, the decision maker chooses, within the set of possible solutions, the values of parameters that better suit his/her preferences. In order to illustrate the benefit of using our approach, it is applied to estimate the parameters of a deterministic epidemiological model, called Be-CoDiS (Between-Countries Disease Spread), used to forecast the possible spread of human diseases within and between countries. We consider data from different Ebola outbreaks from 2014 up to 2019. In all cases, the proposed methodology helps to obtain reasonable predictions of the epidemic magnitudes with the considered model

Bi-Level Optimization to Enhance Intensity Modulated Radiation Therapy Planning

Intensity Modulated Radiation Therapy is an effective cancer treatment. Models based on the Generalized Equivalent Uniform Dose (gEUD) provide radiation plans with excellent planning target volume coverage and low radiation for organs at risk. However, manual adjustment of the parameters involved in gEUD is required to ensure that the plans meet patient-specific physical restrictions. This paper proposes a radiotherapy planning methodology based on bi-level optimization. We evaluated the proposed scheme in a real patient and compared the resulting irradiation plans with those prepared by clinical planners in hospital devices. The results in terms of efficiency and effectiveness are promising

Iniciativa basada en Kahoot para motivar a los alumnos de Arquitectura de Computadores

Debido al auge de la formación universitaria en remoto, es común que las clases magistrales teóricas deriven en un monólogo del profesor con baja participación del alumnado. Existe una escasa utilización, de las herramientas disponibles para participar en la clase: mensajería instantánea, micrófono o funciones de “levantar la mano”. Esta situación se agrava aún más cuando el ratio de alumnos es alto y, por tanto, es más complicada la comunicación con todos ellos, así como saber si están asimilando los conceptos. En este trabajo se describe la experiencia docente en la asignatura de Arquitectura de Computadores de incorporar una herramienta de aprendizaje móvil electrónico (M-learning), concretamente, Kahoot. Esta herramienta permite que el profesor plantee actividades participativas en el aula para reforzar el aprendizaje y aumentar la participación de los alumnos. Se ha realizado un estudio para determinar si el uso de Kahoot ha estimulado el aprendizaje de la asignatura de Arquitectura de Computadores y si ha mejorado la nota global final del alumnado.Due to the rise of remote university training, it is common for theoretical lectures to result in a monologue by the professor with low student participation. There is little use of the tools available to participate in the class: instant messaging, microphone or ’raise your hand’ functions. This situation is even worse when the ratio of students is high and, therefore, it is more complicated to communicate with all of them, as well as to know if they are assimilating the concepts. This paper describes the teaching experience in the Computer Architecture course of incorporating a mobile e-learning tool (M-learning), specifically, Kahoot. This tool allows the teacher to propose participatory activities in the classroom to reinforce learning and increase student participation. A study has been carried out to determine whether the use of Kahoot has stimulated the learning of the Computer Architecture subject and whether it has improved the students’ final overall grade

Endoglin, a novel biomarker and therapeutical target to prevent malignant peripheral nerve sheath tumor growth and metastasis.

PURPOSE Malignant peripheral nerve sheath tumors (MPNSTs) are highly aggressive soft-tissue sarcomas that lack effective treatments, underscoring the urgent need to uncover novel mediators of MPNST pathogenesis that may serve as potential therapeutic targets. Tumor angiogenesis is considered a critical event in MPNST transformation and progression. Here, we have investigated whether endoglin (ENG), a TGF-β coreceptor with a crucial role in angiogenesis, could be a novel therapeutic target in MPNSTs. EXPERIMENTAL DESIGN ENG expression was evaluated in human peripheral nerve sheath tumor tissues and plasma samples. Effects of tumor cell-specific ENG expression on gene expression, signaling pathway activation and in vivo MPNST growth and metastasis were investigated. The efficacy of ENG targeting in monotherapy or in combination with MEK inhibition was analyzed in xenograft models. RESULTS ENG expression was found to be upregulated in both human MPNST tumor tissues and plasma circulating small extracellular vesicles. We demonstrated that ENG modulates Smad1/5 and MAPK/ERK pathway activation and pro-angiogenic and pro-metastatic gene expression in MPNST cells and plays an active role in tumor growth and metastasis in vivo. Targeting with ENG-neutralizing antibodies (TRC105/M1043) decreased MPNST growth and metastasis in xenograft models by reducing tumor cell proliferation and angiogenesis. Moreover, combination of anti-ENG therapy with MEK inhibition effectively reduced tumor cell growth and angiogenesis. CONCLUSIONS Our data unveil a tumor-promoting function of ENG in MPNSTs and support the use of this protein as a novel biomarker and a promising therapeutic target for this disease.We apologize to those authors whose work could not be cited due to size limitations. We thank Dr. Eduard Serra, Dr. Conxi Lázaro and Dr. David Lyden for their support in the project. We also thank Héctor Tejero for his help in analyzing RNA-seq data. Dr. Peinado laboratory is funded by US Department of Defense (W81XWH-16-1-0131), Agencia Estatal de Investigación/Ministerio de Ciencia e Innovación (AEI/MCIN) (PID2020-118558RB-I00/AEI/10.13039/501100011033), Fundación Proyecto Neurofibromatosis, European Union’s Horizon 2020 research and innovation programme “proEVLifeCycle” under the Marie Skłodowska-Curie grant agreement No 860303, and Fundación Científica AECC. We are also grateful for the support of the Ministerio de Universidades (Programa de Formación de Profesorado Universitario (FPU)) for the fellowship FPU016/05356 awarded to T. González-Muñoz and to the Translational NeTwork for the CLinical application of Extracellular VesicleS (TeNTaCLES) RED2018-102411-T(AEI/10.13039/501100011033). A. Di Giannatale was supported during this work by a research gran Nuovo-Soldati Foundation. The CNIO, certified as Severo Ochoa Excellence Centre, is supported by the Spanish Government through the Instituto de Salud Carlos III.N

The probabilistic customer’s choice rule with a threshold attraction value: Effect on the location of competitive facilities in the plane

The classical probabilistic choice rule assumes that customers patronize all the existing facilities. As this assumption may not be appropriate in some cases, in this paper a variant is investigated, in which a customer only patronizes those facilities for which he/she feels an attraction greater than or equal to a threshold value. Implicitly, this implies that there may be some unmet demand. We apply this modified rule to the problem of locating a single new facility in the plane. A comparison of the location decisions derived from the modified rule with those obtained with the classical proportional choice rule when solving the location model reveals that the profit that the locating chain may lose if an inadequate choice rule is employed may be quite high in some instances

A planar single-facility competitive location and design problem under the multi-deterministic choice rule

A new customer choice rule, which may model in some cases the actual patronising behaviour of customers towards the facilities closer to reality than other existing rules, is proposed. According to the new rule, customers split their demand among the firms in the market by patronising only one facility from each firm, the one with the highest utility, and the demand is split among those facilities proportionally to their attraction. The influence of the choice rule in the location of facilities is investigated. In particular, a new continuous competitive single-facility location and design problem using this new rule is proposed. Both exact and heuristic methods are proposed to solve it. A comparison with the classical proportional (or Huff) choice rule when solving the location model reveals that both the location and the quality of the new facility to be located may be quite different depending on the patronising behaviour of customers. Most importantly, the profit that the locating chain may lose if a wrong choice is made can be quite high in some instances. © 2016 Elsevier Lt

Two- and three-dimensional modeling and optimization applied to the design of a fast hydrodynamic focusing microfluidic mixer for protein folding

We present a design of a microfluidic mixer based on hydrodynamic focusing which is used to initiate the folding process (i.e., changes of the molecular structure) of a protein. The folding process is initiated by diluting (from 90% to 30%) the local denaturant concentration (initially 6 M GdCl solution) in a short time interval we refer to as mixing time. Our objective is to optimize this mixer by choosing suitable shape and flow conditions in order to minimize this mixing time. To this end, we first introduce a numerical model that enables computation of the mixing time of a mixer. This model is based on a finite element method approximation of the incompressible Navier-Stokes equations coupled with the convective diffusion equation. To reduce the computational time, this model is implemented in both full three-dimensional (3D) and simplified two-dimensional (2D) versions; and we analyze the ability of the 2D model to approximate the mixing time predicted by the 3D model. We found that the 2D model approximates the mixing time predicted by the 3D model with a mean error of about 15%, which is considered reasonable. Then, we define a mixer optimization problem considering the 2D model and solve it using a hybrid global optimization algorithm. In particular, we consider geometrical variables and injection velocities as optimization parameters. We achieve a design with a predicted mixing time of 0.10 μs, approximately one order of magnitude faster than previous mixer designs. This improvement can be in part explained by the new mixer geometry including an angle of π/5 radians at the channel intersection and injections velocities of 5.2 m s−1 and 0.038 m s−1 for the side and central inlet channels, respectively. Finally, we verify the robustness of the optimized result by performing a sensitivity analysis of its parameters considering the 3D model. During this study, the optimized mixer was demonstrated to be robust by exhibiting mixing time variations of the same order than the parameter ones. Thus, the obtained 2D design can be considered optimal also for the 3D model