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BackgroundEarly identification of the transition from relapsing-remitting multiple sclerosis (RRMS) to secondary progressive MS (SPMS) can be challenging for clinicians, as diagnostic criteria for SPMS are primarily based on physical disability and a holistic interpretation.ObjectiveTo establish a consensus on patient monitoring to identify promptly disease progression and the most useful clinical and paraclinical variables for early identification of disease progression in MS.MethodsA RAND/UCLA Appropriateness Method was used to establish the level of agreement among a panel of 15 medical experts in MS. Eighty-three items were circulated to the experts for confidential rating of the grade of agreement and recommendation. Consensus was defined when ≥66% agreement or disagreement was achieved.ResultsConsensus was reached in 72 out of 83 items (86.7%). The items addressed frequency of follow-up visits, definition of progression, identification of clinical, cognitive, and radiological assessments as variables of suspected or confirmed SPMS diagnosis, the need for more accurate assessment tools, and the use of promising molecular and imaging biomarkers to predict disease progression and/or diagnose SPMS.ConclusionConsensus achieved on these topics could guide neurologists to identify earlier disease progression and to plan targeted clinical and therapeutic interventions during the earliest stages of SPMS.

Through programming tasks, the skills and abilities used to solve mathematical problems are developed and improved. In this article we present a programming teaching trajectory using the solutions presented throughout the history of the “hundred fowls” problem. The proposed itinerary is graduated, meaning that it can be used for different educational stages.

RESUMEN Introducción: Con el propósito de justificar la decisión diplomática de los Estados Unidos, se publicaron dos artículos científicos en revistas médicas que intentan sostener la idea de que en La Habana hubo un ataque dirigido a personal de la embajada estadounidense. Objetivo: Demostrar la falta de rigor científico en dos investigaciones de series de casos no independientes sobre los síntomas de salud de diplomáticos de los Estados Unidos en La Habana. Método: Se realiza un análisis documental de dos publicaciones de series de casos. Se evalúan hipótesis diagnósticas. Resultados: Existe superposición amplia entre las dos series en cuanto a pacientes compartidos y en contraste se presentan algunas diferencias en los datos clínicos que superan lo esperado. Conclusiones: En ambas publicaciones se desaprovecha la riqueza semiográfica de síntomas y la información psicosocial. Se enfatiza más en argumentos asociados al fetichismo de la tecnología expresado en la interpretación de hallazgos inespecíficos. El análisis de datos clínicos permitió ver que se trata de un grupo heterogéneo de personas cuyas quejas de salud han sido reunidas por la interacción de otros factores psicosociales contextuales.

Aims: To evaluate the effect of postoperative malperfusion (PM) on operative mortality and on late survival in patients who underwent surgery for acute type A aortic dissection in a referred center for aortic emergency surgery. Patients and Methods: From January 2005 to September 2015, 237 patients were referred for aortic emergency surgery at our center. We examined complete data available on 214 patients (mean age 62.5±12.6 years, 156 males). At presentation, various types of preoperative malperfusion (cerebral, renal, mesenteric) were observed in 119 patients (55.6%). Arterial access for cardiopulmonary bypass was via femoral artery (n = 99), via axillary artery (n = 99), or into the ascending aorta (n = 22). Aortic repair was performed using an open technique in 124 patients (58%). Results: Fifty-five patients (25.7%) presented PM; operative mortality was 29% (62/214): 47.3% in PM patients vs. 22.6% in non-PM patients (P < 0.0001). Independent predictors were age >75 years at the time of operation (OR: 1.1, P = 0.0004) and renal PM (OR: 53.5, P = 0.0027). Five-year survival was 79±7% in PM vs. 94±3% in non-PM patients (P = 0.002). Independent predictors for reduced survival were age >75 years (OR: 375, P = 0.05) and renal PM (OR: 28.6, P = 0.01). All types of PM and the location of intimal tear distal to the ascending aorta were found as risk factors for survival in the univariate analysis only (P < 0.05). Conclusions: Surgery for acute aortic dissection is effective in reducing preoperative malperfusion by about 50%. Renal PM is associated with higher operative mortality, whereas all types of PM, in particular renal PM, negatively affected late survival. Surgical techniques, site of arterial cannulation, and more complex interventions requiring an open technique did not appear to be predictors of increased risk. [Arch Clin Exp Surg 2017; 6(4.000): 175-182]

Means of positive numbers appear in many applications and have been a traditional matter of study. In this work, we focus on defining a new mean of two positive values with some properties which are essential in applications, ranging from subdivision and multiresolution schemes to the numerical solution of conservation laws. In particular, three main properties are crucial—in essence, the ideas of these properties are roughly the following: to stay close to the minimum of the two values when the two arguments are far away from each other, to be quite similar to the arithmetic mean of the two values when they are similar and to satisfy a Lipchitz condition. We present new means with these properties and improve upon the results obtained with other means, in the sense that they give sharper theoretical constants that are closer to the results obtained in practical examples. This has an immediate correspondence in several applications, as can be observed in the section devoted to a particular example.

Multiresolution representations of data are known to be powerful tools in data analysis and processing, and they are particularly interesting for data compression. In order to obtain a proper definition of the edges, a good option is to use nonlinear reconstructions. These nonlinear reconstruction are the heart of the prediction processes which appear in the definition of the nonlinear subdivision and multiresolution schemes. We define and study some nonlinear reconstructions based on the use of nonlinear means, more in concrete the so-called Generalized means. These means have two interesting properties that will allow us to get associated reconstruction operators adapted to the presence of discontinuities, and having the maximum possible order of approximation in smooth areas. Once we have these nonlinear reconstruction operators defined, we can build the related nonlinear subdivision and multiresolution schemes and prove more accurate inequalities regarding the contractivity of the scheme for the first differences and in turn the results about stability. In this paper, we also define a new nonlinear two-dimensional multiresolution scheme as non-separable, i.e., not based on tensor product. We then present the study of the stability issues for the scheme and numerical experiments reinforcing the proven theoretical results and showing the usefulness of the algorithm.

In this work, a model for the simulation of infectious disease outbreaks including mobility data is presented. The model is based on the SAIR compartmental model and includes mobility data terms that model the flow of people between different regions. The aim of the model is to analyze the influence of mobility on the evolution of a disease after a lockdown period and to study the appearance of small epidemic outbreaks due to the so-called imported cases. We apply the model to the simulation of the COVID-19 in the various areas of Spain, for which the authorities made available mobility data based on the position of cell phones. We also introduce a method for the estimation of incomplete mobility data. Some numerical experiments show the importance of data completion and indicate that the model is able to qualitatively simulate the spread tendencies of small outbreaks. This work was motivated by an open call made to the mathematical community in Spain to help predict the spread of the epidemic.

Educational robotics are commonly present in kindergarten and primary school classrooms, particularly Bee-bot. Its ease of use allows the introduction of computer programming to young children in educational contexts from a science, technology, engineering, arts, and mathematics (STEAM) perspective. Despite this rise, there are still few investigations that collect evidence on the effectiveness of robotic interventions. Although mentoring experiences with robotics had been carried out in educational contexts, this work explores their effect on the acquisition of computational thinking skills through mentoring. Participants from the second grade, aged seven through eight years, were exposed to two sessions of robotics with Bee-bot in order to promote hands-on experimentation. The sessions were conducted by nine students of the fourth grade (the mentors), aged 10 to 11 years. A descriptive case-study methodology was employed for the analysis of the mentoring intervention. The effect of the mentoring experience was assessed in terms of motivation and computational thinking skills. Mixed quantitative and qualitative results show two important findings: (i) Mentoring is a powerful tool to be considered for improvement of the motivation and cooperation of students in their teaching–learning process, and (ii) computational thinking skills can be acquired by second-grade students through a mentoring process.

Nowadays, Augmented Reality (AR) is one of the emerging technologies with a greater impact in the Education field. Research has proved that AR-based activities improve the teaching and learning processes. Also, the use of this type of technology in classroom facilitates the understanding of contents from different areas as Arts, Mathematics or Science. In this work we propose an AR-based instruction in order to explore the benefits in a 6th-grade Primary course related to 3D-geometry shapes. This first experiment, designed from an exploratory approach, will shed light on new study variables to perform new implementations whose conclusions become more consistent. The results obtained allow us to envisage that AR-based proposals slightly improve the classical didactic methods.

Nowadays, Augmented Reality (AR) is one of the emerging technologies with a greater impact in the Education field. Research has proved that AR-based activities improve the teaching and learning processes. Also, the use of this type of technology in classroom facilitates the understanding of contents from different areas as Arts, Mathematics or Science. In this work we propose an AR-based instruction in order to explore the benefits in a 6th-grade Primary course related to 3D-geometry shapes. This first experiment, designed from an exploratory approach, will shed light on new study variables to perform new implementations whose conclusions become more consistent. The results obtained allow us to envisage that AR-based proposals slightly improve the classical didactic methods.

Educational robotics are commonly present in kindergarten and primary school classrooms, particularly Bee-bot. Its ease of use allows the introduction of computer programming to young children in educational contexts from a science, technology, engineering, arts, and mathematics (STEAM) perspective. Despite this rise, there are still few investigations that collect evidence on the effectiveness of robotic interventions. Although mentoring experiences with robotics had been carried out in educational contexts, this work explores their effect on the acquisition of computational thinking skills through mentoring. Participants from the second grade, aged seven through eight years, were exposed to two sessions of robotics with Bee-bot in order to promote hands-on experimentation. The sessions were conducted by nine students of the fourth grade (the mentors), aged 10 to 11 years. A descriptive case-study methodology was employed for the analysis of the mentoring intervention. The effect of the mentoring experience was assessed in terms of motivation and computational thinking skills. Mixed quantitative and qualitative results show two important findings: (i) Mentoring is a powerful tool to be considered for improvement of the motivation and cooperation of students in their teaching-learning process, and (ii) computational thinking skills can be acquired by second-grade students through a mentoring process.

Patterning, as a component of early mathematic knowledge, is a common activity carried out at elementary levels in which children are not equally successful. This study aimed to measure different variables affecting performance on patterning tasks in early childhood. For this purpose, the success of Pre-K (N = 33), K (N = 31) and first-grade (N = 33) children when solving 14 repeating-pattern tasks, which varied in complexity, was analysed. The results revealed no significant differences between core-2 and core-4 length patterns, and greater success with patterns involving size. The study also introduces distractors, as a novel factor, in the patterning activities related to the presence of contradictory or surplus data. The impact of each factor and the relations between complexity and difficulty on the patterning tasks are discussed in order to contribute to the design of teaching itineraries. [Spanish language review by Mercè Rius.]