Input-dependent structural identifiability of nonlinear systems

A dynamic model is structurally identifiable if it is possible to infer its unknown parameters by observing its output. Structural identifiability depends on the system dynamics, output, and input, as well as on the specific values of initial conditions and parameters. Here we present a symbolic method that characterizes the input that a model requires to be structurally identifiable. It determines which derivatives must be non-zero in order to have a sufficiently exciting input. Our approach considers structural identifiability as a generalization of nonlinear observability and incorporates extended Lie derivatives. The methodology assesses structural identifiability for time-varying inputs and, additionally, it can be used to determine the input profile that is required to make the parameters structurally locally identifiable. Furthermore, it is sometimes possible to replace an experiment with time-varying input with multiple experiments with constant inputs. We implement the resulting method as a MATLAB toolbox named STRIKE-GOLDD2. This tool can assist in the design of new experiments for the purpose of parameter estimation

Phenotypic Characterization According to the Feather Color of Indigenous Muscovy Ducks Bred in the Back Yard in Brazzaville, the Congo

. In Congo, waterfowl genetic resources are constituted by native population of Muscovy ducks that play an important role in food security. The present study aimed to identify and to characterize strains bred in the back yard in the households in Brazzaville. A sample of 154 households drawn over seven districts of Brazzaville was enrolled in the survey. Adults ducks found in the households were identified, pictured by a key of determination and then compared by using the multi resolution analysis image method. The survey recorded 13 strains in which four were considered as newly since they have never been reported elsewhere. These strains received temporally the name of the districts where they have been identified for the first time Makelékélé 1 (0.34%, n=6), Makélékélé 2 (0.11%, n =2), Poto poto 1 (0.28%, n=5) and in Poto poto 2 (0.11%, n=2). Finally, the survey reported nine classical strains such as black plumage, duclair, white, tortora, sepia, chocolate, lavender, grey and canizie. The apparent wide variation in plumage colors is an indication that the duck populations have not been ‘purified' through selective breeding. In the context of the valorization of poultry biodiversity, this work represents a step toward a better knowledge of the production abilities of local ducks breeds in Congo

Head-Jolting Nystagmus Occlusion of the Horizontal Semicircular Canal Induced by Vigorous Head Shaking

Importance: We report a new syndrome, which we are calling head-jolting nystagmus, that expands the differential diagnosis of head movement–induced paroxysmal vertigo. Observations: Two male patients (65 and 58 years old) described rotational vertigo after violent and brief (1- to 2-second) oscillations of the head (head jolting) that triggered intense horizontal nystagmus lasting 45 seconds. Accelerations of the head required to induce these episodes could only be achieved by the patients themselves. In case 1, the episodes gradually disappeared over a 6-year period. In case 2, magnetic resonance imaging (3-T) suggested a filling defect within the left horizontal semicircular canal. He underwent surgical canal plugging in March 2013 that resolved the symptoms. Conclusions and Relevance: We attribute head-jolting nystagmus to dislodged material within the horizontal semicircular canal and provide a mechanistic model to explain its origin

Implementing Parallel Differential Evolution on Spark

[Abstract] Metaheuristics are gaining increased attention as an efficient way of solving hard global optimization problems. Differential Evolution (DE) is one of the most popular algorithms in that class. However, its application to realistic problems results in excessive computation times. Therefore, several parallel DE schemes have been proposed, most of them focused on traditional parallel programming interfaces and infrastruc- tures. However, with the emergence of Cloud Computing, new program- ming models, like Spark, have appeared to suit with large-scale data processing on clouds. In this paper we investigate the applicability of Spark to develop parallel DE schemes to be executed in a distributed environment. Both the master-slave and the island-based DE schemes usually found in the literature have been implemented using Spark. The speedup and efficiency of all the implementations were evaluated on the Amazon Web Services (AWS) public cloud, concluding that the island- based solution is the best suited to the distributed nature of Spark. It achieves a good speedup versus the serial implementation, and shows a decent scalability when the number of nodes grows.[Resumen] Las metaheurísticas están recibiendo una atención creciente como técnica eficiente en la resolución de problemas difíciles de optimización global. Differential Evolution (DE) es una de las metaheurísticas más populares, sin embargo su aplicación en problemas reales deriva en tiempos de cómputo excesivos. Por ello se han realizado diferentes propuestas para la paralelización del DE, en su mayoría utilizando infraestructuras e interfaces de programación paralela tradicionales. Con la aparición de la computación en la nube también se han propuesto nuevos modelos de programación, como Spark, que permiten manejar el procesamiento de datos a gran escala en la nube. En este artículo investigamos la aplicabilidad de Spark en el desarrollo de implementaciones paralelas del DE para su ejecución en entornos distribuidos. Se han implementado tanto la aproximación master-slave como la basada en islas, que son las más comunes. También se han evaluado la aceleración y la eficiencia de todas las implementaciones usando el cloud público de Amazon (AWS, Amazon Web Services), concluyéndose que la implementación basada en islas es la más adecuada para el esquema de distribución usado por Spark. Esta implementación obtiene una buena aceleración en relación a la implementación serie y muestra una escalabilidad bastante buena cuando el número de nodos aumenta.[Resume] As metaheurísticas están recibindo unha atención a cada vez maior como técnica eficiente na resolución de problemas difíciles de optimización global. Differential Evolution (DE) é unha das metaheurísticas mais populares, ainda que a sua aplicación a problemas reais deriva en tempos de cómputo excesivos. É por iso que se propuxeron diferentes esquemas para a paralelización do DE, na sua maioría utilizando infraestruturas e interfaces de programación paralela tradicionais. Coa aparición da computación na nube tamén se propuxeron novos modelos de programación, como Spark, que permiten manexar o procesamento de datos a grande escala na nube. Neste artigo investigamos a aplicabilidade de Spark no desenvolvimento de implementacións paralelas do DE para a sua execución en contornas distribuidas. Implementáronse tanto a aproximación master-slave como a baseada en illas, que son as mais comúns. Tamén se avaliaron a aceleración e a eficiencia de todas as implementacións usando o cloud público de Amazon (AWS, Amazon Web Services), tirando como conclusión que a implementación baseada en illas é a mais acaida para o esquema de distribución usado por Spark. Esta implementación obtén unha boa aceleración en relación á implementación serie e amosa unha escalabilidade bastante boa cando o número de nos aumenta.Ministerio de Economía y Competitividad; DPI2014-55276-C5-2-RXunta de Galicia; GRC2013/055Xunta de Galicia; R2014/04

Mortality rates in transplant recipients and transplantation candidates in a high prevalence COVID-19 environment

Background: The risk of COVID-19 infection in transplant recipients (TRs) is unknown. Patients on dialysis may be exposed to greater risk of infection due to an inability to isolate. Consideration of these competing risks is important before restarting suspended transplant programs. This study compared outcomes in kidney and kidney/pancreas TRs with those on the waiting list, following admission with COVID-19 in a high-prevalence region. Methods: Audit data from all 6 London transplant centers were amalgamated. Demographic and laboratory data were collected and outcomes included mortality, intensive care (ITU) admission, and ventilation. Adult patients who had undergone a kidney or kidney/pancreas transplant, and those active on the transplant waiting list at the start of the pandemic were included. Results: One hundred twenty-one TRs and 52 waiting list patients (WL) were admitted to hospital with COVID-19. Thirty-six TR died (30%), while 14 WL patients died (27% P = 0.71). There was no difference in rates of admission to ITU or ventilation. Twenty-four percent of TR required renal replacement therapy, and 12% lost their grafts. Lymphocyte nadir and D-dimer peak showed no difference in those who did and did not die. No other comorbidities or demographic factors were associated with mortality, except for age (odds ratio of 4.3 [95% CI 1.8-10.2] for mortality if aged over 60 y) in TR. Conclusions: TRs and waiting list patients have similar mortality rates after hospital admission with COVID-19. Mortality was higher in older TRs. These data should inform decisions about transplantation in the COVID era

Safety and Feasibility of Thoracic Malignancy Surgery During the COVID-19 Pandemic

Background: The coronavirus disease 2019 (COVID-19) pandemic has decreased surgical activity, particularly in the field of oncology, because of the suspicion of a higher risk of COVID-19–related severe events. This study aimed to investigate the feasibility and safety of thoracic cancer surgery in the most severely affected European and Canadian regions during the COVID-19 pandemic. Methods: The study investigators prospectively collected data on surgical procedures for malignant thoracic diseases from January 1 to April 30, 2020. The study included patients from 6 high-volume thoracic surgery departments: Nancy and Strasbourg (France), Freiburg (Germany), Milan and Turin (Italy), and Montreal (Canada). The centers involved in this research are all located in the most severely affected regions of those countries. An assessment of COVID-19–related symptoms, polymerase chain reaction (PCR)–confirmed COVID-19 infection, rates of hospital and intensive care unit admissions, and death was performed for each patient. Every deceased patient was tested for COVID-19 by PCR. Results: In the study period, 731 patients who underwent 734 surgical procedures were included. In the whole cohort, 9 cases (1.2%) of COVID-19 were confirmed by PCR, including 5 in-hospital contaminants. Four patients (0.5%) needed readmission for oxygen requirements. In this subgroup, 2 patients (0.3%) needed intensive care unit and mechanical ventilatory support. The total number of deaths in the whole cohort was 22 (3%). A single death was related to COVID-19 (0.14%). Conclusions: Maintaining surgical oncologic activity in the era of the COVID-19 pandemic seems safe and feasible, with very low postoperative morbidity or mortality. To continue to offer the best care to patients who do not have COVID-19, reports on other diseases are urgently needed