Secuenciación dinámica de sistemas de fabricación flexible mediante aprendizaje automático: análisis de los principales sistemas de secuenciación existentes

Una forma habitual de secuenciar de modo dinámico los trabajos en los sistemas de fabricación es mediante el empleo de reglas de secuenciación. Sin embargo, el problema que presenta este método es que el comportamiento del sistema de fabricación dependerá de su estado, y no existe una regla que supere a las demás en todos los posibles estados que puede presentar el sistema de fabricación. Por lo tanto, sería interesante usar en cada momento la regla más adecuada. Para lograr este objetivo, se pueden utilizar sistemas de secuenciación que emplean aprendizaje automático que permiten, analizando el comportamiento previo del sistema de fabricación (ejemplos de entrenamiento), obtener el conocimiento necesario para determinar la regla de secuenciación más apropiada en cada instante. En el presente trabajo se realiza una revisión de los principales sistemas de secuenciación existentes en la literatura que utilizan aprendizaje automático para variar de forma dinámica la regla de secuenciación empleada en cada momento

Aplicación de redes neuronales artificiales a la previsión de series temporales no estacionarias o no invertibles

En los últimos tiempos se ha comprobado un aumento del interés en la aplicación de las Redes Neuronales Artificiales a la previsión de series temporales, intentando explotar las indudables ventajas de estas herramientas. En este artículo se calculan previsiones de series no estacionarias o no invertibles, que presentan dificultades cuando se intentan pronosticar utilizando la metodología ARIMA de Box-Jenkins. Las ventajas de la aplicación de redes neuronales se aprecian con más claridad, cuando se trata de pronosticar sistemas multivariantes no estacionarios

Gynecological cancers: an alternative approach to healing

Grief and hope are two conflicting emotions that a patient recently diagnosed with cancer has to master. The real challenge for gynecologic oncologists is how to reach out. Conventional wisdom states that offering patients focus and belief when combating cancer in their lives allows them to embrace hope with greater confidence, which minimizes their grief. Three pictorial models are presented: ‘4-cusp approach’ model used at the initial consultation; ‘tapestry of bereavement or landscape of grief’ model at the postsurgery consultation; and ‘Venn-diagram’ model at any time during patient management. We have applied these models in our practice and believe that they can act as a fulcrum for the patient, the family and healthcare team around which therapy should be centered., Grief and hope are two emotions that a patient faces if diagnosed with cancer. The real challenge for the doctor is how to reach out and help the patient through this process. A doctor's role may be to offer focus and belief to the patient which may allow her to embrace hope with greater confidence. This will hopefully lessen the grief. We present three models which we believe can play a crucial part: ‘4-cusp approach’ used at the initial consultation; ‘tapestry of bereavement or landscape of grief model’ at the postsurgery consultation; and ‘Venn-diagram model’ at any time during care

Influenza A Virus Coding Regions Exhibit Host-Specific Global Ordered RNA Structure

Influenza A is a significant public health threat, partially because of its capacity to readily exchange gene segments between different host species to form novel pandemic strains. An understanding of the fundamental factors providing species barriers between different influenza hosts would facilitate identification of strains capable of leading to pandemic outbreaks and could also inform vaccine development. Here, we describe the difference in predicted RNA secondary structure stability that exists between avian, swine and human coding regions. The results predict that global ordered RNA structure exists in influenza A segments 1, 5, 7 and 8, and that ranges of free energies for secondary structure formation differ between host strains. The predicted free energy distributions for strains from avian, swine, and human species suggest criteria for segment reassortment and strains that might be ideal candidates for viral attenuation and vaccine development

Deep learning of the retina enables phenome- and genome-wide analyses of the microvasculature.

Background: The microvasculature, the smallest blood vessels in the body, has key roles in maintenance of organ health as well as tumorigenesis. The retinal fundus is a window for human in vivo non-invasive assessment of the microvasculature. Large-scale complementary machine learning-based assessment of the retinal vasculature with phenome-wide and genome-wide analyses may yield new insights into human health and disease. Methods: We utilized 97,895 retinal fundus images from 54,813 UK Biobank participants. Using convolutional neural networks to segment the retinal microvasculature, we calculated fractal dimension (FD) as a measure of vascular branching complexity, and vascular density. We associated these indices with 1,866 incident ICD-based conditions (median 10y follow-up) and 88 quantitative traits, adjusting for age, sex, smoking status, and ethnicity. Results: Low retinal vascular FD and density were significantly associated with higher risks for incident mortality, hypertension, congestive heart failure, renal failure, type 2 diabetes, sleep apnea, anemia, and multiple ocular conditions, as well as corresponding quantitative traits. Genome-wide association of vascular FD and density identified 7 and 13 novel loci respectively, which were enriched for pathways linked to angiogenesis (e.g., VEGF, PDGFR, angiopoietin, and WNT signaling pathways) and inflammation (e.g., interleukin, cytokine signaling). Conclusions: Our results indicate that the retinal vasculature may serve as a biomarker for future cardiometabolic and ocular disease and provide insights on genes and biological pathways influencing microvascular indices. Moreover, such a framework highlights how deep learning of images can quantify an interpretable phenotype for integration with electronic health records, biomarker, and genetic data to inform risk prediction and risk modification

Relationships among Polycyclic Aromatic Hydrocarbon–DNA Adducts, Proximity to the World Trade Center, and Effects on Fetal Growth

Polycyclic aromatic hydrocarbons (PAHs) are toxic pollutants released by the World Trade Center (WTC) fires and various urban combustion sources. Benzo[a]pyrene (BaP) is a representative member of the class of PAHs. PAH–DNA adducts, or BaP–DNA adducts as their proxy, provide a measure of chemical-specific genetic damage that has been associated with increased risk of adverse birth outcomes and cancer. To learn whether PAHs from the WTC disaster increased levels of genetic damage in pregnant women and their newborns, we analyzed BaP–DNA adducts in maternal (n = 170) and umbilical cord blood (n = 203) obtained at delivery from nonsmoking women who were pregnant on 11 September 2001 and were enrolled at delivery at three downtown Manhattan hospitals. The mean adduct levels in cord and maternal blood were highest among newborns and mothers who resided within 1 mi of the WTC site during the month after 11 September, intermediate among those who worked but did not live within this area, and lowest in those who neither worked nor lived within 1 mi (reference group). Among newborns of mothers living within 1 mi of the WTC site during this period, levels of cord blood adducts were inversely correlated with linear distance from the WTC site (p = 0.02). To learn whether PAHs from the WTC disaster may have affected birth outcomes, we analyzed the relationship between these outcomes and DNA adducts in umbilical cord blood, excluding preterm births to reduce variability. There were no independent fetal growth effects of either PAH–DNA adducts or environmental tobacco smoke (ETS), but adducts in combination with in utero exposure to ETS were associated with decreased fetal growth. Specifically, a doubling of adducts among ETS-exposed subjects corresponded to an estimated average 276-g (8%) reduction in birth weight (p = 0.03) and a 1.3-cm (3%) reduction in head circumference (p = 0.04). The findings suggest that exposure to elevated levels of PAHs, indicated by PAH–DNA adducts in cord blood, may have contributed to reduced fetal growth in women exposed to the WTC event

Effect of diindolylmethane supplementation on low-grade cervical cytological abnormalities: double-blind, randomised, controlled trial

This work was supported by a Cancer Research UK project grant (C8162/A4609 project costs) and a programme grant (C8162/A10406)

Artificial Intelligence in Supply Chain Operations Planning: Collaboration and Digital Perspectives

[EN] Digital transformation provide supply chains (SCs) with extensive accurate data that should be combined with analytical techniques to improve their management. Among these techniques Artificial Intelligence (AI) has proved their suitability, memory and ability to manage uncertain and constantly changing information. Despite the fact that a number of AI literature reviews exist, no comprehensive review of reviews for the SC operations planning has yet been conducted. This paper aims to provide a comprehensive review of AI literature reviews in a structured manner to gain insights into their evolution in incorporating new ICTs and collaboration. Results show that hybrization man-machine and collaboration and ethical aspects are understudied.This research has been funded by the project entitled NIOTOME (Ref. RTI2018-102020-B-I00) (MCI/AEI/FEDER, UE). 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