CONTROL NUMÉRICO COMPUTARIZADO UTILIZANDO INTERPOLACIÓN LINEAL PARA AUTONIVELAR LA SUPERFICIE DE TRABAJO EN UNA CNC (COMPUTERIZED NUMERICAL CONTROL USING LINEAR INTERPOLATION TO SELF-LEVEL THE WORKING SURFACE IN A CNC)

En este trabajo se presenta la implementación de una máquina CNC (Control Numérico Computarizado, por sus siglas en español) de bajo costo que utiliza un algoritmo de autonivelación para mejorar el desempeño del maquinado. Mediante el algoritmo se puede corregir por software el desnivel de la cama de fresado independientemente de las imperfecciones del material que se va a maquinar. Básicamente lo que hace el software es deformar el dibujo que se imprimirá de tal manera que este compense la deformación del material. Se utiliza una tarjeta de desarrollo “Arduino UNO” para capturar los datos de una sonda de contacto, posteriormente la información obtenida será procesada por una computadora, que a su vez enviará los datos procesados al Arduino para que este accione los motores. Se utilizó NetBeans para el desarrollo de la plataforma del usuario y el firmware GRBL (para Arduino) como lenguaje de programación, ambos de código libre, además el software Matlab es utilizado para realizar las simulaciones del código. Los resultados muestran que el uso del algoritmo de autonivelación efectivamente mejora el proceso de maquinado.This paper presents the implementation of a CNC machine (Computerized Numerical Control, for its acronym in Spanish) of low cost that uses a self-leveling algorithm to improve the machining performance. By means of the algorithm, the unevenness of the milling bed can be corrected, by software, independently of the imperfections of the material to be machined. Basically, what the software does is to deform the drawing that will be printed in such a way that it compensates for the deformation of the material. An "Arduino UNO" board is used to capture the data from a contact probe, then the information obtained will be processed by a computer, which in turn will send the processed data to the Arduino, so that it drives the motors. NetBeans was used for the development of the user platform and the GRBL firmware (for Arduino) as programming language, both of them are free code, in addition the Matlab software is used to perform the code simulations. The results show that the use of the self-leveling algorithm improves the machining process

Stem cell-like transcriptional reprogramming mediates metastatic resistance to mTOR inhibition

Inhibitors of the mechanistic target of rapamycin (mTOR) are currently used to treat advanced metastatic breast cancer. However, whether an aggressive phenotype is sustained through adaptation or resistance to mTOR inhibition remains unknown. Here, complementary studies in human tumors, cancer models and cell lines reveal transcriptional reprogramming that supports metastasis in response to mTOR inhibition. This cancer feature is driven by EVI1 and SOX9. EVI1 functionally cooperates with and positively regulates SOX9, and promotes the transcriptional upregulation of key mTOR pathway components (REHB and RAPTOR) and of lung metastasis mediators (FSCN1 and SPARC). The expression of EVI1 and SOX9 is associated with stem cell-like and metastasis signatures, and their depletion impairs the metastatic potential of breast cancer cells. These results establish the mechanistic link between resistance to mTOR inhibition and cancer metastatic potential, thus enhancing our understanding of mTOR targeting failure

The risk of COVID-19 death is much greater and age dependent with type I IFN autoantibodies

International audienceSignificance There is growing evidence that preexisting autoantibodies neutralizing type I interferons (IFNs) are strong determinants of life-threatening COVID-19 pneumonia. It is important to estimate their quantitative impact on COVID-19 mortality upon SARS-CoV-2 infection, by age and sex, as both the prevalence of these autoantibodies and the risk of COVID-19 death increase with age and are higher in men. Using an unvaccinated sample of 1,261 deceased patients and 34,159 individuals from the general population, we found that autoantibodies against type I IFNs strongly increased the SARS-CoV-2 infection fatality rate at all ages, in both men and women. Autoantibodies against type I IFNs are strong and common predictors of life-threatening COVID-19. Testing for these autoantibodies should be considered in the general population

The risk of COVID-19 death is much greater and age dependent with type I IFN autoantibodies

International audienceSignificance There is growing evidence that preexisting autoantibodies neutralizing type I interferons (IFNs) are strong determinants of life-threatening COVID-19 pneumonia. It is important to estimate their quantitative impact on COVID-19 mortality upon SARS-CoV-2 infection, by age and sex, as both the prevalence of these autoantibodies and the risk of COVID-19 death increase with age and are higher in men. Using an unvaccinated sample of 1,261 deceased patients and 34,159 individuals from the general population, we found that autoantibodies against type I IFNs strongly increased the SARS-CoV-2 infection fatality rate at all ages, in both men and women. Autoantibodies against type I IFNs are strong and common predictors of life-threatening COVID-19. Testing for these autoantibodies should be considered in the general population