(Des)igualdad de oportunidades y movilidad social

En la filosofía y economía normativas se ha alcanzado un consenso amplio acerca de las exigencias de la justicia distributiva, según el cual los poderes públicos deberían intervenir para corregir las desigualdades debidas a factores irrelevantes desde un punto de vista moral como nuestro origen social, pero dejar intactas las desigualdades debidas a nuestro esfuerzo. Es decir, el objetivo de la acción del poder público debería ser la igualdad de oportunidades, no de resultados. Tras un desbroce analítico donde se distinguen dos concepciones muy distintas de la igualdad de oportunidades, el artículo concluye que en las sociedades contemporaneas existe un amplio espacio para la justificación de políticas tendentes a la igualación de resultados, incluso si nuestro objetivo es la igualdad de oportunidadesDrawing primarily on the normative ethics and welfare economics literature on equality of opportunity, there is now widespread consensus on the basic principle of distributive justice: inequalities due to circumstances beyond people’s control are unfair (and should be compensated for), while inequalities due to differing efforts are not seen as having ethical or policy salience. That is to say governments should pursue equality of opportunity, not equality of outcome. After an analytical distinction where two very different conceptions of equality of opportunity are discussed, the article concludes that in today’s societies there is broad justification for (some) equality of outcome – even if our goal is equality of opportunit

Liberalismo económico y darwinismo social. Sobre la figura de Herbert Spencer

El trabajo analiza las razones para la acción. A partir de la distinción entre los enfoques de decisión racional y altruismo recíproco, el artículo examina sucesivamente ambos modelos, concluyendo con una breve observación sobre el poder de las profecías que se auto-cumplen. El texto argumenta que el enfoque de la decisión racional se basa en suposiciones erróneas sobre la naturaleza humana y, por consiguiente, conduce a predicciones igualmente equivocadasThis is an article about reasons for action. Starting from the distinction between rational choice and reciprocal altruism, the article successively develops both approaches, concluding with a remark on the worrisome possibility that inaccurate portrayals of human nature may prove self-reinforcing. The article argues that rational choice approach is based on inaccurate behavioral assumptions and consequently leads to inaccurate predictions

Ideafix: a decision tree-based method for the refinement of variants in FFPE DNA sequencing data

Increasingly, treatment decisions for cancer patients are being made from next-generation sequencing results generated from formalin-fixed and paraffin-embedded (FFPE) biopsies. However, this material is prone to sequence artefacts that cannot be easily identified. In order to address this issue, we designed a machine learning-based algorithm to identify these artefacts using data from >1 600 000 variants from 27 paired FFPE and fresh-frozen breast cancer samples. Using these data, we assembled a series of variant features and evaluated the classification performance of five machine learning algorithms. Using leave-one-sample-out cross-validation, we found that XGBoost (extreme gradient boosting) and random forest obtained AUC (area under the receiver operating characteristic curve) values >0.86. Performance was further tested using two independent datasets that resulted in AUC values of 0.96, whereas a comparison with previously published tools resulted in a maximum AUC value of 0.92. The most discriminating features were read pair orientation bias, genomic context and variant allele frequency. In summary, our results show a promising future for the use of these samples in molecular testing. We built the algorithm into an R package called Ideafix (DEAmination FIXing) that is freely available at https://github.com/mmaitenat/ideafix

Penalized Partial Least Square applied to structured data

Nowadays, data analysis applied to high dimension has arisen. The edification of high-dimensional data can be achieved by the gathering of different independent data. However, each independent set can introduce its own bias. We can cope with this bias introducing the observation set structure into our model. The goal of this article is to build theoretical background for the dimension reduction method sparse Partial Least Square (sPLS) in the context of data presenting such an observation set structure. The innovation consists in building different sPLS models and linking them through a common-Lasso penalization. This theory could be applied to any field, where observation present this kind of structure and, therefore, improve the sPLS in domains, where it is competitive. Furthermore, it can be extended to the particular case, where variables can be gathered in given a priori groups, where sPLS is defined as a sparse group Partial Least Square

PerMallows: An R Package for Mallows and Generalized Mallows Models

In this paper we present the R package PerMallows, which is a complete toolbox to work with permutations, distances and some of the most popular probability models for permutations: Mallows and the Generalized Mallows models. The Mallows model is an exponential location model, considered as analogous to the Gaussian distribution. It is based on the definition of a distance between permutations. The Generalized Mallows model is its best-known extension. The package includes functions for making inference, sampling and learning such distributions. The distances considered in PerMallows are Kendall's τ , Cayley, Hamming and Ulam

Adquisición de datos desde plataforma IOT2040. Protocolos MODBUS/TCP y OPC UA

Este trabajo se basa en la adquisición de datos de un proceso automatizados desde dispositivo SIMATIC IOT2040 de SIEMENS, con diferentes protocolos industriales, mediante una plataforma de programación (Node-RED). Así, se posibilita la lectura, envió y procesamiento de datos de un proceso en un servidor remoto, facilitando la implementación de la industria 4.0 a través del Internet de las Cosas (IoT) a cualquier proceso automatizado futuro.Lan hau automatizazio prozesu datuen eskuratzean oinarritzen da SIMATIC IOT2040 gailuaren bidez, protokolo industrial desberdinekin, programazio plataforma baten (Node-RED) bitartez. Horrela, prozesuko datuen irakurketa, bidalketa eta prozesaketa zerbitzari urrun batean posible egiten da, 4.0 industriaren inplementazioa erraztuz Gauzen Internetaren (IoT) bidez etorkizuneko edozein automatizazio prozesutarako.This work is based on the acquisition of data from an automated process from SIEMENS SIMATIC IoT2040 device with different industrial protocols through a programming platform (Node-RED). Thus, it is possible to read, send and process data from a process ina remote server, facilitating the implementation of industry 4.0 through the Internet of Things(IoT)to any future automated process

Ideafix: a decision tree-based method for the refinement of variants in FFPE DNA sequencing data

[EN]Increasingly, treatment decisions for cancer patients are being made from next-generation sequencing results generated from formalin-fixed and paraffin-embedded (FFPE) biopsies. However, this material is prone to sequence artefacts that cannot be easily identified. In order to address this issue, we designed a machine learning-based algorithm to identify these artefacts using data from >1600000 variants from 27 paired FFPE and fresh-frozen breast cancer samples. Using these data, we assembled a series of variant features and evaluated the classification performance of five machine learning algorithms. Using leave-one-sample-out cross-validation, we found that XGBoost (extreme gradient boosting)and random forest obtained AUC (area under the receiver operating characteristic curve) values >0.86. Performance was further tested using two independent datasets that resulted in AUC values of 0.96, whereas a comparison with previously published tools resulted in a maximum AUC value of 0.92. The most discriminating features were read pair orientation bias, genomic context and variant allele frequency. In summary, our results show a promising future for the use of these samples in molecular testing. We built the algorithm into an R package called Ideafix (DEAmination FIXing) that is freely available at https://github.com/mmaitenat/ideafix.Departamento de Educaci ́on, Universidades e Investi- gaci ́on of the Basque Government [PRE 2019 2 0211 to M.T.A]; Ikerbasque, Basque Foundation for Science [to C.L.]; Starmer–Smith Memorial Fund [to C.L.]; Ministerio de Econom ́ıa, Industria y Competitividad (MINECO) of the Spanish Central Government [to C.L., PID2019- 104933GB-10 to B.C.]; ISCIII and FEDER Funds [PI12/00663, PIE13/00048, DTS14/00109, PI15/00275 and PI18/01710 to C.L.]; Departamento de Desarrollo Econ ́omico y Competitividad and Departamento de Sanidad of the Basque Government [to C.L.]; Aso- ciaci ́on Espa ̃nola Contra el Cancer (AECC) [to C.L.]; Diputaci ́on Foral de Guipuzcoa (DFG) [to C.L.]; Depar- tamento de Industria of the Basque Government [ELKA- RTEK Programme, project code: KK-2018/00038 to C.L., ELKARTEK Programme, project code: KK-2020/00049 to B.C., IT-1244-19 to B.C.