Herramienta para analizar matrices de expresión génicas con machine learning

En el campo de las aplicaciones biomédicas, es tan importante obtener una alta precisión como hacer que los modelos generados sean explicables para el personal clínico. Por esta razón, es esencial aplicar técnicas inteligentes que sean capaces de aprender de manera efectiva en estos escenarios. En esta ocasión se trata de crear un software en R para proporcionar una manera sencilla de construir un análisis explicativo de la causalidad entre la expresión génica y las condiciones del paciente. El software creado está muy automatizado facilitando las entradas de datos para estudiar diferentes matrices de expresión, con un flujo lineal, con una lectura de datos a través del código GEO, un preprocesamiento en el que se facilita un contraste de hipótesis,una normalización para hacer los datos comparables entre ellos y un filtrado de genes que reduce el cálculo computacional del posterior entrenamiento de los modelos machine learning el cual conlleva diferentes técnicas de selección de genes para, a través de la validación del modelo, detectar la relación entre la expresión génica y la condición del paciente y compartir los resultados de los genes realmente implicados en la respuesta Pongo a prueba esta herramienta con uno de los temas mas actuales en cuanto a diagnostico clínico, la detección del cáncer a través de la expresión génica de las plaquetas. Los datos se han obtenido del experimento con código GSE89843. Se obtienen AUC por encima del 90% con tan solo 10 genes, lo que supone un gran avance en este campo. El AUC se puede interpretar como la probabilidad de clasificarlos correctamente. Debido a su bajo coste por el número reducido de genes y su poca invasividad puede realizarse a modo de test preventivo y reducir su tasa de mortalidad.In the field of biomedical applications, it is as important to obtain high precision as to make the generated models explainable to clinical staff. For this reason, it is essential to apply intelligent techniques that are able to learn effectively in these scenarios. This time it is about creating software in R to provide a simple way to construct an explanatory analysis of the causality between gene expression and patient conditions. The software created is highly automated, facilitating data entry to study different expression matrices, with a linear flow, with a reading of data through the GEO code, a preprocessing in which a hypothesis contrast is facilitated, a normalization to make the comparable data between them and a gene filtration that reduces the computational calculation of the subsequent training of machine learning models which entails different gene selection techniques to, through the validation of the model, detect the relationship between gene expression and the patient's condition and share the results of the genes really involved in the response I test this tool with one of the most current issues in terms of clinical diagnosis, the detection of cancer through the gene expression of platelets. The data were obtained from the experiment with code GSE89843. AUC above 90% are obtained with only 10 genes, which is a great advance in this field. The AUC can be interpreted as the probability of classifying them correctly. Due to its low cost due to the reduced number of genes and its low invasiveness, it can be carried out as a preventive test and reduce its mortality rate.En el camp de les aplicacions biomèdiques, és tan important obtenir una alta precisió com fer que els models generats siguin explicables per al personal clínic. Per aquesta raó, és essencial aplicar tècniques intel·ligents que siguin capaces d'aprendre de manera efectiva en aquests escenaris. En aquesta ocasió es tracta de crear un programari en R per a proporcionar una manera senzilla de construir una anàlisi explicativa de la causalitat entre l'expressió gènica i les condicions del pacient. El programari creat està molt automatitzat facilitant les entrades de dades per a estudiar diferents matrius d'expressió, amb un flux lineal, amb una lectura de dades a través del codi GEO, un preprocesamiento en el qual es facilita un contrast d'hipòtesi,una normalització per a fer les dades comparables entre ells i un filtrat de gens que redueix el càlcul computacional del posterior entrenament dels models machine learning el qual comporta diferents tècniques de selecció de gens per a, a través de la validació del model, detectar la relació entre l'expressió gènica i la condició del pacient i compartir els resultats dels gens realment implicats en la resposta. Poso a prova aquesta eina amb un dels temes mes actuals quant a diagnostico clínic, la detecció del càncer a través de l'expressió gènica de les plaquetes. Les dades s'han obtingut de l'experiment amb codi GSE89843. S'obtenen AUC per sobre del 90% amb tan sols 10 gens, la qual cosa suposa un gran avanç en aquest camp. El AUC es pot interpretar com la probabilitat de classificar-los correctament. A causa del seu baix cost pel nombre reduït de gens i la seva poca invasividad pot realitzar-se a manera de test preventiu i reduir la seva taxa de mortalitat

Fiscal data revisions in Europe

Public deficit figures are subject to revisions, as most macroeconomic aggregates are. Nevertheless, in the case of Europe, the latter could be particularly worrisome given the role of fiscal data in the functioning of EU’s multilateral surveillance rules. Adherence to such rules is judged upon initial releases of data, in the framework of the so-called Excessive Deficit Procedure (EDP) Notifications. In addition, the lack of reliability of fiscal data may hinder the credibility of fiscal consolidation plans. In this paper we document the empirical properties of revisions to annual government deficit figures in Europe by exploiting the information contained in a pool of real-time vintages of data pertaining to fifteen EU countries over the period 1995-2008. We build up such real-time dataset from official publications. Our main findings are as follows: (i) preliminary deficit data releases are biased and non-efficient predictors of subsequent releases, with later vintages of data tending to show larger deficits on average; (ii) such systematic bias in deficit revisions is a general feature of the sample, and cannot solely be attributed to the behaviour of a small number of countries, even though the Greek case is clearly an outlier; (iii) Methodological improvements and clarifications stemming from Eurostat’s decisions that may lead to data revisions explain a significant share of the bias, providing some evidence of window dressing on the side of individual countries; (iv) expected real GDP growth, political cycles and the strength of fiscal rules also contribute to explain revision patterns; (v) nevertheless, if the systematic bias is excluded, revisions can be considered rational after two years. JEL Classification: E01, E21, E24, E31, E5, H600data revisions, fiscal statistics, news and noise, Rationality, real-time data

Nanoporous PMMA: A novel system with different acoustic properties

The acoustic properties of closed cell nanoporous and microporous poly(methyl methacrylate) (PMMA) foams have been well characterized, showing that nanoporous PMMA exhibit a different absorption coefficient and transmission loss behavior in comparison with microporous PMMA. Experimental differences may be explained by the different wave propagation mechanism in the micro and nanoscale, which is determined by the confinement of both the gas (Knudsen regime) and the solid phases. These results place nanoporous materials as a new class of polymeric porous material with potential properties in the field of acoustics, especially in multifunctional systems requiring a certain degree of soundproofing

Nanostructures with Group IV nanocrystals obtained by LPCVD and thermal annealing of SiGeO layers

Nanocrystals embedded in an oxide matrix have been fabricated by annealing SiGeO films deposited by LPCVD. The composition of the oxide layers and its evolution after annealing as well as the presence and nature of nanocrystals in the films have been studied by several experimental techniques. The results are analyzed and discussed in terms of the main deposition parameters and the annealing temperature

Integrable systems with BMS3_{3} Poisson structure and the dynamics of locally flat spacetimes

We construct a hierarchy of integrable systems whose Poisson structure corresponds to the BMS$_{3}$ algebra, and then discuss its description in terms of the Riemannian geometry of locally flat spacetimes in three dimensions. The analysis is performed in terms of two-dimensional gauge fields for $isl(2,R)$. Although the algebra is not semisimple, the formulation can be carried out \`a la Drinfeld-Sokolov because it admits a nondegenerate invariant bilinear metric. The hierarchy turns out to be bi-Hamiltonian, labeled by a nonnegative integer $k$, and defined through a suitable generalization of the Gelfand-Dikii polynomials. The symmetries of the hierarchy are explicitly found. For $k\geq 1$, the corresponding conserved charges span an infinite-dimensional Abelian algebra without central extensions, and they are in involution; while in the case of $k=0$, they generate the BMS$_{3}$ algebra. In the special case of $k=1$, by virtue of a suitable field redefinition and time scaling, the field equations are shown to be equivalent to a specific type of the Hirota-Satsuma coupled KdV systems. For $k\geq 1$, the hierarchy also includes the so-called perturbed KdV equations as a particular case. A wide class of analytic solutions is also explicitly constructed for a generic value of $k$. Remarkably, the dynamics can be fully geometrized so as to describe the evolution of spacelike surfaces embedded in locally flat spacetimes. Indeed, General Relativity in 3D can be endowed with a suitable set of boundary conditions, so that the Einstein equations precisely reduce to the ones of the hierarchy aforementioned. The symmetries of the integrable systems then arise as diffeomorphisms that preserve the asymptotic form of the spacetime metric, and therefore, they become Noetherian. The infinite set of conserved charges is recovered from the corresponding surface integrals in the canonical approach.Comment: 34 pages, 2 figure

Risk-Based Framework for the Integration of RPAS in Non-Segregated Airspace

Remotely Piloted Aircraft Systems (RPAS) are new airspace users that require to be safely integrated into the non-segregated airspace. Currently, their integration is planned for the horizon 2025, but there is a lot of pressure by RPAS operators to fly as soon as possible. This research focuses on the development of a risk-based framework for the integration of RPAS in non-segregated airspace. The risk-based framework relies on a hierarchical methodology that is split into two time horizons: design and operation. Different operational and geometrical factors characterise each stage. Then, a set of risk and operational indicators are defined for each stage. These indicators evaluate the operational airspace state and provide information about how the integration of RPAS should be. Primary results provide information about geographical and temporary restrictions. Geographical restrictions refer to the airways that favour or inhibit the integration of RPAS, and temporary restrictions denote the time span when the RPAS can pierce into the airspace

Opportunities and Challenges of Implementing P2P Streaming Applications in the Web

P2P applications are increasingly present on the web. We have identified a gap in current proposals when it comes to the use of traditional P2P overlays for real-time multimedia streaming. We analyze the possibilities and challenges to extend WebRTC in order to implement JavaScript APIs for P2P streaming algorithms