Machine Learning Improves Risk Stratification in Myelodysplastic Neoplasms: An Analysis of the Spanish Group of Myelodysplastic Syndromes

Machine learning; Risk stratification; Myelodysplastic neoplasmsAprendizaje automático; Estratificación del riesgo; Neoplasias mielodisplásicasAprenentatge automàtic; Estratificació del risc; Neoplàsies mielodisplàstiquesMyelodysplastic neoplasms (MDS) are a heterogeneous group of hematological stem cell disorders characterized by dysplasia, cytopenias, and increased risk of acute leukemia. As prognosis differs widely between patients, and treatment options vary from observation to allogeneic stem cell transplantation, accurate and precise disease risk prognostication is critical for decision making. With this aim, we retrieved registry data from MDS patients from 90 Spanish institutions. A total of 7202 patients were included, which were divided into a training (80%) and a test (20%) set. A machine learning technique (random survival forests) was used to model overall survival (OS) and leukemia-free survival (LFS). The optimal model was based on 8 variables (age, gender, hemoglobin, leukocyte count, platelet count, neutrophil percentage, bone marrow blast, and cytogenetic risk group). This model achieved high accuracy in predicting OS (c-indexes; 0.759 and 0.776) and LFS (c-indexes; 0.812 and 0.845). Importantly, the model was superior to the revised International Prognostic Scoring System (IPSS-R) and the age-adjusted IPSS-R. This difference persisted in different age ranges and in all evaluated disease subgroups. Finally, we validated our results in an external cohort, confirming the superiority of the Artificial Intelligence Prognostic Scoring System for MDS (AIPSS-MDS) over the IPSS-R, and achieving a similar performance as the molecular IPSS. In conclusion, the AIPSS-MDS score is a new prognostic model based exclusively on traditional clinical, hematological, and cytogenetic variables. AIPSS-MDS has a high prognostic accuracy in predicting survival in MDS patients, outperforming other well-established risk-scoring systems

Dissociation of a Strong Acid in Neat Solvents: Diffusion Is Observed after Reversible Proton Ejection Inside the Solvent Shell

This is the peer-reviewed version of the following article: The Journal of Physical Chemistry B 2013, 117, 14065–14078, DOI: 10.1021/jp4042765, which has been published in final form at https://pubs.acs.org/doi/abs/10.1021/jp4042765. This article may be used for non-commercial purposes onlyStrong-acid dissociation was studied in alcohols. Optical excitation of the cationic photoacid N-methyl-6-hydroxyquinolinium triggers proton transfer to the solvent, which was probed by spectral reconstruction of picosecond fluorescence traces. The process fulfills the classical Eigen–Weller mechanism in two stages: (a) solvent-controlled reversible dissociation inside the solvent shell and (b) barrierless splitting of the encounter complex. This can be appreciated only when fluorescence band integrals are used to monitor the time evolution of the reactant and product concentrations. Band integrals are insensitive to solvent dynamics and report relative concentrations directly. This was demonstrated by first measuring the fluorescence decay of the conjugate base across the full emission band, independently of the proton-transfer reaction. Multiexponential decay curves at single wavelengths result from a dynamic red shift of fluorescence in the course of solvent relaxation, whereas clean single exponential decays are obtained if the band integral is monitored instead. The extent of the shift is consistent with previously reported femtosecond transient absorption measurements, continuum theory of solvatochromism, and molecular properties derived from quantum chemical calculations. In turn, band integrals show clean biexponential decay of the photoacid and triexponential evolution of the conjugate base in the course of the proton transfer to solvent reaction. The dissociation step follows the slowest stage of solvation, which was measured here independently by picosecond fluorescence spectroscopy in five aliphatic alcohols. Also, the rate constant of the encounter-complex splitting stage is compatible with proton diffusion. Thus, for this photoacid, both stages reach the highest possible rates: solvation and diffusion control. Under these conditions, the concentration of the encounter complex is substantial during the earliest nanosecondWe thank the Spanish Government and the European Regional Development Fund (grant nos. CTQ2010-17835, CTQ2010-17026, and CTQ2011-29311-C02-01) and the Xunta de Galicia (grants nos. CN 2012/314, 2012-PG237, GPC2013/052 and INCITE09 314 252 PR) for financial support of our work. J.L.P.L. thanks the Spanish Ministry of Economy and Competitiveness for funding through the Ramon y Cajal ́ Programm 2009. M.V. and C.C.B. thank the Spanish Government for funding through the FPU program. A. B. thanks the Segundo Gil Dávila Foundation for financial supportS

Análise estatística do mercado de aloxamento turístico da provincia de Ourense dende unha nova perspectiva: a análise de datos composicionais

O obxectivo principal deste traballo é analizar os datos recollidos na enquisa sobre a utilización das vivendas do uso turístico na provincia de Ourense dende o punto de vista composicional. Despois dun proceso de familiarización cos datos de natureza composicional, realizouse unha análise descritiva das variables que indicaban a repartición do gasto dos e das turistas que tiñan esta estrutura composicional. Na segunda etapa aplicouse un modelo de regresión loxística coa covarianza composicional. Finalmente, recolléronse os datos procedentes de fontes secundarias do Instituto Galego de Estatística para realizar unha análise da evolución temporal dunha serie composicional que representaba o número de persoas viaxeiras mensuais na provincia de Ourense. Os resultados obtidos indican que é recomendable ter en conta este tipo de estrutura de datos para acadar información sobre a repartición ou a interacción entre as posibles partes

Deposición de capas funcionales sobre esmaltes cerámicos mediante la técnica sol-gel (revisión)

Functionalized enamels for the ceramic tile industry has been a research topic since the past 15 years. Different researchers have focused their efforts on achieving surfaces with functional attributes that increase product value and provide technical solutions for the technological needs of our times. This article presents a review of the scientific literature dedicated to obtaining functional surfaces by means of a sol gel technique, that provides a means for deposition and formation of thin layers on traditional ceramic enamels in order to provide functional characteristics. The document presents: typical used alcoxidic solutions, the different deposition techniques emphasizing the experimental findings obtained by the authors, and presents a synthesis of the functional effects obtained by means of the technique.La funcionalización de esmaltes para la industria de las baldosas cerámicas ha sido un frente de investigación importante en los últimos 15 años. Diferentes investigadores han centrado sus esfuerzos en conseguir superficies con atributos funcionales que incrementen el valor agregado del producto y a su vez aporten respuestas a las necesidades tecnológicas de nuestros tiempos. El presente artículo se concentra en hacer una revisión de la literatura científica dedicada a la obtención de superficies funcionales por medio de la técnica de sol-gel, la cual es apta para la fabricación de soluciones que se depositan formando capas finas sobre los esmaltes de cerámica tradicional con el fin de aportar características funcionales al mismo. El documento presenta las principales soluciones alcóxidicas usualmente empleadas, las diferentes técnicas de deposición haciendo énfasis en los hallazgos experimentales obtenidos por los diferentes autores, y presenta una síntesis de los efectos funcionales hasta la fecha obtenidos por medio de la técnica

Ammonia levels in different kinds of sampling sites in the central Iberian Peninsula

Ponencia presentada en:2nd Iberian Meeting on Aerosol Science and Technology (RICTA 2014) celebrado en Tarragona del 7 al 9 de julio de 2014.Ammonia is the Secondary Inorganic Aerosol (SIC) gaseous precursor which has been studied to a lesser extent in the Madrid Metropolitan Area up to date. A study conducted in the city of Madrid with the aim of characterizing levels of ammonia took place in 2011. These campaigns formed part of a larger study conducted in 6 Spanish cities. A time series of weekly integrated ammonia measurements available at an EMEP rural site (Campisábalos) has been used to obtain information on the ammonia rural background in the region. The results point to traffic and waste treatment plants as the main ammonia sources in Madrid. Relevant seasonal differences have not been observed in the Metropolitan Area. The explanation can be related to the fall in the rural background levels during July 2011, which might conceal urban summer emission increases observed in other cities

Proportionate flow shop games

htmlabstractIn a proportionate flow shop problem several jobs have to be processed through a fixed sequence of machines and the processing time of each job is equal on all machines. By identifying jobs with agents, whose costs linearly depend on the completion time of their jobs, and assuming an initial processing order on the jobs, we face two problems: the first one is how to obtain an optimal order that minimizes the total processing cost, the second one is how to allocate the cost savings obtained by ordering the jobs optimally. In this paper we focus on the allocation problem. PFS games are defined as cooperative games associated to proportionate flow shop problems. It is seen that PFS games have a nonempty core. Moreover, it is shown that PFS games are convex if the jobs are initially ordered in decreasing urgency. For this case an explicit game independent expression for the Shapley value is provid

Improved personalized survival prediction of patients with diffuse large B-cell Lymphoma using gene expression profiling

BACKGROUND: Thirty to forty percent of patients with Diffuse Large B-cell Lymphoma (DLBCL) have an adverse clinical evolution. The increased understanding of DLBCL biology has shed light on the clinical evolution of this pathology, leading to the discovery of prognostic factors based on gene expression data, genomic rearrangements and mutational subgroups. Nevertheless, additional efforts are needed in order to enable survival predictions at the patient level. In this study we investigated new machine learning-based models of survival using transcriptomic and clinical data. METHODS: Gene expression profiling (GEP) of in 2 different publicly available retrospective DLBCL cohorts were analyzed. Cox regression and unsupervised clustering were performed in order to identify probes associated with overall survival on the largest cohort. Random forests were created to model survival using combinations of GEP data, COO classification and clinical information. Cross-validation was used to compare model results in the training set, and Harrel's concordance index (c-index) was used to assess model's predictability. Results were validated in an independent test set. RESULTS: Two hundred thirty-three and sixty-four patients were included in the training and test set, respectively. Initially we derived and validated a 4-gene expression clusterization that was independently associated with lower survival in 20% of patients. This pattern included the following genes: TNFRSF9, BIRC3, BCL2L1 and G3BP2. Thereafter, we applied machine-learning models to predict survival. A set of 102 genes was highly predictive of disease outcome, outperforming available clinical information and COO classification. The final best model integrated clinical information, COO classification, 4-gene-based clusterization and the expression levels of 50 individual genes (training set c-index, 0.8404, test set c-index, 0.7942). CONCLUSION: Our results indicate that DLBCL survival models based on the application of machine learning algorithms to gene expression and clinical data can largely outperform other important prognostic variables such as disease stage and COO. Head-to-head comparisons with other risk stratification models are needed to compare its usefulness