Diagnosis of clonality of chronic lymphoproliferative disorders of T and NK cells

Tesis por compendio de publicaciones[ES]En el presente trabajo de tesis doctoral nos planteamos como objetivo general mejorar las estrategias de diagnóstico de clonalidad de los distintos SLPC‐T/NK, y de evaluación pronóstica en el caso particular de las LLGG, para su implementación rutinaria en un contexto clínico (es decir, fácil de implementar, reproducible, sensible y específico). Para ello, nos planteamos tres objetivos específicos: 1. ‐ Optimizar y validar el ensayo de CMF con el anticuerpo anti‐TRBC1 para la detección de células clonales Tαβ, mediante: 1a) la estandarización del protocolo de marcaje de TRBC1; 1b) la definición del patrón de expresión de TRBC1 de las células Tαβ normales totales y de las principales subpoblaciones T, como referencia de la normalidad, incluyendo el análisis por familias TCRVβ y por estadio madurativo T; y 1c) la evaluación de la sensibilidad y especificidad analítica de la técnica. 2. ‐ Validar la utilidad del ensayo (ya optimizado) de CMF con el anticuerpo anti‐TRBC1 en la detección de clonalidad Tαβ en el caso particular de las expansiones de linfocitos grandes granulares. 3. ‐ Evaluar la frecuencia y tipo de mutaciones en los genes STAT3 y STAT5B en todos los subtipos de LLGGT/SLPC‐NK, con la finalidad de establecer su utilidad diagnóstica (incluido el diagnóstico de clonalidad T y, sobre todo, NK) y valorar su impacto pronóstico, a través de la asociación de la presencia de estas mutaciones con las características biológicas y clínicas de la enfermedad, y la evolución de los pacientes

RHOASo: An Early Stop Hyper-Parameter Optimization Algorithm

[EN] This work proposes a new algorithm for optimizing hyper-parameters of a machine learning algorithm, RHOASo, based on conditional optimization of concave asymptotic functions. A comparative analysis of the algorithm is presented, giving particular emphasis to two important properties: the capability of the algorithm to work efficiently with a small part of a dataset and to finish the tuning process automatically, that is, without making explicit, by the user, the number of iterations that the algorithm must perform. Statistical analyses over 16 public benchmark datasets comparing the performance of seven hyper-parameter optimization algorithms with RHOASo were carried out. The efficiency of RHOASo presents the positive statistically significant differences concerning the other hyper-parameter optimization algorithms considered in the experiments. Furthermore, it is shown that, on average, the algorithm needs around 70% of the iterations needed by other algorithms to achieve competitive performance. The results show that the algorithm presents significant stability regarding the size of the used dataset partition.SIInstituto Nacional de Cibersegurida

Partitions, diophantine equations, and control systems

[EN] Ordered partitions of elements of a reduced abelian monoid are defined and studied by means of the solutions of linear diophantine equations. Links to feedback classification of linear dynamical systems over certain commutative rings are given in the same way as partitions of integers are related to feedback classification of linear dynamical systems over fields in the classical literature.S

On the State Approach Representations of Convolutional Codes over Rings of Modular Integers

[EN] In this study, we prove the existence of minimal first-order representations for convolutional codes with the predictable degree property over principal ideal artinian rings. Further, we prove that any such first-order representation leads to an input/state/output representation of the code provided the base ring is local. When the base ring is a finite field, we recover the classical construction, studied in depth by J. Rosenthal and E. V. York. This allows us to construct observable convolutional codes over such rings in the same way as is carried out in classical convolutional coding theory. Furthermore, we prove the minimality of the obtained representations. This completes the study of the existence of input/state/output representations of convolutional codes over rings of modular integers.S

Phenol and nitrogen removal in microalgal‐bacterial granular sequential batch reactors

Producción CientíficaBACKGROUND The microalgal-bacterial systems work on the principle of the symbiotic relationship between algae and bacteria. The ability of algal-bacterial photobioreactors for the treatment of wastewater containing ammonia and phenol has been poorly addressed. In this work a self-sustaining synergetic microalgal-bacterial granular sludge process was thus developed to treatment of industrial wastewater based upon the low cost of photosynthetic oxygenation and the simultaneous phenol and nitrogen removal. The performance of a conventional sequential batch reactor (SBR) based on aerobic bacterial communities (SBRB) and a microalgal-bacterial granular SBR (SBRMB) were comparatively assessed. The major challenges associated with microalgal-bacterial systems have been discussed. RESULTS A complete removal of phenol (100 mg L-1) was achieved in both reactors. The reactors SBRB and SBRMB showed similar performance in term of removal of inorganic nitrogen. Nitrogen mass balances estimated nitrogen assimilation, nitrification and denitrification. Higher simultaneous nitrification and denitrification (70% SND) occurred in SBRB as determined by mass balances. The higher nitrogen assimilation (17.9%) by the microalgal-bacterial biomass compensated the lower denitrifying activity in SBRMB (54% SND), resulting in a removal of inorganic nitrogen (61%) similar to that obtained in SBRB (66%). N2O was not detected in the headspace of any system. CONCLUSIONS Granular microalgae-bacterial consortia implemented in SBR constitute an efficient method for industrial wastewater treatment achieving complete removal of ammonia and phenol. The application of SBRMB would be more cost-effective than SBRB mainly due to the significant energy savings in SBRMB resulting in a sustainable system that contributes to the circular bioeconomy.Junta de Castilla y León y la UE-FEDER (grant numbers CLU 2017-09, CL-EI-2021-07 and UIC 315

Research-oriented simulated teaching: una experiencia innovadora para el desarrollo de competencias de investigación científica

Accésit 2019[ES] El Espacio Europeo de Educación Superior demanda nuevas estrategias docentes que desarrollen una formación en competencias que permita a sus estudiantes enfrentarse con éxito a su futuro social y profesional. En el caso del campo científico-tecnológico, la actualidad profesional se encuentra ligada a la aparición de problemas abiertos que implican retos enmarcados en contextos de Investigación, Desarrollo e Innovación (I+D+I), donde el conocimiento técnico es esencial, pero la adquisición de competencias relacionadas con la actividad investigadora es clave para poder resolver los problemas surgidos de una manera óptima y eficaz. En este contexto, la universidad ha de ejercer su cometido académico y enriquecer su función social mediante la formación en competencias investigativas con la finalidad de que el alumnado adquiera las capacidades necesarias para mantener a la sociedad en la vanguardia del conocimiento. En este contexto, y tomando como referencia las competencias de investigación que queramos priorizar, se ha de determinar qué enfoques pedagógicos mantienen la coherencia entre el diseño, la metodología, la evaluación y los objetivos a alcanzar

Effect of the Sampling of a Dataset inthe Hyperparameter Optimization Phase over the Efficiency ofa Machine Learning Algorithm

[EN] Selecting the best configuration of hyperparameter values for a Machine Learning model yields directly in the performance of the model on the dataset. It is a laborious task that usually requires deep knowledge of the hyperparameter optimizations methods and the Machine Learning algorithms. Although there exist several automatic optimization techniques, these usually take significant resources, increasing the dynamic complexity in order to obtain a great accuracy. Since one of the most critical aspects in this computational consume is the available dataset, among others, in this paper we perform a study of the effect of using different partitions of a dataset in the hyperparameter optimization phase over the efficiency of a Machine Learning algorithm. Nonparametric inference has been used to measure the rate of different behaviors of the accuracy, time, and spatial complexity that are obtained among the partitions and the whole dataset. Also, a level of gain is assigned to each partition allowing us to study patterns and allocate whose samples are more profitable. Since Cybersecurity is a discipline in which the efficiency of Artificial Intelligence techniques is a key aspect in order to extract actionable knowledge, the statistical analyses have been carried out over five Cybersecurity datasets.SIThe authors would like to thank the Spanish National Cybersecurity Institute (INCIBE), who partially supported this work. Also, in this research, the resources of the Centro de Supercomputación de Castilla y León (SCAYLE, www .scayle.es), funded by the “European Regional Development Fund (ERDF)”, have been used.Instituto Nacional de CiberseguridadEuropean Regional Development FundSupercomputación Castilla y Leó

On Detecting and Removing Superficial Redundancy in Vector Databases

14 p.A mathematical model is proposed in order to obtain an automatized tool to remove any unnecessary data, to compute the level of the redundancy, and to recover the original and filtered database, at any time of the process, in a vector database. This type of database can be modeled as an oriented directed graph. Thus, the database is characterized by an adjacency matrix. Therefore, a record is no longer a row but a matrix. Then, the problem of cleaning redundancies is addressed from a theoretical point of view. Superficial redundancy is measured and filtered by using the 1-norm of a matrix. Algorithms are presented by Python and MapReduce, and a case study of a real cybersecurity database is performed.S

Post-disturbance vegetation dynamics during the Late Pleistocene and the Holocene: An example from NW Iberia

This is the post-print version of the final paper published in Global and Planetary Change. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2012 Elsevier B.V.There is a wealth of studies dealing with the reconstruction of past environmental changes and their effects on vegetation composition in NW Iberia, but none of them have focused specifically on the post-disturbance dynamics (i.e. the type of response) of the vegetation at different space and time scales. To fill this gap, we analysed the record of pollen and non-pollen palynomorphs (NPP) of a 235-cm thick colluvial sequence spanning the last ~ 13,900 years. The aims were to detect the changes in vegetation, identify the responsible drivers and determine the type of responses to disturbance. To extract this information we applied multivariate statistical techniques (constrained cluster analysis and principal components analysis on transposed matrices, PCAtr) to the local (hydro-hygrophytes and NPP) and regional (land pollen) datasets separately. In both cases the cluster analysis resulted in eight local and regional assemblage zones, while five (local types) and four (regional types) principal components were obtained by PCAtr to explain 94.1% and 96.6% of the total variance, respectively. The main drivers identified were climate change, grazing pressure, fire events and cultivation. The vegetation showed gradual, threshold and elastic responses to these drivers, at different space (local vs. regional) and time scales, revealing a complex ecological history. Regional responses to perturbations were sometimes delayed with respect to the local response. The results also showed an ecosystem resilience, such as the persistence of open Betula-dominated vegetation community for ~ 1700 years after the onset of the Holocene, and elastic responses, such as the oak woodland to the 8200 cal yr BP dry/cold event. Our results support the notion that palaeoecological research is a valuable tool to investigate ecosystem history, their responses to perturbations and their ability to buffer them. This knowledge is critical for modelling the impact of future environmental change and to help to manage the landscape more sustainably.The Spanish Governmen