Characterization of the Performance of Optical Label-Free Biosensors

The field of optical label free biosensors has become a topic of interest during past years, with devices based on the detection of angular or wavelength shift of optical modes [1]. Common parameters to characterize their performance are the Limit of Detection (LOD, defined as the minimum change of refractive index upon the sensing surface that the device is able to detect, and also BioLOD, which represents the minimum amount of target analyte accurately resolved by the system; with units of concentration (common un its are p pm, ng/ml, or nM). LOD gives a first value to compare different biosensors, and is obtained both theoretically (using photonic calculation tools), and experimentally,covering the sensing area with fluids of different refractive indexes.

Biophotonic Sensing Cells Optimization for label-free biosensing

Los sectores de detección biológica demandan continuamente técnicas de análisis y diagnóstico más eficientes y precisas para identificar enfermedades y desarrollar nuevos medicamentos. Actualmente se considera que hay una gran necesidad de desarrollar herramientas de diagnóstico capaces de asegurar sensibilidad, rapidez, sencillez y asequibilidad para aplicaciones en sectores como la salud, la alimentación, el medioambiente o la seguridad. En el ámbito clínico se necesitan profundos avances tecnológicos capaces de ofrecer análisis rápidos, exactos, fiables y asequibles en coste y que tengan como consecuencia la mejora clínica y económica a partir de un diagnóstico eficiente. En concreto, hay un interés creciente por la descentralización del diagnóstico clínico mediante plataformas de detección cercanas al usuario final, denominadas POCs (Point Of Care devices). La utilización de POCs (referidas al diagnóstico cercano al usuario final o fuera del laboratorio de análisis clínico), mediante detección in vitro (IVD), será extremadamente útil en centros de salud, clínicas o unidades hospitalarias, entornos laborales o incluso en el hogar. Por otra parte, el desarrollo de la genómica, proteómica y otras tecnologías conocidas como “omics” (sufijo en inglés para referirse, por ejemplo, a genomics, transcriptomics, proteomics, metabolomics, lipidomics) está incrementando la demanda de nuevas tecnologías mucho más avanzadas con una clara orientación hacia la medicina personalizada y la necesidad de hacer frente a cambios en los tratamientos en el caso de enfermedades complejas. Desde hace poco tiempo se han definido las Celdas Biofónicas (BICELLs) como una metodología novedosa para la detección de agentes biológicos que ofrecen una serie de características que las hacen interesantes como son: Capacidad de multiplexación, alta sensibilidad, posibilidad de medir en gota, compatible con otras tecnologías. En este trabajo se hace un estudio y optimización sobre diferentes tipos de BICELLs y se valoran una serie de figuras de merito a tener en cuenta desde el punto de vista del lector óptico a emplear.

Registration and fusion of contrast-enhanced MRI myocardial substrate maps and X-ray angiograms

The aim of this work is to provide the necessary methods to register and fuse the endo-epicardial signal intensity (SI) maps extracted from contrast-enhanced magnetic resonance imaging (ceMRI) with X-ray coronary ngiograms using an intrinsic registrationbased algorithm to help pre-planning and guidance of catheterization procedures. Fusion of angiograms with SI maps was treated as a 2D-3D pose estimation, where each image point is projected to a Plücker line, and the screw representation for rigid motions is minimized using a gradient descent method. The resultant transformation is applied to the SI map that is then projected and fused on each angiogram. The proposed method was tested in clinical datasets from 6 patients with prior myocardial infarction. The registration procedure is optionally combined with an iterative closest point algorithm (ICP) that aligns the ventricular contours segmented from two ventriculograms.

Label-free biosensing for dry eye by Means of BICELLS

The use of Biophotonic Sensing Cells (BICELLs) based on micro-nano pattemed photonic architectures has been recently proven as an efficient methodology for label-free biosensing by using Optical Interrogation [1]. According to this, we have studied the different optical response for a specific typology of BICELL, consisting of structures of SU -8. This material is biocompatible with different types of biomolecules and can be immobilized on its sensing surface. In particular, we have measured the optical response for a biomarker in clinic diagnostic of dry eye. Although different proteins can be enstudied such as: PRDX5, ANXA 1, ANXA 11, CST 4, PLAA Y S 1 OOA6 related with ocular surface (dry eye), for this work PLAA (phospholipase A2) is studied by means of label free biosensing based on BICELLs for analyzing the performance and specificity according with means values of concentration in ROC curves.

Efficient Monte Carlo optimization for multi-label classifier chains

Multi-label classification (MLC) is the supervised learning problem where an instance may be associated with multiple labels. Modeling dependencies between labels allows MLC methods to improve their performance at the expense of an increased computational cost. In this paper we focus on the classifier chains (CC) approach for modeling dependencies. On the one hand, the original CC algorithm makes a greedy approximation, and is fast but tends to propagate errors down the chain. On the other hand, a recent Bayes-optimal method improves the performance, but is computationally intractable in practice. Here we present a novel double-Monte Carlo scheme (M2CC), both for finding a good chain sequence and performing efficient inference. The M2CC algorithm remains tractable for high-dimensional data sets and obtains the best overall accuracy, as shown on several real data sets with input dimension as high as 1449 and up to 103 labels.

Expressive power of binary relevance and chain classifiers based on Bayesian Networks for multi-label classification

Bayesian network classifiers are widely used in machine learning because they intuitively represent causal relations. Multi-label classification problems require each instance to be assigned a subset of a defined set of h labels. This problem is equivalent to finding a multi-valued decision function that predicts a vector of h binary classes. In this paper we obtain the decision boundaries of two widely used Bayesian network approaches for building multi-label classifiers: Multi-label Bayesian network classifiers built using the binary relevance method and Bayesian network chain classifiers. We extend our previous single-label results to multi-label chain classifiers, and we prove that, as expected, chain classifiers provide a more expressive model than the binary relevance method.

Recent progress in research on tungsten materials for nuclear fusion applications in Europe

The current magnetic confinement nuclear fusion power reactor concepts going beyond ITER are based on assumptions about the availability of materials with extreme mechanical, heat, and neutron load capacity. In Europe, the development of such structural and armour materials together with the necessary production, machining, and fabrication technologies is pursued within the EFDA long-term fusion materials programme. This paper reviews the progress of work within the programme in the area of tungsten and tungsten alloys. Results, conclusions, and future projections are summarized for each of the programme´s main subtopics, which are: (1) fabrication, (2) structural W materials, (3) W armour materials, and (4) materials science and modelling. It gives a detailed overview of the latest results on materials research, fabrication processes, joining options, high heat flux testing, plasticity studies, modelling, and validation experiments.

Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty

Abstract Interneuron classification is an important and long-debated topic in neuroscience. A recent study provided a data set of digitally reconstructed interneurons classified by 42 leading neuroscientists according to a pragmatic classification scheme composed of five categorical variables, namely, of the interneuron type and four features of axonal morphology. From this data set we now learned a model which can classify interneurons, on the basis of their axonal morphometric parameters, into these five descriptive variables simultaneously. Because of differences in opinion among the neuroscientists, especially regarding neuronal type, for many interneurons we lacked a unique, agreed-upon classification, which we could use to guide model learning. Instead, we guided model learning with a probability distribution over the neuronal type and the axonal features, obtained, for each interneuron, from the neuroscientists’ classification choices. We conveniently encoded such probability distributions with Bayesian networks, calling them label Bayesian networks (LBNs), and developed a method to predict them. This method predicts an LBN by forming a probabilistic consensus among the LBNs of the interneurons most similar to the one being classified. We used 18 axonal morphometric parameters as predictor variables, 13 of which we introduce in this paper as quantitative counterparts to the categorical axonal features. We were able to accurately predict interneuronal LBNs. Furthermore, when extracting crisp (i.e., non-probabilistic) predictions from the predicted LBNs, our method outperformed related work on interneuron classification. Our results indicate that our method is adequate for multi-dimensional classification of interneurons with probabilistic labels. Moreover, the introduced morphometric parameters are good predictors of interneuron type and the four features of axonal morphology and thus may serve as objective counterparts to the subjective, categorical axonal features.

Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty

Interneuron classification is an important and long-debated topic in neuroscience. A recent study provided a data set of digitally reconstructed interneurons classified by 42 leading neuroscientists according to a pragmatic classification scheme composed of five categorical variables, namely, of the interneuron type and four features of axonal morphology. From this data set we now learned a model which can classify interneurons, on the basis of their axonal morphometric parameters, into these five descriptive variables simultaneously. Because of differences in opinion among the neuroscientists, especially regarding neuronal type, for many interneurons we lacked a unique, agreed-upon classification, which we could use to guide model learning. Instead, we guided model learning with a probability distribution over the neuronal type and the axonal features, obtained, for each interneuron, from the neuroscientists’ classification choices. We conveniently encoded such probability distributions with Bayesian networks, calling them label Bayesian networks (LBNs), and developed a method to predict them. This method predicts an LBN by forming a probabilistic consensus among the LBNs of the interneurons most similar to the one being classified. We used 18 axonal morphometric parameters as predictor variables, 13 of which we introduce in this paper as quantitative counterparts to the categorical axonal features. We were able to accurately predict interneuronal LBNs. Furthermore, when extracting crisp (i.e., non-probabilistic) predictions from the predicted LBNs, our method outperformed related work on interneuron classification. Our results indicate that our method is adequate for multi-dimensional classification of interneurons with probabilistic labels. Moreover, the introduced morphometric parameters are good predictors of interneuron type and the four features of axonal morphology and thus may serve as objective counterparts to the subjective, categorical axonal features.

Optimal laser intensity profiles for a uniform target illumination in direct-drive inertial confinement fusion

A numerical method providing the optimal laser intensity profiles for a direct-drive inertial confinement fusion scheme has been developed. The method provides an alternative approach to phase-space optimization studies, which can prove computationally expensive. The method applies to a generic irradiation configuration characterized by an arbitrary number NB of laser beams provided that they irradiate the whole target surface, and thus goes beyond previous analyses limited to symmetric configurations. The calculated laser intensity profiles optimize the illumination of a spherical target. This paper focuses on description of the method, which uses two steps: first, the target irradiation is calculated for initial trial laser intensities, and then in a second step the optimal laser intensities are obtained by correcting the trial intensities using the calculated illumination. A limited number of example applications to direct drive on the Laser MegaJoule (LMJ) are described.

Simulating the remote handling of the blanket segments in DEMO fusion reactor with thermo-mechanical meshfree multibody dynamics

The present study shows a first approach to the simulation of the remote handling oper- ation which takes into account the thermal and flexible behavior of the blanket segments and its implications on the remote handling equipment, in order to validate and improve its design.

Multisensor data fusion for accurate modelling of mobile objects

In the last decade, multi-sensor data fusion has become a broadly demanded discipline to achieve advanced solutions that can be applied in many real world situations, either civil or military. In Defence,accurate detection of all target objects is fundamental to maintaining situational awareness, to locating threats in the battlefield and to identifying and protecting strategically own forces. Civil applications, such as traffic monitoring, have similar requirements in terms of object detection and reliable identification of incidents in order to ensure safety of road users. Thanks to the appropriate data fusion technique, we can give these systems the power to exploit automatically all relevant information from multiple sources to face for instance mission needs or assess daily supervision operations. This paper focuses on its application to active vehicle monitoring in a particular area of high density traffic, and how it is redirecting the research activities being carried out in the computer vision, signal processing and machine learning fields for improving the effectiveness of detection and tracking in ground surveillance scenarios in general. Specifically, our system proposes fusion of data at a feature level which is extracted from a video camera and a laser scanner. In addition, a stochastic-based tracking which introduces some particle filters into the model to deal with uncertainty due to occlusions and improve the previous detection output is presented in this paper. It has been shown that this computer vision tracker contributes to detect objects even under poor visual information. Finally, in the same way that humans are able to analyze both temporal and spatial relations among items in the scene to associate them a meaning, once the targets objects have been correctly detected and tracked, it is desired that machines can provide a trustworthy description of what is happening in the scene under surveillance. Accomplishing so ambitious task requires a machine learning-based hierarchic architecture able to extract and analyse behaviours at different abstraction levels. A real experimental testbed has been implemented for the evaluation of the proposed modular system. Such scenario is a closed circuit where real traffic situations can be simulated. First results have shown the strength of the proposed system.

Microstructural and mechanical characterization of tungsten based materials for fusion reactors

El wolframio (W) y sus aleaciones se consideran los mejores candidatos para la construcción del divertor en la nueva generación de reactores de fusión nuclear. Este componente va a recibir las cargas térmicas más elevadas durante el funcionamiento del reactor ya que estará en contacto directo con el plasma. En los últimos años, después de un profundo análisis y siguiendo una estrategia de reducción de costes, la Organización de ITER tomó la decisión de construir el divertor integramente de wolframio desde el principio. Por ello, el wolframio no sólo actuará como material en contacto con el plasma (PFM), sino que también tendría aplicaciones estructurales. El wolframio, debido a sus excelentes propiedades termo-físicas, cumple todos los requerimientos para ser utilizado como PFM, sin embargo, su inherente fragilidad pone en peligro su uso estructural. Por tanto, uno de los principales objetivos de esta tesis es encontrar una aleación de wolframio con menor fragilidad. Durante éste trabajo, se realizó la caracterización microstructural y mecánica de diferentes materiales basados en wolframio. Sin embargo, ésta tarea es un reto debido a la pequeña cantidad de material suministrado, su reducido tamaño de grano y fragilidad. Por ello, para una correcta medida de todas las propiedades físicas y mecánicas se utilizaron diversas técnicas experimentales. Algunas de ellas se emplean habitualmente como la nanoindentación o los ensayos de flexión en tres puntos (TPB). Sin embargo, otras fueron especificamente desarrolladas e implementadas durante el desarrollo de esta tesis como es el caso de la medida real de la tenacidad de fractura en los materiales masivos, o de las medidas in situ de la tenacidad de fractura en las láminas delgadas de wolframio. Diversas composiciones de aleaciones de wolframio masivas (W-1% Y2O3, W-2% V-0.5% Y2O3, W-4% V-0.5% Y2O3, W-2% Ti-1% La2O3 y W-4% Ti-1% La2O3) se han estudiado y comparado con un wolframio puro producido en las mismas condiciones. Estas aleaciones, producidas por ruta pulvimetalúrgica de aleado mecánico (MA) y compactación isostática en caliente (HIP), fueron microstructural y mecánicamente caracterizadas desde 77 hasta 1473 K en aire y en alto vacío. Entre otras propiedades físicas y mecánicas se midieron la dureza, el módulo elástico, la resistencia a flexión y la tenacidad de fractura para todas las aleaciones. Finalmente se analizaron las superficies de fractura después de los ensayos de TPB para relacionar los micromecanismos de fallo con el comportamiento macroscópico a rotura. Los resultados obtenidos mostraron un comportamiento mecánico frágil en casi todo el intervalo de temperaturas y para casi todas las aleaciones sin mejoría de la temperatura de transición dúctil-frágil (DBTT). Con el fin de encontrar un material base wolframio con una DBTT más baja se realizó también un estudio, aún preliminar, de láminas delgadas de wolframio puro y wolframio dopado con 0.005wt.% potasio (K). Éstas láminas fueron fabricadas industrialmente mediante sinterizado y laminación en caliente y en frío y se sometieron posteriormente a un tratamiento térmico de recocido desde 1073 hasta 2673 K. Se ha analizado la evolución de su microestructura y las propiedades mecánicas al aumentar la temperatura de recocido. Los resultados mostraron la estabilización de los granos de wolframio con el incremento de la temperatura de recocido en las láminas delgadas de wolframio dopado con potasio. Sin embargo, es necesario realizar estudios adicionales para entender mejor la microstructura y algunas propiedades mecánicas de estos materiales, como la tenacidad de fractura. Tungsten (W) and tungsten-based alloys are considered to be the best candidate materials for fabricating the divertor in the next-generation nuclear fusion reactors. This component will experience the highest thermal loads during the operation of a reactor since it directly faces the plasma. In recent years, after thorough analysis that followed a strategy of cost reduction, the ITER Organization decided to built a full-tunsgten divertor before the first nuclear campaigns. Therefore, tungsten will be used not only as a plasma-facing material (PFM) but also in structural applications. Tungsten, due to its the excellent thermo-physical properties fulfils the requirements of a PFM, however, its use in structural applications is compromised due to its inherent brittleness. One of the objectives of this phD thesis is therefore, to find a material with improved brittleness behaviour. The microstructural and mechanical characterisation of different tunsgten-based materials was performed. However, this is a challenging task because of the reduced laboratory-scale size of the specimens provided, their _ne microstructure and their brittleness. Consequently, many techniques are required to ensure an accurate measurement of all the mechanical and physical properties. Some of the applied methods have been widely used such as nanoindentation or three-point bending (TPB) tests. However, other methods were specifically developed and implemented during this work such as the measurement of the real fracture toughness of bulk-tunsgten alloys or the in situ fracture toughness measurements of very thin tungsten foils. Bulk-tunsgten materials with different compositions (W-1% Y2O3, W-2% V- 0.5% Y2O3, W-4% V-0.5% Y2O3, W-2% Ti-1% La2O3 and W-4% Ti-1% La2O3) were studied and compared with pure tungsten processed under the same conditions. These alloys, produced by a powder metallurgical route of mechanical alloying (MA) and hot isostatic pressing (HIP), were microstructural and mechanically characterised from 77 to 1473 K in air and under high vacuum conditions. Hardness, elastic modulus, flexural strength and fracture toughness for all of the alloys were measured in addition to other physical and mechanical properties. Finally, the fracture surfaces after the TPB tests were analysed to correlate the micromechanisms of failure with the macroscopic behaviour. The results reveal brittle mechanical behaviour in almost the entire temperature range for the alloys and micromechanisms of failure with no improvement in the ductile-brittle transition temperature (DBTT). To continue the search of a tungsten material with lowered DBTT, a preliminary study of pure tunsgten and 0.005 wt.% potassium (K)-doped tungsten foils was also performed. These foils were industrially produced by sintering and hot and cold rolling. After that, they were annealed from 1073 to 2673 K to analyse the evolution of the microstructural and mechanical properties with increasing annealing temperature. The results revealed the stabilisation of the tungsten grains with increasing annealing temperature in the potassium-doped tungsten foil. However, additional studies need to be performed to gain a better understanding of the microstructure and mechanical properties of these materials such as fracture toughness.

Optimization of Dengue immunoassay by label-free interferometric optical detection method

In this communication we report a direct immunoassay for detecting dengue virus by means of a label-free interferometric optical detection method. We also demonstrate how we can optimize this sensing response by adding a blocking step able to significantly enhance the optical sensing response. The blocking reagent used for this optimization is a dry milk diluted in phosphate buffered saline. The recognition curve of dengue virus over the proposed surface sensor demonstrates the capacity of this method to be applied in Point of Care technology.

Fetal origins of the TEL-AML1 fusion gene in identical twins with leukemia

The TEL (ETV6)−AML1 (CBFA2) gene fusion is the most common reciprocal chromosomal rearrangement in childhood cancer occurring in ≈25% of the most predominant subtype of leukemia— common acute lymphoblastic leukemia. The TEL-AML1 genomic sequence has been characterized in a pair of monozygotic twins diagnosed at ages 3 years, 6 months and 4 years, 10 months with common acute lymphoblastic leukemia. The twin leukemic DNA shared the same unique (or clonotypic) but nonconstitutive TEL-AML1 fusion sequence. The most plausible explanation for this finding is a single cell origin of the TEL-AML fusion in one fetus in utero, probably as a leukemia-initiating mutation, followed by intraplacental metastasis of clonal progeny to the other twin. Clonal identity is further supported by the finding that the leukemic cells in the two twins shared an identical rearranged IGH allele. These data have implications for the etiology and natural history of childhood leukemia.