(Appendix) Vertebral number of topsmelt, Atherinops affinis, adults and offspring from different latitudes along the North-American Pacific coast

Organisms that are distributed across spatial climate gradients often exhibit adaptive local variations in morphological and physiological traits, but to what extent such gradients shape evolutionary responses is still unclear. Given the strong natural contrast in latitudinal temperature gradients between the North-American Pacific and Atlantic coast, we asked how increases in vertebral number (VN, known as Jordan's Rule) with latitude would differ between Pacific (Atherinops affinis) and Atlantic Silversides (Menidia menidia), two ecologically equivalent and taxonomically similar fishes with similar latitudinal distributions. VN was determined from radiographs of wild-caught adults (genetic + environmental differences) and its genetic basis confirmed by rearing offspring in common garden experiments. Compared to published data on VN variation in M. menidia (a mean increase of 7.0 vertebrae from 32 to 46°N, VN slope = 0.42/lat), the latitudinal VN increase in Pacific Silversides was approximately half as strong (a mean increase of 3.3 vertebrae from 28 to 43°N, VN slope = 0.23/lat). This mimicked the strong Atlantic (1.11°C/lat) versus weak Pacific latitudinal gradient (0.40°C/lat) in median annual sea surface temperature (SST). Importantly, the relationship of VN to SST was not significantly different between the two species (average slope = -0.39 vertebrae/°C), thus suggesting a common thermal dependency of VN in silverside fishes. Our findings provide novel support for the hypothesis that temperature gradients are the ultimate cause of Jordan's Rule, even though its exact adaptive significance remains speculative. A second investigated trait, the mode of sex determination in Atlantic versus Pacific Silversides, revealed patterns that were inconsistent with our expectation: M. menidia displays temperature-dependent sex determination (TSD) at low latitudes, where growing seasons are long or unconstrained, but also a gradual shift to genetic sex determination (GSD) with increasing latitude due to more and more curtailed growing seasons. Sex ratios in A. affinis, on the other hand, were independent of latitude and rearing temperature (indicating GSD), even though growing seasons are thermally unconstrained across most of the geographical distribution of A. affinis. This suggests that additional factors (e.g., longevity) play an important role in shaping the mode of sex determination in silverside fishes.

A repository for integration of software artifacts with dependency resolution and federation support

While developing new IT products, reusability of existing components is a key aspect that can considerably improve the success rate. This fact has become even more important with the rise of the open source paradigm. However, integrating different products and technologies is not always an easy task. Different communities employ different standards and tools, and most times is not clear which dependencies a particular piece of software has. This is exacerbated by the transitive nature of these dependencies, making component integration a complicated affair. To help reducing this complexity we propose a model-based repository, capable of automatically resolve the required dependencies. This repository needs to be expandable, so new constraints can be analyzed, and also have federation support, for the integration with other sources of artifacts. The solution we propose achieves these working with OSGi components and using OSGi itself.

Optimization of a label-free biosensor vertically characterized based on a periodic lattice of high aspect ratio SU-8 nano-pillars with a simplified 2D theoretical model

We have recently demonstrated a biosensor based on a lattice of SU8 pillars on a 1 μm SiO2/Si wafer by measuring vertically reflectivity as a function of wavelength. The biodetection has been proven with the combination of Bovine Serum Albumin (BSA) protein and its antibody (antiBSA). A BSA layer is attached to the pillars; the biorecognition of antiBSA involves a shift in the reflectivity curve, related with the concentration of antiBSA. A detection limit in the order of 2 ng/ml is achieved for a rhombic lattice of pillars with a lattice parameter (a) of 800 nm, a height (h) of 420 nm and a diameter(d) of 200 nm. These results correlate with calculations using 3D-finite difference time domain method. A 2D simplified model is proposed, consisting of a multilayer model where the pillars are turned into a 420 nm layer with an effective refractive index obtained by using Beam Propagation Method (BPM) algorithm. Results provided by this model are in good correlation with experimental data, reaching a reduction in time from one day to 15 minutes, giving a fast but accurate tool to optimize the design and maximizing sensitivity, and allows analyzing the influence of different variables (diameter, height and lattice parameter). Sensitivity is obtained for a variety of configurations, reaching a limit of detection under 1 ng/ml. Optimum design is not only chosen because of its sensitivity but also its feasibility, both from fabrication (limited by aspect ratio and proximity of the pillars) and fluidic point of view. (© 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

Compile-time derivation of variable dependency using abstract interpretation

Traditional schemes for abstract interpretation-based global analysis of logic programs generally focus on obtaining procedure argument mode and type information. Variable sharing information is often given only the attention needed to preserve the correctness of the analysis. However, such sharing information can be very useful. In particular, it can be used for predicting runtime goal independence, which can eliminate costly run-time checks in and-parallel execution. In this paper, a new algorithm for doing abstract interpretation in logic programs is described which concentrates on inferring the dependencies of the terms bound to program variables with increased precisión and at all points in the execution of the program, rather than just at a procedure level. Algorithms are presented for computing abstract entry and success substitutions which extensively keep track of variable aliasing and term dependence information. In addition, a new, abstract domain independent ñxpoint algorithm is presented and described in detail. The algorithms are illustrated with examples. Finally, results from an implementation of the abstract interpreter are presented.

Efficient design and optimization of bio-photonic sensing cells (BICELLs) for label free biosensing

In previous works we demonstrated the benefits of using micro–nano patterning materials to be used as bio-photonic sensing cells (BICELLs), referred as micro–nano photonic structures having immobilized bioreceptors on its surface with the capability of recognizing the molecular binding by optical transduction. Gestrinone/anti-gestrinone and BSA/anti-BSA pairs were proven under different optical configurations to experimentally validate the biosensing capability of these bio-sensitive photonic architectures. Moreover, Three-Dimensional Finite Difference Time Domain (FDTD) models were employed for simulating the optical response of these structures. For this article, we have developed an effective analytical simulation methodology capable of simulating complex biophotonic sensing architectures. This simulation method has been tested and compared with previous experimental results and FDTD models. Moreover, this effective simulation methodology can be used for efficiently design and optimize any structure as BICELL. In particular for this article, six different BICELL's types have been optimized. To carry out this optimization we have considered three figures of merit: optical sensitivity, Q-factor and signal amplitude. The final objective of this paper is not only validating a suitable and efficient optical simulation methodology but also demonstrating the capability of this method for analyzing the performance of a given number of BICELLs for label-free biosensing.

Biophotonic Sensing Cells (BICELLs) for label-free biosensing

Label free immunoassay sector is a ferment of activity, experiencing rapid growth as new technologies come forward and achieve acceptance. The landscape is changing in a “bottom up” approach, as individual companies promote individual technologies and find a market for them. Therefore, each of the companies operating in the label-free immunoassay sector offers a technology that is in some way unique and proprietary. However, no many technologies based on Label-free technology are currently in the market for PoC and High Throughput Screening (HTS), where mature labeled technologies have taken the market.

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.

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.

A new label fusion method using graph cuts: application to hippocampus segmentation

The aim of this paper is to develop a probabilistic modeling framework for the segmentation of structures of interest from a collection of atlases. Given a subset of registered atlases into the target image for a particular Region of Interest (ROI), a statistical model of appearance and shape is computed for fusing the labels. Segmentations are obtained by minimizing an energy function associated with the proposed model, using a graph-cut technique. We test different label fusion methods on publicly available MR images of human brains.

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.

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.

Energy dependency at the urban scale and its social consequences

Spatial planning & energy. Young planners workshop. Final reports - conclusions