Probabilistic Inference from Arbitrary Uncertainty using Mixtures of Factorized Generalized Gaussians

This paper presents a general and efficient framework for probabilistic inference and learning from arbitrary uncertain information. It exploits the calculation properties of finite mixture models, conjugate families and factorization. Both the joint probability density of the variables and the likelihood function of the (objective or subjective) observation are approximated by a special mixture model, in such a way that any desired conditional distribution can be directly obtained without numerical integration. We have developed an extended version of the expectation maximization (EM) algorithm to estimate the parameters of mixture models from uncertain training examples (indirect observations). As a consequence, any piece of exact or uncertain information about both input and output values is consistently handled in the inference and learning stages. This ability, extremely useful in certain situations, is not found in most alternative methods. The proposed framework is formally justified from standard probabilistic principles and illustrative examples are provided in the fields of nonparametric pattern classification, nonlinear regression and pattern completion. Finally, experiments on a real application and comparative results over standard databases provide empirical evidence of the utility of the method in a wide range of applications

Prognostic utility of serum free light chain ratios and heavy-light chain ratios in multiple myeloma in three PETHEMA/GEM phase III clinical trials

We investigated the prognostic impact and clinical utility of serum free light chains (sFLC) and serum heavy-light chains (sHLC) in patients with multiple myeloma treated according to the GEM2005MENOS65, GEM2005MAS65, and GEM2010MAS65 PETHEMA/GEM phase III clinical trials. Serum samples collected at diagnosis were retrospectively analyzed for sFLC (n = 623) and sHLC (n = 183). After induction or autologous transplantation, 309 and 89 samples respectively were available for sFLC and sHLC assays. At diagnosis, a highly abnormal (HA) sFLC ratio (sFLCr) (32) was not associated with higher risk of progression. After therapy, persistence of involved-sFLC levels >100 mg/L implied worse survival (overall survival [OS], P = 0.03; progression-free survival [PFS], P = 0.007). Among patients that achieved a complete response, sFLCr normalization did not necessarily indicate a higher quality response. We conducted sHLC investigations for IgG and IgA MM. Absolute sHLC values were correlated with monoclonal protein levels measured with serum protein electrophoresis. At diagnosis, HA-sHLCrs (73) showed a higher risk of progression (P = 0.006). Additionally, involved-sHLC levels >5 g/L after treatment were associated with shorter survival (OS, P = 0.001; PFS, P = 0.018). The HA-sHLCr could have prognostic value at diagnosis; absolute values of involved-sFLC >100 mg/L and involved-sHLC >5 g/L could have prognostic value after treatment

Enhanced Fear Expression in a Psychopathological Mouse Model of Trait Anxiety: Pharmacological Interventions

The propensity to develop an anxiety disorder is thought to be determined by genetic and environmental factors. Here we investigated the relationship between a genetic predisposition to trait anxiety and experience-based learned fear in a psychopathological mouse model. Male CD-1 mice selectively bred for either high (HAB), or normal (NAB) anxiety-related behaviour on the elevated plus maze were subjected to classical fear conditioning. During conditioning both mouse lines showed increased fear responses as assessed by freezing behaviour. However, 24 h later, HAB mice displayed more pronounced conditioned responses to both a contextual or cued stimulus when compared with NAB mice. Interestingly, 6 h and already 1 h after fear conditioning, freezing levels were high in HAB mice but not in NAB mice. These results suggest that trait anxiety determines stronger fear memory and/or a weaker ability to inhibit fear responses in the HAB line. The enhanced fear response of HAB mice was attenuated by treatment with either the α2,3,5-subunit selective benzodiazepine partial agonist L-838,417, corticosterone or the selective neurokinin-1 receptor antagonist L-822,429. Overall, the HAB mouse line may represent an interesting model (i) for identifying biological factors underlying misguided conditioned fear responses and (ii) for studying novel anxiolytic pharmacotherapies for patients with fear-associated disorders, including post-traumatic stress disorder and phobias

Circulating microparticles: square the circle

Background: The present review summarizes current knowledge about microparticles (MPs) and provides a systematic overview of last 20 years of research on circulating MPs, with particular focus on their clinical relevance. Results: MPs are a heterogeneous population of cell-derived vesicles, with sizes ranging between 50 and 1000 nm. MPs are capable of transferring peptides, proteins, lipid components, microRNA, mRNA, and DNA from one cell to another without direct cell-to-cell contact. Growing evidence suggests that MPs present in peripheral blood and body fluids contribute to the development and progression of cancer, and are of pathophysiological relevance for autoimmune, inflammatory, infectious, cardiovascular, hematological, and other diseases. MPs have large diagnostic potential as biomarkers; however, due to current technological limitations in purification of MPs and an absence of standardized methods of MP detection, challenges remain in validating the potential of MPs as a non-invasive and early diagnostic platform. Conclusions: Improvements in the effective deciphering of MP molecular signatures will be critical not only for diagnostics, but also for the evaluation of treatment regimens and predicting disease outcomes

J-PLUS: The Javalambre Photometric Local Universe Survey

The Javalambre Photometric Local Universe Survey (J-PLUS) is an ongoing 12-band photometric optical survey, observing thousands of square degrees of the Northern Hemisphere from the dedicated JAST/T80 telescope at the Observatorio Astrofisico de Javalambre (OAJ). The T80Cam is a camera with a field of view of 2 deg(2) mounted on a telescope with a diameter of 83 cm, and is equipped with a unique system of filters spanning the entire optical range (3500-10 000 angstrom). This filter system is a combination of broad-, medium-, and narrow-band filters, optimally designed to extract the rest-frame spectral features (the 3700-4000 angstrom Balmer break region, H delta, Ca H+K, the G band, and the Mg b and Ca triplets) that are key to characterizing stellar types and delivering a low-resolution photospectrum for each pixel of the observed sky. With a typical depth of AB similar to 21.25 mag per band, this filter set thus allows for an unbiased and accurate characterization of the stellar population in our Galaxy, it provides an unprecedented 2D photospectral information for all resolved galaxies in the local Universe, as well as accurate photo-z estimates (at the delta z/(1 + z) similar to 0.005-0.03 precision level) for moderately bright (up to r similar to 20 mag) extragalactic sources. While some narrow-band filters are designed for the study of particular emission features ([O II]/lambda 3727, H alpha/lambda 6563) up to z < 0.017, they also provide well-defined windows for the analysis of other emission lines at higher redshifts. As a result, J-PLUS has the potential to contribute to a wide range of fields in Astrophysics, both in the nearby Universe (Milky Way structure, globular clusters, 2D IFU-like studies, stellar populations of nearby and moderate-redshift galaxies, clusters of galaxies) and at high redshifts (emission-line galaxies at z approximate to 0.77, 2.2, and 4.4, quasi-stellar objects, etc.). With this paper, we release the first similar to 1000 deg(2) of J-PLUS data, containing about 4.3 million stars and 3.0 million galaxies at r < 21 mag. With a goal of 8500 deg(2) for the total J-PLUS footprint, these numbers are expected to rise to about 35 million stars and 24 million galaxies by the end of the survey

Negative results for approximation using single layer and multilayer feedforward neural networks

We prove a negative result for the approximation of functions defined on compact subsets of $\mathbb{R}^d$ (where $d \geq 2$) using feedforward neural networks with one hidden layer and arbitrary continuous activation function. In a nutshell, this result claims the existence of target functions that are as difficult to approximate using these neural networks as one may want. We also demonstrate an analogous result (for general $d \in \mathbb{N}$) for neural networks with an \emph{arbitrary} number of hidden layers, for activation functions that are either rational functions or continuous splines with finitely many pieces.Comment: 12 pages, submitted to a Journa

Modeling the pore level fluid flow in porous media using the immersed boundary method

This chapter demonstrates the potential of the immersed boundary method for the direct numerical simulation of the flow through porous media. A 2D compact finite differences method was employed to solve the unsteady incompressible Navier-Stokes equations with fourth-order Runge-Kutta temporal discretization and fourth-order compact schemes for spatial discretization. The solutions were obtained in a Cartesian grid, with all the associated advantages. The porous media is made of equal size square cylinders in a staggered arrangement and is bounded by solid walls. The transverse and longitudinal distances between cylinders are equal to two cylinder diameters and at the inlet a fully developed velocity profile is specified. The Reynolds number based on the cylinder diameter and maximum inlet velocity ranges from 40 to 80. The different flow regimes are identified and characterised, along with the prediction of the Reynolds number at which transition from steady to unsteady flow takes place. Additionally, the average drag and lift coefficients are presented as a function of the Reynolds number