Bayesian image reconstruction with space-variant noise suppression

In this paper we present a Bayesian image reconstruction algorithm with entropy prior (FMAPE) that uses a space-variant hyperparameter. The spatial variation of the hyperparameter allows different degrees of resolution in areas of different statistical characteristics, thus avoiding the large residuals resulting from algorithms that use a constant hyperparameter. In the first implementation of the algorithm, we begin by segmenting a Maximum Likelihood Estimator (MLE) reconstruction. The segmentation method is based on using a wavelet decomposition and a self-organizing neural network. The result is a predetermined number of extended regions plus a small region for each star or bright object. To assign a different value of the hyperparameter to each extended region and star, we use either feasibility tests or cross-validation methods. Once the set of hyperparameters is obtained, we carried out the final Bayesian reconstruction, leading to a reconstruction with decreased bias and excellent visual characteristics. The method has been applied to data from the non-refurbished Hubble Space Telescope. The method can be also applied to ground-based images.

Single-trial topographic analysis of human EEG: A new image of event-related potentials

We present a novel approach for analyzing single-trial electroencephalography (EEG) data, using topographic information. The method allows for visualizing event-related potentials using all the electrodes of recordings overcoming the problem of previous approaches that required electrode selection and waveforms filtering. We apply this method to EEG data from an auditory object recognition experiment that we have previously analyzed at an ERP level. Temporally structured periods were statistically identified wherein a given topography predominated without any prior information about the temporal behavior. In addition to providing novel methods for EEG analysis, the data indicate that ERPs are reliably observable at a single-trial level when examined topographically.

Image restoration in astronomy: a Bayesian perspective

When preparing an article on image restoration in astronomy, it is obvious that some topics have to be dropped to keep the work at reasonable length. We have decided to concentrate on image and noise models and on the algorithms to find the restoration. Topics like parameter estimation and stopping rules are also commented on. We start by describing the Bayesian paradigm and then proceed to study the noise and blur models used by the astronomical community. Then the prior models used to restore astronomical images are examined. We describe the algorithms used to find the restoration for the most common combinations of degradation and image models. Then we comment on important issues such as acceleration of algorithms, stopping rules, and parameter estimation. We also comment on the huge amount of information available to, and made available by, the astronomical community.

An Adeno-Associated Virus-Based Intracellular Sensor of Pathological Nuclear Factor-κB Activation for Disease-Inducible Gene Transfer.

Stimulation of resident cells by NF-κB activating cytokines is a central element of inflammatory and degenerative disorders of the central nervous system (CNS). This disease-mediated NF-κB activation could be used to drive transgene expression selectively in affected cells, using adeno-associated virus (AAV)-mediated gene transfer. We have constructed a series of AAV vectors expressing GFP under the control of different promoters including NF-κB -responsive elements. As an initial screen, the vectors were tested in vitro in HEK-293T cells treated with TNF-α. The best profile of GFP induction was obtained with a promoter containing two blocks of four NF-κB -responsive sequences from the human JCV neurotropic polyoma virus promoter, fused to a new tight minimal CMV promoter, optimally distant from each other. A therapeutical gene, glial cell line-derived neurotrophic factor (GDNF) cDNA under the control of serotype 1-encapsidated NF-κB -responsive AAV vector (AAV-NF) was protective in senescent cultures of mouse cortical neurons. AAV-NF was then evaluated in vivo in the kainic acid (KA)-induced status epilepticus rat model for temporal lobe epilepsy, a major neurological disorder with a central pathophysiological role for NF-κB activation. We demonstrate that AAV-NF, injected in the hippocampus, responded to disease induction by mediating GFP expression, preferentially in CA1 and CA3 neurons and astrocytes, specifically in regions where inflammatory markers were also induced. Altogether, these data demonstrate the feasibility to use disease-activated transcription factor-responsive elements in order to drive transgene expression specifically in affected cells in inflammatory CNS disorders using AAV-mediated gene transfer.

Peroxisome proliferator-activated receptor beta/delta (PPARbeta/delta) but not PPARalpha serves as a plasma free fatty acid sensor in liver.

Peroxisome proliferator-activated receptor alpha (PPARalpha) is an important transcription factor in liver that can be activated physiologically by fasting or pharmacologically by using high-affinity synthetic agonists. Here we initially set out to elucidate the similarities in gene induction between Wy14643 and fasting. Numerous genes were commonly regulated in liver between the two treatments, including many classical PPARalpha target genes, such as Aldh3a2 and Cpt2. Remarkably, several genes induced by Wy14643 were upregulated by fasting independently of PPARalpha, including Lpin2 and St3gal5, suggesting involvement of another transcription factor. Using chromatin immunoprecipitation, Lpin2 and St3gal5 were shown to be direct targets of PPARbeta/delta during fasting, whereas Aldh3a2 and Cpt2 were exclusive targets of PPARalpha. Binding of PPARbeta/delta to the Lpin2 and St3gal5 genes followed the plasma free fatty acid (FFA) concentration, consistent with activation of PPARbeta/delta by plasma FFAs. Subsequent experiments using transgenic and knockout mice for Angptl4, a potent stimulant of adipose tissue lipolysis, confirmed the stimulatory effect of plasma FFAs on Lpin2 and St3gal5 expression levels via PPARbeta/delta. In contrast, the data did not support activation of PPARalpha by plasma FFAs. The results identify Lpin2 and St3gal5 as novel PPARbeta/delta target genes and show that upregulation of gene expression by PPARbeta/delta is sensitive to plasma FFA levels. In contrast, this is not the case for PPARalpha, revealing a novel mechanism for functional differentiation between PPARs.

Semi-supervised remote sensing image classification with cluster kernels

A semisupervised support vector machine is presented for the classification of remote sensing images. The method exploits the wealth of unlabeled samples for regularizing the training kernel representation locally by means of cluster kernels. The method learns a suitable kernel directly from the image and thus avoids assuming a priori signal relations by using a predefined kernel structure. Good results are obtained in image classification examples when few labeled samples are available. The method scales almost linearly with the number of unlabeled samples and provides out-of-sample predictions.

Avoiding sensor blindness in Geiger mode avalanche photodiode arrays fabricated in a conventional CMOS process

The need to move forward in the knowledge of the subatomic world has stimulated the development of new particle colliders. However, the objectives of the next generation of colliders sets unprecedented challenges to the detector performance. The purpose of this contribution is to present a bidimensional array based on avalanche photodiodes operated in the Geiger mode to track high energy particles in future linear colliders. The bidimensional array can function in a gated mode to reduce the probability to detect noise counts interfering with real events. Low reverse overvoltages are used to lessen the dark count rate. Experimental results demonstrate that the prototype fabricated with a standard HV-CMOS process presents an increased efficiency and avoids sensor blindness by applying the proposed techniques.

A reusable smart interface for gas sensor resistance measurement

The advances of the semiconductor industry enable microelectromechanical systems sensors, signal conditioning logic and network access to be integrated into a smart sensor node. In this framework, a mixed-mode interface circuit for monolithically integrated gas sensor arrays was developed with high-level design techniques. This interface system includes analog electronics for inspection of up to four sensor arrays and digital logic for smart control and data communication. Although different design methodologies were used in the conception of the complete circuit, high-level synthesis tools and methodologies were crucial in speeding up the whole design cycle, enhancing reusability for future applications and producing a flexible and robust component.

On-line event detection by recursive dynamic principal component analysis and gas sensor arrays under drift conditions

Leakage detection is an important issue in many chemical sensing applications. Leakage detection hy thresholds suffers from important drawbacks when sensors have serious drifts or they are affected by cross-sensitivities. Here we present an adaptive method based in a Dynamic Principal Component Analysis that models the relationships between the sensors in the may. In normal conditions a certain variance distribution characterizes sensor signals. However, in the presence of a new source of variance the PCA decomposition changes drastically. In order to prevent the influence of sensor drifts the model is adaptive and it is calculated in a recursive manner with minimum computational effort. The behavior of this technique is studied with synthetic signals and with real signals arising by oil vapor leakages in an air compressor. Results clearly demonstrate the efficiency of the proposed method.

A current-mode interface circuit for a piezoresistive pressure sensor

An interfacing circuit for piezoresistive pressure sensors based on CMOS current conveyors is presented. The main advantages of the proposed interfacing circuit include the use of a single piezoresistor, the capability of offset compensation, and a versatile current-mode configuration, with current output and current or voltage input. Experimental tests confirm linear relation of output voltage versus piezoresistance variation.

The unfolded protein response: integrating stress signals through the stress sensor IRE1α.

Stress induced by accumulation of unfolded proteins at the endoplasmic reticulum (ER) is a classic feature of secretory cells and is observed in many tissues in human diseases including cancer, diabetes, obesity, and neurodegeneration. Cellular adaptation to ER stress is achieved by the activation of the unfolded protein response (UPR), an integrated signal transduction pathway that transmits information about the protein folding status at the ER to the nucleus and cytosol to restore ER homeostasis. Inositol-requiring transmembrane kinase/endonuclease-1 (IRE1α), the most conserved UPR stress sensor, functions as an endoribonuclease that processes the mRNA of the transcription factor X-box binding protein-1 (XBP1). IRE1α signaling is a highly regulated process, controlled by the formation of a dynamic scaffold onto which many regulatory components assemble, here referred to as the UPRosome. Here we provide an overview of the signaling and regulatory mechanisms underlying IRE1α function and discuss the emerging role of the UPR in adaptation to protein folding stress in specialized secretory cells and in pathological conditions associated with alterations in ER homeostasis.

Gas identification with tin oxide sensor array and self-organizing maps: adaptive correction of sensor drifts

Low-cost tin oxide gas sensors are inherently nonspecific. In addition, they have several undesirable characteristics such as slow response, nonlinearities, and long-term drifts. This paper shows that the combination of a gas-sensor array together with self-organizing maps (SOM's) permit success in gas classification problems. The system is able to determine the gas present in an atmosphere with error rates lower than 3%. Correction of the sensor's drift with an adaptive SOM has also been investigated

Drift compensation of gas sensor array data by Orthogonal Signal Correction

Drift is an important issue that impairs the reliability of gas sensing systems. Sensor aging, memory effects and environmental disturbances produce shifts in sensor responses that make initial statistical models for gas or odor recognition useless after a relatively short period (typically few weeks). Frequent recalibrations are needed to preserve system accuracy. However, when recalibrations involve numerous samples they become expensive and laborious. An interesting and lower cost alternative is drift counteraction by signal processing techniques. Orthogonal Signal Correction (OSC) is proposed for drift compensation in chemical sensor arrays. The performance of OSC is also compared with Component Correction (CC). A simple classification algorithm has been employed for assessing the performance of the algorithms on a dataset composed by measurements of three analytes using an array of seventeen conductive polymer gas sensors over a ten month period.