Activation of a 15-kDa endonuclease in hypoxia/reoxygenation injury without morphologic features of apoptosis.

Hypoxia/reoxygenation is an important cause of tissue injury in a variety of organs and is classically considered to be a necrotic form of cell death. We examined the role of endonuclease activation, considered a characteristic feature of apoptosis, in hypoxia/reoxygenation injury. We demonstrate that subjecting rat renal proximal tubules to hypoxia/reoxygenation results in DNA strand breaks and DNA fragmentation (both by an in situ technique and by agarose gel electrophoresis), which precedes cell death. Hypoxia/reoxygenation resulted in an increase in DNA-degrading activity with an apparent molecular mass of 15 kDa on a substrate gel. This DNA-degrading activity was entirely calcium dependent and was blocked by the endonuclease inhibitor aurintricarboxylic acid. The protein extract from tubules subjected to hypoxia/reoxygenation cleaved intact nuclear DNA obtained from normal proximal tubules into small fragments, which further supports the presence of endonuclease activity. Despite unequivocal evidence of endonuclease activation, the morphologic features of apoptosis, including chromatin condensation, were not observed by light and electron microscopy. Endonuclease inhibitors, aurintricarboxylic acid and Evans blue, provided complete protection against DNA damage induced by hypoxia/reoxygenation but only partial protection against cell death. Taken together, our data provide strong evidence for a role of endonuclease activation as an early event, which is entirely responsible for the DNA damage and partially responsible for the cell death that occurs during hypoxia/reoxygenation injury. Our data also indicate that in hypoxia/reoxygenation injury endonuclease activation and DNA fragmentation occur without the morphological features of apoptosis.

Modes of expression and common structural features of the complete phenylalanine ammonia-lyase gene family in parsley.

We describe a complete gene family encoding phenylalanine ammonia-lyase (PAL; EC 4.3.1.5) in one particular plant species. In parsley (Petroselinum crispum), the PAL gene family comprises two closely related members, PAL1 and PAL2, whose TATA-proximal promoter and coding regions are almost identical, and two additional members, PAL3 and PAL4, with less similarity to one another and to the PAL1 and PAL2 genes. Using gene-specific probes derived from the 5' untranslated regions of PAL1/2, PAL3, and PAL4, we determined the respective mRNA levels in parsley leaves and cell cultures treated with UV light or fungal elicitor and in wounded leaves and roots. For comparison, the functionally closely related cinnamate 4-hydroxylase (C4H) and 4-coumarate:CoA ligase (4CL) mRNAs were measured in parallel. The results indicate various degrees of differential responsiveness of PAL4 relative to the other PAL gene family members, in contrast to a high degree of coordination in the overall expression of the PAL, C4H, and 4CL genes. The only significant sequence similarities shared by all four PAL gene promoters are a TATA-proximal set of three putative cis-acting elements (boxes P, A, and L). None of these elements alone, or the promoter region containing all of them together, conferred elicitor or light responsiveness on a reporter gene in transient expression assays. The elements appear to be necessary but not sufficient for elicitor- or light-mediated PAL gene activation, similar to the situation previously reported for 4CL.

Ectopic expression of the agouti gene in transgenic mice causes obesity, features of type II diabetes, and yellow fur.

Mice that carry the lethal yellow (Ay) or viable yellow (Avy) mutation, two dominant mutations of the agouti (a) gene in mouse chromosome 2, exhibit a phenotype that includes yellow fur, marked obesity, a form of type II diabetes associated with insulin resistance, and an increased susceptibility to tumor development. Molecular analyses of these and several other dominant "obese yellow" a-locus mutations suggested that ectopic expression of the normal agouti protein gives rise to this complex pleiotropic phenotype. We have now tested this hypothesis directly by generating transgenic mice that ectopically express an agouti cDNA clone encoding the normal agouti protein in all tissues examined. Transgenic mice of both sexes have yellow fur, become obese, and develop hyperinsulinemia. In addition, male transgenic mice develop hyperglycemia by 12-20 weeks of age. These results demonstrate conclusively that the ectopic agouti expression is responsible for most, if not all, of the phenotypic traits of the dominant, obese yellow mutants.

The structure of Ascaris hemoglobin domain I at 2.2 A resolution: molecular features of oxygen avidity.

The perienteric hemoglobin of the parasitic nematode Ascaris has an exceptionally high affinity for oxygen. It is an octameric protein containing two similar heme-binding domains per subunit, but recombinant constructs expressing a single, monomeric heme-binding domain (domain 1; D1) retain full oxygen avidity. We have solved the crystal structure of D1 at 2.2 A resolution. Analysis of the structure reveals a characteristic globin fold and illuminates molecular features involved in oxygen avidity of Ascaris perienteric hemoglobin. A strong hydrogen bond between tyrosine at position 10 in the B helix (tyrosine-B10) and the distal oxygen of the ligand, combined with a weak hydrogen bond between glutamine-E7 and the proximal oxygen, grips the ligand in the binding pocket. A third hydrogen bond between these two amino acids appears to stabilize the structure. The B helix of D1 is displaced laterally by 2.5 A when compared with sperm whale myoglobin. This shifts the tyrosine-B10 hydroxyl far enough from liganded oxygen to form a strong hydrogen bond without steric hindrance. Changes in the F helix compared with myoglobin contribute to a tilted heme that may also be important for oxygen affinity.

Evidence for reciprocal antagonism between motion sensors tuned to coarse and fine features

Early visual processing analyses fine and coarse image features separately. Here we show that motion signals derived from fine and coarse analyses are combined in rather a surprising way: Coarse and fine motion sensors representing the same direction of motion inhibit one another and an imbalance can reverse the motion perceived. Observers judged the direction of motion of patches of filtered two-dimensional noise, centered on 1 and 3 cycles/deg. When both sets of noise were present and only the 3 cycles/deg noise moved, judgments were reversed at short durations. When both sets of noise moved, judgments were correct but sensitivity was impaired. Reversals and impairments occurred both with isotropic noise and with orientation-filtered noise. The reversals and impairments could be simulated in a model of motion sensing by adding a stage in which the outputs of motion sensors tuned to 1 and 3 cycles/deg and the same direction of motion were subtracted from one another. The subtraction model predicted and we confirmed in experiments with orientation-filtered noise that if the 1 cycle/deg noise flickered and the 3 cycles/deg noise moved, the 1 cycle/deg noise appeared to move in the opposite direction to the 3 cycles/deg noise even at long durations.

Human Motion Analysis Based on Sequential Modeling of Radar Signal and Stereo Image Features

Falls are one of the greatest threats to elderly health in their daily living routines and activities. Therefore, it is very important to detect falls of an elderly in a timely and accurate manner, so that immediate response and proper care can be provided, by sending fall alarms to caregivers. Radar is an effective non-intrusive sensing modality which is well suited for this purpose, which can detect human motions in all types of environments, penetrate walls and fabrics, preserve privacy, and is insensitive to lighting conditions. Micro-Doppler features are utilized in radar signal corresponding to human body motions and gait to detect falls using a narrowband pulse-Doppler radar. Human motions cause time-varying Doppler signatures, which are analyzed using time-frequency representations and matching pursuit decomposition (MPD) for feature extraction and fall detection. The extracted features include MPD features and the principal components of the time-frequency signal representations. To analyze the sequential characteristics of typical falls, the extracted features are used for training and testing hidden Markov models (HMM) in different falling scenarios. Experimental results demonstrate that the proposed algorithm and method achieve fast and accurate fall detections. The risk of falls increases sharply when the elderly or patients try to exit beds. Thus, if a bed exit can be detected at an early stage of this motion, the related injuries can be prevented with a high probability. To detect bed exit for fall prevention, the trajectory of head movements is used for recognize such human motion. A head detector is trained using the histogram of oriented gradient (HOG) features of the head and shoulder areas from recorded bed exit images. A data association algorithm is applied on the head detection results to eliminate head detection false alarms. Then the three dimensional (3D) head trajectories are constructed by matching scale-invariant feature transform (SIFT) keypoints in the detected head areas from both the left and right stereo images. The extracted 3D head trajectories are used for training and testing an HMM based classifier for recognizing bed exit activities. The results of the classifier are presented and discussed in the thesis, which demonstrates the effectiveness of the proposed stereo vision based bed exit detection approach.

Large scale SLAM with visual features

Comunicación presentada en el X Workshop of Physical Agents, Cáceres, 10-11 septiembre 2009.

A study of 2D features for 3D visual SLAM

Paper submitted to the 43rd International Symposium on Robotics (ISR), Taipei, Taiwan, August 29-31, 2012.

Hand gesture recognition based on morphologic features

Comunicación presentada en el IX Simposium Nacional de Reconocimiento de Formas y Análisis de Imágenes, Benicàssim, Mayo, 2001.

Event-based visual servoing with features’ prediction

Event-based visual servoing is a recently presented approach that performs the positioning of a robot using visual information only when it is required. From the basis of the classical image-based visual servoing control law, the scheme proposed in this paper can reduce the processing time at each loop iteration in some specific conditions. The proposed control method enters in action when an event deactivates the classical image-based controller (i.e. when there is no image available to perform the tracking of the visual features). A virtual camera is then moved through a straight line path towards the desired position. The virtual path used to guide the robot improves the behavior of the previous event-based visual servoing proposal.

The role of statistical and semantic features in single-document extractive summarization

This paper reports on the further results of the ongoing research analyzing the impact of a range of commonly used statistical and semantic features in the context of extractive text summarization. The features experimented with include word frequency, inverse sentence and term frequencies, stopwords filtering, word senses, resolved anaphora and textual entailment. The obtained results demonstrate the relative importance of each feature and the limitations of the tools available. It has been shown that the inverse sentence frequency combined with the term frequency yields almost the same results as the latter combined with stopwords filtering that in its turn proved to be a highly competitive baseline. To improve the suboptimal results of anaphora resolution, the system was extended with the second anaphora resolution module. The present paper also describes the first attempts of the internal document data representation.

Combining visual features and Growing Neural Gas networks for robotic 3D SLAM

The use of 3D data in mobile robotics provides valuable information about the robot’s environment. Traditionally, stereo cameras have been used as a low-cost 3D sensor. However, the lack of precision and texture for some surfaces suggests that the use of other 3D sensors could be more suitable. In this work, we examine the use of two sensors: an infrared SR4000 and a Kinect camera. We use a combination of 3D data obtained by these cameras, along with features obtained from 2D images acquired from these cameras, using a Growing Neural Gas (GNG) network applied to the 3D data. The goal is to obtain a robust egomotion technique. The GNG network is used to reduce the camera error. To calculate the egomotion, we test two methods for 3D registration. One is based on an iterative closest points algorithm, and the other employs random sample consensus. Finally, a simultaneous localization and mapping method is applied to the complete sequence to reduce the global error. The error from each sensor and the mapping results from the proposed method are examined.

Electrochemical features of Pt(S)[n(110) × (100)] surfaces in acidic media

Experiments have been carried out in sulfuric and perchloric acid solutions on Pt(S)[n(110) × (100)] electrodes. The comparison between the two different electrolytic media reveals an important influence of the anion in the voltammetric features. Total charge curves have been obtained with the CO charge displacement method in combination with voltammetric measurements. From these curves, the dependence of the pztc with the step density and the strength of the anion adsorption have been analyzed. The problem of the so-called third peak is treated for a series of electrodes that contain (110) terraces, revealing the requirement of (110) domains for occurrence of this adsorption state.

Spectral features in isolated neutron stars induced by inhomogeneous surface temperatures

The thermal X-ray spectra of several isolated neutron stars display deviations from a pure blackbody. The accurate physical interpretation of these spectral features bears profound implications for our understanding of the atmospheric composition, magnetic field strength and topology, and equation of state of dense matter. With specific details varying from source to source, common explanations for the features have ranged from atomic transitions in the magnetized atmospheres or condensed surface, to cyclotron lines generated in a hot ionized layer near the surface. Here, we quantitatively evaluate the X-ray spectral distortions induced by inhomogeneous temperature distributions of the neutron star surface. To this aim, we explore several surface temperature distributions, we simulate their corresponding general relativistic X-ray spectra (assuming an isotropic, blackbody emission), and fit the latter with a single blackbody model. We find that, in some cases, the presence of a spurious ‘spectral line’ is required at a high significance level in order to obtain statistically acceptable fits, with central energy and equivalent width similar to the values typically observed. We also perform a fit to a specific object, RX J0806.4−4123, finding several surface temperature distributions able to model the observed spectrum. The explored effect is unlikely to work in all sources with detected lines, but in some cases it can indeed be responsible for the appearance of such lines. Our results enforce the idea that surface temperature anisotropy can be an important factor that should be considered and explored also in combination with more sophisticated emission models like atmospheres.

Learning Probabilistic Features for Robotic Navigation Using Laser Sensors

SLAM is a popular task used by robots and autonomous vehicles to build a map of an unknown environment and, at the same time, to determine their location within the map. This paper describes a SLAM-based, probabilistic robotic system able to learn the essential features of different parts of its environment. Some previous SLAM implementations had computational complexities ranging from O(Nlog(N)) to O(N2), where N is the number of map features. Unlike these methods, our approach reduces the computational complexity to O(N) by using a model to fuse the information from the sensors after applying the Bayesian paradigm. Once the training process is completed, the robot identifies and locates those areas that potentially match the sections that have been previously learned. After the training, the robot navigates and extracts a three-dimensional map of the environment using a single laser sensor. Thus, it perceives different sections of its world. In addition, in order to make our system able to be used in a low-cost robot, low-complexity algorithms that can be easily implemented on embedded processors or microcontrollers are used.