Global characterization and target identification of piRNAs and endo-siRNAs in mouse gametes and zygotes

44 p.-1 tab.-10 fig.A set of small RNAs known as rasRNAs (repeat-associated small RNAs) have been related to the down-regulation of Transposable Elements (TEs) to safeguard genome integrity. Two key members of the rasRNAs group are piRNAs and endo-siRNAs. We have performed a comparative analysis of piRNAs and endo-siRNAs present in mouse oocytes, spermatozoa and zygotes, identified by deep sequencing and bioinformatic analysis. The detection of piRNAs and endo-siRNAs in the spermatozoa and revealed also in zygotes, hints to their potential delivery to oocytes during fertilization. However, a comparative assessment of the three cell types indicates that both piRNAs and endo-siRNAs are mainly maternally inherited. Finally, we have assessed the role of the different rasRNA molecules in connection with amplification processes by way of the "ping-pong cycle". Our results suggest that the ping-pong cycle can act on other rasRNAs, such as tRNA- and rRNA-derived fragments, thus not only being restricted to TEs during gametogenesis. © 2014 Elsevier B.V.This work was funded by grants from The European Chemical Industry Council Long-range Research Initiative (CEFIC-LRi), from the MEDDTL (11-MRESPNRPE-9-CVS-072), France, from the CSIC (PIE 201020E016), Spain.Peer reviewe

Improving a leaves automatic recognition process using PCA

In this work we present a simulation of a recognition process with perimeter characterization of a simple plant leaves as a unique discriminating parameter. Data coding allowing for independence of leaves size and orientation may penalize performance recognition for some varieties. Border description sequences are then used, and Principal Component Analysis (PCA) is applied in order to study which is the best number of components for the classification task, implemented by means of a Support Vector Machine (SVM) System. Obtained results are satisfactory, and compared with [4] our system improves the recognition success, diminishing the variance at the same time

Reducción del vector de características de reconocimiento facial

In this present work, we are proposing a characteristics reduction system for a facial biometric identification system, using transformed domains such as discrete cosine transformed (DCT) and discrete wavelets transformed (DWT) as parameterization; and Support Vector Machines (SVM) and Neural Network (NN) as classifiers. The size reduction has been done with Principal Component Analysis (PCA) and with Independent Component Analysis (ICA). This system presents a similar success results for both DWT-SVM system and DWT-PCA-SVM system, about 98%. The computational load is improved on training mode due to the decreasing of input’s size and less complexity of the classifier

Automatic Recognition of Leaves by Shape Detection Pre-Processing with Ica

In this work we present a simulation of a recognition process with perimeter characterization of a simple plant leaves as a unique discriminating parameter. Data coding allowing for independence of leaves size and orientation may penalize performance recognition for some varieties. Border description sequences are then used to characterize the leaves. Independent Component Analysis (ICA) is then applied in order to study which is the best number of components to be considered for the classification task, implemented by means of an Artificial Neural Network (ANN). Obtained results with ICA as a pre-processing tool are satisfactory, and compared with some references our system improves the recognition success up to 80.8% depending on the number of considered independent components

Human Computer Interactions in Next-Generation of Aircraft Smart Navigation Management Systems: Task Analysis and Architecture under an Agent-Oriented Methodological Approach

The limited efficiency of current air traffic systems will require a next-generation of Smart Air Traffic System (SATS) that relies on current technological advances. This challenge means a transition toward a new navigation and air-traffic procedures paradigm, where pilots and air traffic controllers perform and coordinate their activities according to new roles and technological supports. The design of new Human-Computer Interactions (HCI) for performing these activities is a key element of SATS. However efforts for developing such tools need to be inspired on a parallel characterization of hypothetical air traffic scenarios compatible with current ones. This paper is focused on airborne HCI into SATS where cockpit inputs came from aircraft navigation systems, surrounding traffic situation, controllers' indications, etc. So the HCI is intended to enhance situation awareness and decision-making through pilot cockpit. This work approach considers SATS as a system distributed on a large-scale with uncertainty in a dynamic environment. Therefore, a multi-agent systems based approach is well suited for modeling such an environment. We demonstrate that current methodologies for designing multi-agent systems are a useful tool to characterize HCI. We specifically illustrate how the selected methodological approach provides enough guidelines to obtain a cockpit HCI design that complies with future SATS specifications.This work was supported in part by Projects MINECO TEC2011-28626-C02-01/02, by program CENIT-ATLANTIDA (cofinanced by Indra and Boeing R&TE), and by ULPGC Precompetitive Research Project (ULPGC Own Program).Publicad

Characterization of Healthy and Pathological Voice Through Measures Based on Nonlinear Dynamics

In this paper, we propose to quantify the quality of the recorded voice through objective nonlinear measures. Quantification of speech signal quality has been traditionally carried out with linear techniques since the classical model of voice production is a linear approximation. Nevertheless, nonlinear behaviors in the voice production process have been shown. This paper studies the usefulness of six nonlinear chaotic measures based on nonlinear dynamics theory in the discrimination between two levels of voice quality: healthy and pathological. The studied measures are first- and second-order Renyi entropies, the correlation entropy and the correlation dimension. These measures were obtained from the speech signal in the phase-space domain. The values of the first minimum of mutual information function and Shannon entropy were also studied. Two databases were used to assess the usefulness of the measures: a multiquality database composed of four levels of voice quality (healthy voice and three levels of pathological voice); and a commercial database (MEEI Voice Disorders) composed of two levels of voice quality (healthy and pathological voices). A classifier based on standard neural networks was implemented in order to evaluate the measures proposed. Global success rates of 82.47% (multiquality database) and 99.69% (commercial database) were obtained.Publicad

Transformation of Hand-Shape Features for a Biometric Identification Approach

The present work presents a biometric identification system for hand shape identification. The different contours have been coded based on angular descriptions forming a Markov chain descriptor. Discrete Hidden Markov Models (DHMM), each representing a target identification class, have been trained with such chains. Features have been calculated from a kernel based on the HMM parameter descriptors. Finally, supervised Support Vector Machines were used to classify parameters from the DHMM kernel. First, the system was modelled using 60 users to tune the DHMM and DHMM_kernel+SVM configuration parameters and finally, the system was checked with the whole database (GPDS database, 144 users with 10 samples per class). Our experiments have obtained similar results in both cases, demonstrating a scalable, stable and robust system. Our experiments have achieved an upper success rate of 99.87% for the GPDS database using three hand samples per class in training mode, and seven hand samples in test mode. Secondly, the authors have verified their algorithms using another independent and public database (the UST database). Our approach has reached 100% and 99.92% success for right and left hand, respectively; showing the robustness and independence of our algorithms. This success was found using as features the transformation of 100 points hand shape with our DHMM kernel, and as classifier Support Vector Machines with linear separating functions, with similar success

High catalytic activity of Au/CeO x/TiO 2(110) controlled by the nature of the mixed-metal oxide at the nanometer level

Mixed-metal oxides play a very important role in many areas of chemistry, physics, materials science, and geochemistry. Recently, there has been a strong interest in understanding phenomena associated with the deposition of oxide nanoparticles on the surface of a second (host) oxide. Here, scanning tunneling microscopy, photoemission, and density-functional calculations are used to study the behavior of ceria nanoparticles deposited on a TiO2(110) surface. The titania substrate imposes nontypical coordination modes on the ceria nanoparticles. In the CeO x/TiO 2(110) systems, the Ce cations adopt an structural geometry and an oxidation state (+3) that are quite different from those seen in bulk ceria or for ceria nanoparticles deposited on metal substrates. The increase in the stability of the Ce 3+ oxidation state leads to an enhancement in the chemical and catalytic activity of the ceria nanoparticles. The codeposition of ceria and gold nanoparticles on a TiO 2(110) substrate generates catalysts with an extremely high activity for the production of hydrogen through the water-gas shift reaction (H 2O + CO → H 2 + CO 2) or for the oxidation of carbon monoxide (2C0 + O 2→2CO 2). The enhanced stability of the Ce 3+ state is an example of structural promotion in catalysis described here on the atomic level. The exploration of mixed-metal oxides at the nanometer level may open avenues for optimizing catalysts through stabilization of unconventional surface structures with special chemical activity.Ministerio de Ciencia e Innovación MAT2008-0491