Restriction-modification systems as genomic parasites in competition for specific sequences.

Restriction-modification (RM) systems are believed to have evolved to protect cells from foreign DNA. However, this hypothesis may not be sufficient to explain the diversity and specificity in sequence recognition, as well as other properties, of these systems. We report that the EcoRI restriction endonuclease-modification methylase (rm) gene pair stabilizes plasmids that carry it and that this stabilization is blocked by an RM of the same sequence specificity (EcoRI or its isoschizomer, Rsr I) but not by an RM of a different specificity (PaeR7I) on another plasmid. The PaeR7I rm likewise stabilizes plasmids, unless an rm gene pair with identical sequence specificity is present. Our analysis supports the following model for stabilization and incompatibility: the descendants of cells that have lost an rm gene pair expose the recognition sites in their chromosomes to lethal attack by any remaining restriction enzymes unless modification by another RM system of the same specificity protects these sites. Competition for specific sequences among these selfish genes may have generated the great diversity and specificity in sequence recognition among RM systems. Such altruistic suicide strategies, similar to those found in virus-infected cells, may have allowed selfish RM systems to spread by effectively competing with other selfish genes.

State of the art in continuous speech recognition.

In the past decade, tremendous advances in the state of the art of automatic speech recognition by machine have taken place. A reduction in the word error rate by more than a factor of 5 and an increase in recognition speeds by several orders of magnitude (brought about by a combination of faster recognition search algorithms and more powerful computers), have combined to make high-accuracy, speaker-independent, continuous speech recognition for large vocabularies possible in real time, on off-the-shelf workstations, without the aid of special hardware. These advances promise to make speech recognition technology readily available to the general public. This paper focuses on the speech recognition advances made through better speech modeling techniques, chiefly through more accurate mathematical modeling of speech sounds.

Training and search methods for speech recognition.

Speech recognition involves three processes: extraction of acoustic indices from the speech signal, estimation of the probability that the observed index string was caused by a hypothesized utterance segment, and determination of the recognized utterance via a search among hypothesized alternatives. This paper is not concerned with the first process. Estimation of the probability of an index string involves a model of index production by any given utterance segment (e.g., a word). Hidden Markov models (HMMs) are used for this purpose [Makhoul, J. & Schwartz, R. (1995) Proc. Natl. Acad. Sci. USA 92, 9956-9963]. Their parameters are state transition probabilities and output probability distributions associated with the transitions. The Baum algorithm that obtains the values of these parameters from speech data via their successive reestimation will be described in this paper. The recognizer wishes to find the most probable utterance that could have caused the observed acoustic index string. That probability is the product of two factors: the probability that the utterance will produce the string and the probability that the speaker will wish to produce the utterance (the language model probability). Even if the vocabulary size is moderate, it is impossible to search for the utterance exhaustively. One practical algorithm is described [Viterbi, A. J. (1967) IEEE Trans. Inf. Theory IT-13, 260-267] that, given the index string, has a high likelihood of finding the most probable utterance.

A chloroplast homologue of the signal recognition particle subunit SRP54 is involved in the posttranslational integration of a protein into thylakoid membranes.

The mechanisms involved in the integration of proteins into the thylakoid membrane are largely unknown. However, many of the steps of this process for the light-harvesting chlorophyll a/b protein (LHCP) have been described and reconstituted in vitro. LHCP is synthesized as a precursor in the cytosol and posttranslationally imported into chloroplasts. Upon translocation across the envelope membranes, the N-terminal transit peptide is cleaved, and the apoprotein is assembled into a soluble "transit complex" and then integrated into the thylakoid membrane via three transmembrane helices. Here we show that 54CP, a chloroplast homologue of the 54-kDa subunit of the mammalian signal recognition particle (SRP54), is essential for transit complex formation, is present in the complex, and is required for LHCP integration into the thylakoid membrane. Our data indicate that 54CP functions posttranslationally as a molecular chaperone and potentially pilots LHCP to the thylakoids. These results demonstrate that one of several pathways for protein routing to the thylakoids is homologous to the SRP pathway and point to a common evolutionary origin for the protein transport systems of the endoplasmic reticulum and the thylakoid membrane.

New multivalent calixarene-based ligands for protein recognition and inhibition

One of the challenges that concerns chemistry is the design of molecules able to modulate protein-protein and protein-ligand interactions, since these are involved in many physiological and pathological processes. The interactions occurring between proteins and their natural counterparts can take place through reciprocal recognition of rather large surface areas, through recognition of single contact points and single residues, through inclusion of the substrates in specific, more or less deep binding sites. In many cases, the design of synthetic molecules able to interfere with the processes involving proteins can benefit from the possibility of exploiting the multivalent effect. Multivalency, widely spread in Nature, consists in the simultaneous formation between two entities (cell-cell, cell-protein, protein-protein) of multiple equivalent ligand-recognition site complexes. In this way the whole interaction results particularly strong and specific. Calixarenes furnish a very interesting scaffold for the preparation of multivalent ligands and in the last years calixarene-based ligands demonstrated their remarkable capability to recognize and inhibit or restore the activity of different proteins, with a high efficiency and selectivity in several recognition phenomena. The relevance and versatility of these ligands is due to the different exposition geometries of the binding units that can be explored exploiting the conformational properties of these macrocycles, the wide variety of functionalities that can be linked to their structure at different distances from the aromatic units and to their intrinsic multivalent nature. With the aim of creating new multivalent systems for protein targeting, the work reported in this thesis regards the synthesis and properties of glycocalix[n]arenes and guanidino calix[4]arenes for different purposes. Firstly, a new bolaamphiphile glycocalix[4]arene in 1,3-alternate geometry, bearing cellobiose, was synthesized for the preparation of targeted drug delivery systems based on liposomes. The formed stable mixed liposomes obtained by mixing the macrocycle with DOPC were shown to be able of exploiting the sugar units emerging from the lipid bilayer to agglutinate Concanavalin A, a lectin specific for glucose. Moreover, always thanks to the presence of the glycocalixarene in the layer, the same liposomes demonstrated through preliminary experiments to be uptaken by cancer cells overexpressing glucose receptors on their exterior surface more efficiently respect to simple DOPC liposomes lacking glucose units in their structure. Then a small library of glycocalix[n]arenes having different valency and geometry was prepared, for the creation of potentially active immunostimulants against Streptococcus pneumoniae, particularly the 19F serotype, one of the most virulent. These synthesized glycocalixarenes bearing β-N-acetylmannosamine as antigenic unit were compared with the natural polysaccharide on the binding to the specific anti-19F human polyclonal antibody, to verify their inhibition potency. Among all, the glycocalixarene based on the conformationally mobile calix[4]arene resulted the more efficient ligand, probably due its major possibility to explore the antibody surface and dispose the antigenic units in a proper arrangement for the interaction process. These results pointed out the importance of how the different multivalent presentation in space of the glycosyl units can influence the recognition phenomena. At last, NMR studies, using particularly 1H-15N HSQC experiments, were performed on selected glycocalix[6]arenes and guanidino calix[4]arenes blocked in the cone geometry, in order to better understand protein-ligand interactions. The glycosylated compounds were studied with Ralstonia solanacearum lectin, in order to better understand the nature of the carbohydrate‐lectin interactions in solution. The series of cationic calixarene was employed with three different acidic proteins: GB1, Fld and alpha synuclein. Particularly GB1 and Fld were observed to interact with all five cationic calix[4]arenes but showing different behaviours and affinities.

Molecular approaches to smart materials via interface recognition and conformationally switchable cavitands

In this thesis the molecular level design of functional materials and systems is reported. In the first part, tetraphosphonate cavitand (Tiiii) recognition properties towards amino acids are studied both in the solid state, through single crystal X-ray diffraction, and in solution, via NMR and ITC experiments. The complexation ability of these supramolecular receptors is then applied to the detection of biologically remarkable N-methylated amino acids and peptides using complex dynamic emulsions-based sensing platforms. In the second part, a general supramolecular approach for surface decoration with single-molecule magnets (SMMs) is presented. The self-assembly of SMMs is achieved through the formation of a multiple hydrogen bonds architecture (UPy-NaPy complexation). Finally we explore the possibility to impart auxetic behavior to polymeric material through the introduction of conformationally switchable monomers, namely tetraquinoxaline cavitands (QxCav). Their interconversion from a closed vase conformation to an extended kite form is studied first in solution, then in polymeric matrixes via pH and tensile stimuli by UV-Vis spectroscopy.

Towards Closed-loop Deep Brain Stimulation: Behavior Recognition from Human STN

Deep brain stimulation (DBS) provides significant therapeutic benefit for movement disorders such as Parkinson’s disease (PD). Current DBS devices lack real-time feedback (thus are open loop) and stimulation parameters are adjusted during scheduled visits with a clinician. A closed-loop DBS system may reduce power consumption and side effects by adjusting stimulation parameters based on patient’s behavior. Thus behavior detection is a major step in designing such systems. Various physiological signals can be used to recognize the behaviors. Subthalamic Nucleus (STN) Local field Potential (LFP) is a great candidate signal for the neural feedback, because it can be recorded from the stimulation lead and does not require additional sensors. This thesis proposes novel detection and classification techniques for behavior recognition based on deep brain LFP. Behavior detection from such signals is the vital step in developing the next generation of closed-loop DBS devices. LFP recordings from 13 subjects are utilized in this study to design and evaluate our method. Recordings were performed during the surgery and the subjects were asked to perform various behavioral tasks. Various techniques are used understand how the behaviors modulate the STN. One method studies the time-frequency patterns in the STN LFP during the tasks. Another method measures the temporal inter-hemispheric connectivity of the STN as well as the connectivity between STN and Pre-frontal Cortex (PFC). Experimental results demonstrate that different behaviors create different m odulation patterns in STN and it’s connectivity. We use these patterns as features to classify behaviors. A method for single trial recognition of the patient’s current task is proposed. This method uses wavelet coefficients as features and support vector machine (SVM) as the classifier for recognition of a selection of behaviors: speech, motor, and random. The proposed method is 82.4% accurate for the binary classification and 73.2% for classifying three tasks. As the next step, a practical behavior detection method which asynchronously detects behaviors is proposed. This method does not use any priori knowledge of behavior onsets and is capable of asynchronously detect the finger movements of PD patients. Our study indicates that there is a motor-modulated inter-hemispheric connectivity between LFP signals recorded bilaterally from STN. We utilize a non-linear regression method to measure this inter-hemispheric connectivity and to detect the finger movements. Our experimental results using STN LFP recorded from eight patients with PD demonstrate this is a promising approach for behavior detection and developing novel closed-loop DBS systems.

Human-computer interaction for optical music recognition tasks

The need to digitise music scores has led to the development of Optical Music Recognition (OMR) tools. Unfortunately, the performance of these systems is still far from providing acceptable results. This situation forces the user to be involved in the process due to the need of correcting the mistakes made during recognition. However, this correction is performed over the output of the system, so these interventions are not exploited to improve the performance of the recognition. This work sets the scenario in which human and machine interact to accurately complete the OMR task with the least possible effort for the user.

Noise-tolerance feasibility for restricted-domain Information Retrieval systems

Information Retrieval systems normally have to work with rather heterogeneous sources, such as Web sites or documents from Optical Character Recognition tools. The correct conversion of these sources into flat text files is not a trivial task since noise may easily be introduced as a result of spelling or typeset errors. Interestingly, this is not a great drawback when the size of the corpus is sufficiently large, since redundancy helps to overcome noise problems. However, noise becomes a serious problem in restricted-domain Information Retrieval specially when the corpus is small and has little or no redundancy. This paper devises an approach which adds noise-tolerance to Information Retrieval systems. A set of experiments carried out in the agricultural domain proves the effectiveness of the approach presented.

RGBD Human-Hand recognition for the Interaction with Robot-Hand

New low cost sensors and the new open free libraries for 3D image processing are permitting to achieve important advances for robot vision applications such as tridimensional object recognition, semantic mapping, navigation and localization of robots, human detection and/or gesture recognition for human-machine interaction. In this paper, a method to recognize the human hand and to track the fingers is proposed. This new method is based on point clouds from range images, RGBD. It does not require visual marks, camera calibration, environment knowledge and complex expensive acquisition systems. Furthermore, this method has been implemented to create a human interface in order to move a robot hand. The human hand is recognized and the movement of the fingers is analyzed. Afterwards, it is imitated from a Barret hand, using communication events programmed from ROS.

Towards a real-time 3D object recognition pipeline on mobile GPGPU computing platforms using low-cost RGB-D sensors

In this project, we propose the implementation of a 3D object recognition system which will be optimized to operate under demanding time constraints. The system must be robust so that objects can be recognized properly in poor light conditions and cluttered scenes with significant levels of occlusion. An important requirement must be met: the system must exhibit a reasonable performance running on a low power consumption mobile GPU computing platform (NVIDIA Jetson TK1) so that it can be integrated in mobile robotics systems, ambient intelligence or ambient assisted living applications. The acquisition system is based on the use of color and depth (RGB-D) data streams provided by low-cost 3D sensors like Microsoft Kinect or PrimeSense Carmine. The range of algorithms and applications to be implemented and integrated will be quite broad, ranging from the acquisition, outlier removal or filtering of the input data and the segmentation or characterization of regions of interest in the scene to the very object recognition and pose estimation. Furthermore, in order to validate the proposed system, we will create a 3D object dataset. It will be composed by a set of 3D models, reconstructed from common household objects, as well as a handful of test scenes in which those objects appear. The scenes will be characterized by different levels of occlusion, diverse distances from the elements to the sensor and variations on the pose of the target objects. The creation of this dataset implies the additional development of 3D data acquisition and 3D object reconstruction applications. The resulting system has many possible applications, ranging from mobile robot navigation and semantic scene labeling to human-computer interaction (HCI) systems based on visual information.

Age effects on symbol sign recognition. Final report.

Federal Highway Administration, Washington, D.C.

An evaluation of existing tire pressure monitoring systems.

Mode of access: Internet.

Age effects on symbol sign recognition /

Mode of access: Internet.

Graph-based Semi-Supervised Learning in Acoustic Modeling for Automatic Speech Recognition

Thesis (Ph.D.)--University of Washington, 2016-06