Fos and Jun do not bend the AP-1 recognition site.

We have used a solution-based DNA cyclization assay and a gel-phasing method to show that contrary to previous reports [Kerppola, T. K. & Curran, T. (1991) Cell 66, 317-326], basic region leucine zipper proteins Fos and Jun do not significantly bend their AP-1 recognition site. We have constructed two sets of DNA constructs that contain the 7-bp 5'-TGACTCA-3' AP-1 binding site, from either the yeast or the human collagenase gene, which is well separated from and phased by 3-4 helical turns against an A tract-directed bend. The cyclization probabilities of DNAs with altered phasings are not significantly affected by Fos-Jun binding. Similarly, Fos-Jun and Jun-Jun bound to differently phased DNA constructs show insignificant variations in gel mobilities. Both these methods independently indicate that Fos and Jun bend their AP-1 target site by <5 degrees, an observation that has important implications in understanding their mechanism of transcriptional regulation.

Mutagenesis in the C-terminal region of human interleukin 5 reveals a central patch for receptor alpha chain recognition.

Cassette mutagenesis was used to identify side chains in human interleukin 5 (hIL-5) that mediate binding to hIL-5 receptor alpha chain (hIL-5R alpha). A series of single alanine substitutions was introduced into a stretch of residues in the C-terminal region, including helix D, which previously had been implicated in receptor alpha chain recognition and which is aligned on the IL-5 surface so as to allow the topography of receptor binding residues to be examined. hIL-5 and single site mutants were expressed in COS cells, their interactions with hIL-5R alpha were measured by a sandwich surface plasmon resonance biosensor method, and their biological activities were measured by an IL-5-dependent cell proliferation assay. A pattern of mutagenesis effects was observed, with greatest impact near the interface between the two four-helix bundles of IL-5, in particular at residues Glu-110 and Trp-111, and least at the distal ends of the D helices. This pattern suggests the possibility that residues near the interface of the two four-helix bundles in hIL-5 comprise a central patch or hot spot, which constitutes an energetically important alpha chain recognition site. This hypothesis suggests a structural explanation for the 1:1 stoichiometry observed for the complex of hIL-5 with hIL-5R alpha.

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.

Topological SLAM using a graph-matching based method on omnidirectional images

Comunicación presentada en el XI Workshop of Physical Agents, Valencia, 9-10 septiembre 2010.

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.

A new approach for semi-automatic rock mass joints recognition from 3D point clouds

Rock mass characterization requires a deep geometric understanding of the discontinuity sets affecting rock exposures. Recent advances in Light Detection and Ranging (LiDAR) instrumentation currently allow quick and accurate 3D data acquisition, yielding on the development of new methodologies for the automatic characterization of rock mass discontinuities. This paper presents a methodology for the identification and analysis of flat surfaces outcropping in a rocky slope using the 3D data obtained with LiDAR. This method identifies and defines the algebraic equations of the different planes of the rock slope surface by applying an analysis based on a neighbouring points coplanarity test, finding principal orientations by Kernel Density Estimation and identifying clusters by the Density-Based Scan Algorithm with Noise. Different sources of information —synthetic and 3D scanned data— were employed, performing a complete sensitivity analysis of the parameters in order to identify the optimal value of the variables of the proposed method. In addition, raw source files and obtained results are freely provided in order to allow to a more straightforward method comparison aiming to a more reproducible research.

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.

Method for targetless tracking subpixel in-plane movements

We present a targetless motion tracking method for detecting planar movements with subpixel accuracy. This method is based on the computation and tracking of the intersection of two nonparallel straight-line segments in the image of a moving object in a scene. The method is simple and easy to implement because no complex structures have to be detected. It has been tested and validated using a lab experiment consisting of a vibrating object that was recorded with a high-speed camera working at 1000 fps. We managed to track displacements with an accuracy of hundredths of pixel or even of thousandths of pixel in the case of tracking harmonic vibrations. The method is widely applicable because it can be used for distance measuring amplitude and frequency of vibrations with a vision system.

Human Behaviour Recognition based on Trajectory Analysis using Neural Networks

Automated human behaviour analysis has been, and still remains, a challenging problem. It has been dealt from different points of views: from primitive actions to human interaction recognition. This paper is focused on trajectory analysis which allows a simple high level understanding of complex human behaviour. It is proposed a novel representation method of trajectory data, called Activity Description Vector (ADV) based on the number of occurrences of a person is in a specific point of the scenario and the local movements that perform in it. The ADV is calculated for each cell of the scenario in which it is spatially sampled obtaining a cue for different clustering methods. The ADV representation has been tested as the input of several classic classifiers and compared to other approaches using CAVIAR dataset sequences obtaining great accuracy in the recognition of the behaviour of people in a Shopping Centre.

Automaticity in L2 learning: Correlation between vocabulary proficiency and response time in word recognition

Automaticity (in this essay defined as short response time) and fluency in language use are closely connected to each other and some research has been conducted regarding some of the aspects involved. In fact, the notion of automaticity is still debated and many definitions and opinions on what automaticity is have been suggested (Andersson,1987, 1992, 1993, Logan, 1988, Segalowitz, 2010). One aspect that still needs more research is the correlation between vocabulary proficiency (a person’s knowledge about words and ability to use them correctly) and response time in word recognition. Therefore, the aim of this study has been to investigate this correlation using two different tests; one vocabulary size test (Paul Nation) and one lexical decision task (SuperLab) that measures both response time and accuracy. 23 Swedish students partaking in the English 7 course in upper secondary Swedish school were tested. The data were analyzed using a quantitative method where the average values and correlations from the test were used to compare the results. The correlations were calculated using Pearson’s Coefficient Correlations Calculator. The empirical study indicates that vocabulary proficiency is not strongly correlated with shorter response times in word recognition. Rather, the data indicate that L2 learners instead are sensitive to the frequency levels of the vocabulary. The accuracy (number of correct recognized words) and response times correlate with the frequency level of the tested words. This indicates that factors other than vocabulary proficiency are important for the ability to recognize words quickly.

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

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

Development of a DNA-based method for detection and identification of Phytophthora species

Phytophthora diseases cause major losses to agricultural and horticultural production in Australia and worldwide. Most Phytophthora diseases are soilborne and difficult to control, making disease prevention an important component of many disease management strategies. Detection and identification of the causal agent, therefore, is an essential part of effective disease management. This paper describes the development and validation of a DNA-based diagnostic assay that can detect and identify 27 different Phytophthora species. We have designed PCR primers that are specific to the genus Phytophthora. The resulting amplicon after PCR is subjected to digestion by restriction enzymes to yield a specific restriction pattern or fingerprint unique to each species. The restriction patterns are compared with a key comprising restriction patterns of type specimens or representative isolates of 27 different Phytophthora species. A number of fundamental issues, such as genetic diversity within and among species which underpin the development and validation of DNA-based diagnostic assays, are addressed in this paper.

Using generative models for handwritten digit recognition

We describe a method of recognizing handwritten digits by fitting generative models that are built from deformable B-splines with Gaussian ``ink generators'' spaced along the length of the spline. The splines are adjusted using a novel elastic matching procedure based on the Expectation Maximization (EM) algorithm that maximizes the likelihood of the model generating the data. This approach has many advantages. (1) After identifying the model most likely to have generated the data, the system not only produces a classification of the digit but also a rich description of the instantiation parameters which can yield information such as the writing style. (2) During the process of explaining the image, generative models can perform recognition driven segmentation. (3) The method involves a relatively small number of parameters and hence training is relatively easy and fast. (4) Unlike many other recognition schemes it does not rely on some form of pre-normalization of input images, but can handle arbitrary scalings, translations and a limited degree of image rotation. We have demonstrated our method of fitting models to images does not get trapped in poor local minima. The main disadvantage of the method is it requires much more computation than more standard OCR techniques.

Selective recognition of cyclic GMP using a fluorescence-based molecularly imprinted polymer

Guanosine 3′,5′-cyclic monophosphate (cGMP) plays a role as a second messenger in many different biological systems. Given the ubiquitous nature of cGMP, a simple method of detecting cGMP is of interest. To that end a fluorescent polymer with recognition sites for cGMP has been prepared. Its selectivity and sensitivity were investigated and a dose-dependant decrease in fluorescence of the polymer in the presence of cGMP was observed. In contrast, virtually no effect was detected upon application of the structurally similar molecules, guanosine 5′-monophosphate (GMP) and adenosine 3′,5′-cyclic monophosphate (cAMP), thus demonstrating the high selectivity of this polymer. The association constant for the binding of cGMP to the imprinted polymer was determined in order of 3 × 10 5 M -1. A fluorescent, molecularly imprinted polymer that selectively recognises cGMP may have a useful application as a fluorescent chemosensor for cGMP detection in biological samples.

The human factors of automatic speech recognition in control room systems

This thesis addresses the viability of automatic speech recognition for control room systems; with careful system design, automatic speech recognition (ASR) devices can be useful means for human computer interaction in specific types of task. These tasks can be defined as complex verbal activities, such as command and control, and can be paired with spatial tasks, such as monitoring, without detriment. It is suggested that ASR use be confined to routine plant operation, as opposed the critical incidents, due to possible problems of stress on the operators' speech.  It is proposed that using ASR will require operators to adapt a commonly used skill to cater for a novel use of speech. Before using the ASR device, new operators will require some form of training. It is shown that a demonstration by an experienced user of the device can lead to superior performance than instructions. Thus, a relatively cheap and very efficient form of operator training can be supplied by demonstration by experienced ASR operators. From a series of studies into speech based interaction with computers, it is concluded that the interaction be designed to capitalise upon the tendency of operators to use short, succinct, task specific styles of speech. From studies comparing different types of feedback, it is concluded that operators be given screen based feedback, rather than auditory feedback, for control room operation. Feedback will take two forms: the use of the ASR device will require recognition feedback, which will be best supplied using text; the performance of a process control task will require task feedback integrated into the mimic display. This latter feedback can be either textual or symbolic, but it is suggested that symbolic feedback will be more beneficial. Related to both interaction style and feedback is the issue of handling recognition errors. These should be corrected by simple command repetition practices, rather than use error handling dialogues. This method of error correction is held to be non intrusive to primary command and control operations. This thesis also addresses some of the problems of user error in ASR use, and provides a number of recommendations for its reduction.