Recognition of Membrane-Bound Fusion-Peptide/MPER Complexes by the HIV-1 Neutralizing 2F5 Antibody : Implications for Anti-2F5 Immunogenicity

13 p.

The State of Coral Reef Ecosystems of the United States and Pacific Freely Associated States: 2005

Executive Summary: For over three decades, scientists have been documenting the decline of coral reef ecosystems, amid increasing recognition of their value in supporting high biological diversity and their many benefits to human society. Coral reef ecosystems are recognized for their benefits on many levels, such as supporting economies by nurturing fisheries and providing for recreational and tourism opportunities, providing substances useful for medical purposes, performing essential ecosystem services that protect against coastal erosion, and provid-ing a diversity of other, more intangible contributions to many cultures. In the past decade, the increased awareness regarding coral reefs has prompted action by governmental and non-governmental organizations, including increased funding from the U.S. Congress for conservation of these important ecosystems and creation of the U.S. Coral Reef Task Force (USCRTF) to coordinate activities and implement conservation measures [Presidential Executive Order 13089]. Numerous partnerships forged among Federal agencies and state, local, non-governmental, academic and private partners support activities that range from basic science to systematic monitoring of ecosystem com-ponents and are conducted by government agencies, non-governmental organizations, universities, and the private sector. This report shares the results of many of these efforts in the framework of a broad assessment of the condition of coral reef ecosystems across 14 U.S. jurisdictions and Pacific Freely Associated States. This report relies heavily on quantitative, spatially-explicit data that has been collected in the recent past and comparisons with historical data, where possible. The success of this effort can be attributed to the dedication of over 160 report contributors who comprised the expert writing teams for each jurisdiction. The content of the report chapters are the result of their considerable collaborative efforts. The writing teams, which were organized by jurisdiction and comprised of experts from numerous research and management institutions, were provided a basic chapter outline and a length limit, but the content of each chapter was left entirely to their discretion. Each jurisdictional chapter in the report is structured to: 1) describe how each of the primary threats identified in the National Coral Reef Action Strategy (NCRAS) has manifested in the jurisdiction; 2) introduce ongoing monitoring and assessment activities relative to three major categories of inquiry – water quality, benthic habitats, and associated biological communities – and provide summary results in a data-rich format; and 3) highlight recent management activities that promote conservation of coral reef ecosystems.

Study of an object recognition algorithm

This project introduces an improvement of the vision capacity of the robot Robotino operating under ROS platform. A method for recognizing object class using binary features has been developed. The proposed method performs a binary classification of the descriptors of each training image to characterize the appearance of the object class. It presents the use of the binary descriptor based on the difference of gray intensity of the pixels in the image. It shows that binary features are suitable to represent object class in spite of the low resolution and the weak information concerning details of the object in the image. It also introduces the use of a boosting method (Adaboost) of feature selection al- lowing to eliminate redundancies and noise in order to improve the performance of the classifier. Finally, a kernel classifier SVM (Support Vector Machine) is trained with the available database and applied for predictions on new images. One possible future work is to establish a visual servo-control that is to say the reac- tion of the robot to the detection of the object.

Detección automática de presencia/ausencia de atún en imágenes obtenidas mediante sonar de largo alcance a bordo de buques pesqueros y aplicación de Optical Character Recognition (OCR) para la extracción de parámetros

En esta tesis de máster se presenta una metodología para el análisis automatizado de las señales del sonar de largo alcance y una aplicación basada en la técnica de reconocimiento óptico de Optical Character Recognition, caracteres (OCR). La primera contribución consiste en el análisis de imágenes de sonar mediante técnicas de procesamiento de imágenes. En este proceso, para cada imagen de sonar se extraen y se analizan las regiones medibles, obteniendo para cada región un conjunto de características. Con la ayuda de los expertos, cada región es identi cada en una clase (atún o no-atún). De este modo, mediante el aprendizaje supervisado se genera la base de datos y, a su vez, se obtiene un modelo de clasi cación. La segunda contribución es una aplicación OCR que reconoce y extrae de las capturas de pantalla de imágenes de sonar, los caracteres alfanuméricos correspondientes a los parámetros de situación (velocidad, rumbo, localización GPS) y la confi guración de sonar (ganancias, inclinación, ancho del haz). El objetivo de este proceso es el de maximizar la e ficiencia en la detección de atún en el Golfo de Vizcaya y dar el primer paso hacia el desarrollo de un índice de abundancia de esta especie, el cual esté basado en el procesamiento automático de las imágenes de sonar grabadas a bordo de la ota pesquera durante su actividad pesquera rutinaria.

A detailed investigation of classification methods for vowel speech imagery recognition

Accurate and fast decoding of speech imagery from electroencephalographic (EEG) data could serve as a basis for a new generation of brain computer interfaces (BCIs), more portable and easier to use. However, decoding of speech imagery from EEG is a hard problem due to many factors. In this paper we focus on the analysis of the classification step of speech imagery decoding for a three-class vowel speech imagery recognition problem. We empirically show that different classification subtasks may require different classifiers for accurately decoding and obtain a classification accuracy that improves the best results previously published. We further investigate the relationship between the classifiers and different sets of features selected by the common spatial patterns method. Our results indicate that further improvement on BCIs based on speech imagery could be achieved by carefully selecting an appropriate combination of classifiers for the subtasks involved.