Molecular basis for the recognition of two structurally different major histocompatibility complex/peptide complexes by a single T-cell receptor

2C is a typical alloreactive cytotoxic T lymphocyte clone that recognizes two different ligands. These ligands are adducts of the allo-major histocompatibility complex (MHC) molecule H-2Ld and an endogenous octapeptide, and of the self-MHC molecule H-2Kb and another peptide. MHC-binding and T-cell assays with synthetic peptides in combination with molecular modeling studies were employed to analyze the structural basis for this crossreactivity. The molecular surfaces of the two complexes differ greatly in densities and distributions of positive and negative charges. However, modifications of the peptides that increase similarity decrease the capacities of the resulting MHC peptide complexes to induce T-cell responses. Moreover, the roles of the peptides in ligand recognition are different for self- and allo-MHC-restricted T-cell responses. The self-MHC-restricted T-cell responses were finely tuned to recognition of the peptide. The allo-MHC-restricted responses, on the other hand, largely ignore modifications of the peptide. The results strongly suggest that adaptation of the T-cell receptor to the different ligand structures, rather than molecular mimicry by the ligands, is the basis for the crossreactivity of 2C. This conclusion has important implications for T-cell immunology and for the understanding of immunological disorders.

Brain activity evidence for recognition without recollection after early hippocampal damage

Amnesic patients with early and seemingly isolated hippocampal injury show relatively normal recognition memory scores. The cognitive profile of these patients raises the possibility that this recognition performance is maintained mainly by stimulus familiarity in the absence of recollection of contextual information. Here we report electrophysiological data on the status of recognition memory in one of the patients, Jon. Jon's recognition of studied words lacks the event-related potential (ERP) index of recollection, viz., an increase in the late positive component (500–700 ms), under conditions that elicit it reliably in normal subjects. On the other hand, a decrease of the ERP amplitude between 300 and 500 ms, also reliably found in normal subjects, is well preserved. This so-called N400 effect has been linked to stimulus familiarity in previous ERP studies of recognition memory. In Jon, this link is supported by the finding that his recognized and unrecognized studied words evoked topographically distinct ERP effects in the N400 time window. These data suggest that recollection is more dependent on the hippocampal formation than is familiarity, consistent with the view that the hippocampal formation plays a special role in episodic memory, for which recollection is so critical.

Low-cost real-time gesture recognition

A major impediment to developing real-time computer vision systems has been the computational power and level of skill required to process video streams in real-time. This has meant that many researchers have either analysed video streams off-line or used expensive dedicated hardware acceleration techniques. Recent software and hardware developments have greatly eased the development burden of realtime image analysis leading to the development of portable systems using cheap PC hardware and software exploiting the Multimedia Extension (MMX) instruction set of the Intel Pentium chip. This paper describes the implementation of a computationally efficient computer vision system for recognizing hand gestures using efficient coding and MMX-acceleration to achieve real-time performance on low cost hardware.

Progressive structural analysis for dynamic recognition of on-line handwritten mathematical expressions

Structural analysis in handwritten mathematical expressions focuses on interpreting the recognized symbols using geometrical information such as relative sizes and positions of the symbols. Most existing approaches rely on hand-crafted grammar rules to identify semantic relationships among the recognized mathematical symbols. They could easily fail when writing errors occurred. Moreover, they assume the availability of the whole mathematical expression before being able to analyze the semantic information of the expression. To tackle these problems, we propose a progressive structural analysis (PSA) approach for dynamic recognition of handwritten mathematical expressions. The proposed PSA approach is able to provide analysis result immediately after each written input symbol. This has an advantage that users are able to detect any recognition errors immediately and correct only the mis-recognized symbols rather than the whole expression. Experiments conducted on 57 most commonly used mathematical expressions have shown that the PSA approach is able to achieve very good performance results.

Gesture Recognition Using Neural Networks Based on HW/SW Cosimulation Platform

Hardware/software (HW/SW) cosimulation integrates software simulation and hardware simulation simultaneously. Usually, HW/SW co-simulation platform is used to ease debugging and verification for very large-scale integration (VLSI) design. To accelerate the computation of the gesture recognition technique, an HW/SW implementation using field programmable gate array (FPGA) technology is presented in this paper. The major contributions of this work are: (1) a novel design of memory controller in the Verilog Hardware Description Language (Verilog HDL) to reduce memory consumption and load on the processor. (2) The testing part of the neural network algorithm is being hardwired to improve the speed and performance. The American Sign Language gesture recognition is chosen to verify the performance of the approach. Several experiments were carried out on four databases of the gestures (alphabet signs A to Z). (3) The major benefit of this design is that it takes only few milliseconds to recognize the hand gesture which makes it computationally more efficient.

The emerging ASEAN approach to mutual recognition : a comparison with Europe, trans-Tasman, and North America

Existing studies on mutual recognition agreements (MRAs) are mostly based on the European experience. In this paper, we will examine the ongoing attempts to establish a mutual recognition architecture in the Association of Southeast Asian Nations (ASEAN) and seek to explain the region's unique approach to MRAs, which can be classified as a "hub and spoke" model of mutual recognition. On one hand, ASEAN is attempting to establish a quasi-supranational ASEAN-level mechanism to confer "ASEAN qualification" effective in the entire ASEAN region. On the other hand, ASEAN MRAs respect members' national sovereignty, and it is national authorities, not ASEAN institutions, who have the ultimate power to approve or disapprove the supply of services by ASEAN qualification holders. Such a mixed approach to mutual recognition can be best understood as a centralized mechanism for learning-by-doing, rather than centralized recognition per se.

An incremental learning algorithm for face recognition

[EN]In face recognition, where high-dimensional representation spaces are generally used, it is very important to take advantage of all the available information. In particular, many labelled facial images will be accumulated while the recognition system is functioning, and due to practical reasons some of them are often discarded. In this paper, we propose an algorithm for using this information. The algorithm has the fundamental characteristic of being incremental. On the other hand, the algorithm makes use of a combination of classification results for the images in the input sequence. Experiments with sequences obtained with a real person detection and tracking system allow us to analyze the performance of the algorithm, as well as its potential improvements.

Fast and accurate hand pose detection for human-robot interaction

[EN]Enabling natural human-robot interaction using computer vision based applications requires fast and accurate hand detection. However, previous works in this field assume different constraints, like a limitation in the number of detected gestures, because hands are highly complex objects difficult to locate. This paper presents an approach which integrates temporal coherence cues and hand detection based on wrists using a cascade classifier. With this approach, we introduce three main contributions: (1) a transparent initialization mechanism without user participation for segmenting hands independently of their gesture, (2) a larger number of detected gestures as well as a faster training phase than previous cascade classifier based methods and (3) near real-time performance for hand pose detection in video streams.

Hand detection on images based on deformable part models and additional features

Hand detection on images has important applications on person activities recognition. This thesis focuses on PASCAL Visual Object Classes (VOC) system for hand detection. VOC has become a popular system for object detection, based on twenty common objects, and has been released with a successful deformable parts model in VOC2007. A hand detection on an image is made when the system gets a bounding box which overlaps with at least 50% of any ground truth bounding box for a hand on the image. The initial average precision of this detector is around 0.215 compared with a state-of-art of 0.104; however, color and frequency features for detected bounding boxes contain important information for re-scoring, and the average precision can be improved to 0.218 with these features. Results show that these features help on getting higher precision for low recall, even though the average precision is similar.

Object detection and recognition in complex scenes

Dissertação de Mestrado, Engenharia Informática, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2014

Comparative study of human age estimation based on hand-crafted and deep face features

In the past few years, human facial age estimation has drawn a lot of attention in the computer vision and pattern recognition communities because of its important applications in age-based image retrieval, security control and surveillance, biomet- rics, human-computer interaction (HCI) and social robotics. In connection with these investigations, estimating the age of a person from the numerical analysis of his/her face image is a relatively new topic. Also, in problems such as Image Classification the Deep Neural Networks have given the best results in some areas including age estimation. In this work we use three hand-crafted features as well as five deep features that can be obtained from pre-trained deep convolutional neural networks. We do a comparative study of the obtained age estimation results with these features.

Comparison of Computer-Based and Optical Face Recognition Paradigms

The main objectives of this thesis are to validate an improved principal components analysis (IPCA) algorithm on images; designing and simulating a digital model for image compression, face recognition and image detection by using a principal components analysis (PCA) algorithm and the IPCA algorithm; designing and simulating an optical model for face recognition and object detection by using the joint transform correlator (JTC); establishing detection and recognition thresholds for each model; comparing between the performance of the PCA algorithm and the performance of the IPCA algorithm in compression, recognition and, detection; and comparing between the performance of the digital model and the performance of the optical model in recognition and detection. The MATLAB © software was used for simulating the models. PCA is a technique used for identifying patterns in data and representing the data in order to highlight any similarities or differences. The identification of patterns in data of high dimensions (more than three dimensions) is too difficult because the graphical representation of data is impossible. Therefore, PCA is a powerful method for analyzing data. IPCA is another statistical tool for identifying patterns in data. It uses information theory for improving PCA. The joint transform correlator (JTC) is an optical correlator used for synthesizing a frequency plane filter for coherent optical systems. The IPCA algorithm, in general, behaves better than the PCA algorithm in the most of the applications. It is better than the PCA algorithm in image compression because it obtains higher compression, more accurate reconstruction, and faster processing speed with acceptable errors; in addition, it is better than the PCA algorithm in real-time image detection due to the fact that it achieves the smallest error rate as well as remarkable speed. On the other hand, the PCA algorithm performs better than the IPCA algorithm in face recognition because it offers an acceptable error rate, easy calculation, and a reasonable speed. Finally, in detection and recognition, the performance of the digital model is better than the performance of the optical model.

Antibody Recognition Of Plasmodium Falciparum Infected Red Blood Cells By Symptomatic And Asymptomatic Individuals In The Brazilian Amazon.

In the Amazon Region, there is a virtual absence of severe malaria and few fatal cases of naturally occurring Plasmodium falciparum infections; this presents an intriguing and underexplored area of research. In addition to the rapid access of infected persons to effective treatment, one cause of this phenomenon might be the recognition of cytoadherent variant proteins on the infected red blood cell (IRBC) surface, including the var gene encoded P. falciparum erythrocyte membrane protein 1. In order to establish a link between cytoadherence, IRBC surface antibody recognition and the presence or absence of malaria symptoms, we phenotype-selected four Amazonian P. falciparum isolates and the laboratory strain 3D7 for their cytoadherence to CD36 and ICAM1 expressed on CHO cells. We then mapped the dominantly expressed var transcripts and tested whether antibodies from symptomatic or asymptomatic infections showed a differential recognition of the IRBC surface. As controls, the 3D7 lineages expressing severe disease-associated phenotypes were used. We showed that there was no profound difference between the frequency and intensity of antibody recognition of the IRBC-exposed P. falciparum proteins in symptomatic vs. asymptomatic infections. The 3D7 lineages, which expressed severe malaria-associated phenotypes, were strongly recognised by most, but not all plasmas, meaning that the recognition of these phenotypes is frequent in asymptomatic carriers, but is not necessarily a prerequisite to staying free of symptoms.

[factors That Influence Bone Mass Of Healthy Children And Adolescents Measured By Quantitative Ultrasound At The Hand Phalanges: A Systematic Review].

To analyze the main factors that influence bone mass in children and teenagers assessed by quantitative ultrasound (QUS) of the phalanges. A systematic literature review was performed according to the PRISMA method with searches in databases Pubmed/Medline, SciELO and Bireme for the period 2001-2012, in English and Portuguese languages, using the keywords: children, teenagers, adolescent, ultrasound finger phalanges, quantitative ultrasound of phalanges, phalangeal quantitative ultrasound. 21 articles were included. Girls had, in QUS, Amplitude Dependent Speed of Sound (AD-SoS) values higher than boys during pubertal development. The values of the parameters of QUS of the phalanges and dual-energy X-ray Absorptiometry (DXA) increased with the increase of the maturational stage. Anthropometric variables such as age, weight, height, body mass index (BMI), lean mass showed positive correlations with the values of QUS of the phalanges. Physical activity has also been shown to be positively associated with increased bone mass. Factors such as ethnicity, genetics, caloric intake and socioeconomic profile have not yet shown a conclusive relationship and need a larger number of studies. QUS of the phalanges is a method used to evaluate the progressive acquisition of bone mass during growth and maturation of individuals in school phase, by monitoring changes that occur with increasing age and pubertal stage. There were mainly positive influences in variables of sex, maturity, height, weight and BMI, with similar data when compared to the gold standard method, the DXA.

Taxonomic value of foliar characters in Dahlstedtia Malme: Leguminosae, Papilionoideae, Millettieae

Dahlstedtia Malme (Leguminosae) is a neotropical genus, native to the Brazilian Atlantic Forest, and comprises two species, D. pinnata (Benth.) Malme and D. pentaphylla (Taub.) Burk., although it has been considered a monotypic genus by some authors. Leaf anatomy was compared to verify the presence of anatomical characters to help delimit species. Foliar primordium, leaflet, petiolule, petiole and pulvinus were collected from cultivated plants (Campinas, SP, Brazil) and from natural populations (Picinguaba, Ubatuba and Caraguatatuba, SP, Brazil - D. pinnata; Antonina, PR, Brazil - D. pentaphylla). Studies on leaflet surface assessment (Scanning Electron Microscopy), as well as histology and venation analyses were carried out of dehydrated, fresh and fixed material from two species. Leaflet material was macerated for stomatal counts. Histological sections, obtained by free-hand cut or microtome, were stained with Toluidine Blue, Safranin/Alcian Blue, Ferric Chloride, Acid Phloroglucin. Secretory cavities are present in the lamina, petiolule, petiole, pulvinus and leaf primordium in D. pentaphylla, but not in D. pinnata, and can be considered an important character for species diagnosis. Other leaf characters were uninformative in delimiting Dahlstedtia species. There is cambial activity in the petiolule, petiole and pulvinus. This study, associated with other available data, supports the recognition of two species in Dahlstedtia.