Aplicação do teste SSW em indivíduos com perda auditiva neurossensorial usuários e não usuários de aparelho de amplificação sonora individual

OBJETIVO: comparar o desempenho de pacientes usuários e não usuários de AASI, por meio do teste SSW. MÉTODO: o estudo foi realizado em 13 sujeitos com idade entre 55 e 85 anos, com perda auditiva bilateral, sendo seis usuários de prótese auditiva bilateral e sete não usuários de prótese auditiva. O teste de processamento auditivo aplicado foi o teste de reconhecimento de dissílabos em tarefa dicótica SSW. Foi realizado um tratamento estatístico feito por meio da técnica Bootstrap e do Teste de Hipótese Kolmogorov-Smirnov. RESULTADOS: o grupo de usuários apresentou melhor desempenho nas condições estudadas do que o grupo de não usuários, principalmente nas condições competitivas. CONCLUSÃO: os resultados obtidos nessa pesquisa apontam para a eficácia do uso do AASI na melhora da compreensão de fala da população estudada, não somente pela compensação da perda auditiva periférica, mas também pela interferência no processo de envelhecimento do sistema nervoso auditivo central.

How a simple and stereotyped acoustic signal transmits individual information: the song of the White-browed Warbler Basileuterus leucoblepharus

O Pula-pula-assobiador Basileuterus leucoblepharus, um pássaro comum da Mata Atlântica, emite um único e distintivo tipo de canto para defesa territorial. O reconhecimento individual ou entre vizinho e estranho pode ser mais difícil quando as aves compartilham cantos semelhantes. De fato, a análise dos cantos de diferentes indivíduos revelou ligeiras diferenças nos domínios temporal e das freqüências. Efetivamente, um exame cuidadoso dos sinais de 21 indivíduos diferentes por 5 métodos complementares de análise revelou que, primeiro, um ou dois espaços na série tonal ocorrem entre duas notas sucessivas em determinados momentos do canto e, segundo, ocupam posições em tempo e freqüência estereotipadas para cada indivíduo. Experiências de "play-back" confirmam esses dados. Através de experiências de propagação, mostramos que esta informação individual pode ser transmitida somente a curta distância ( < 100 m) na mata. Considerando o tamanho e a repartição dos territórios, este processo de comunicação mostra-se eficiente e bem adaptado.

Frontal sinus recognition for human identification

Many methods based on biometrics such as fingerprint, face, iris, and retina have been proposed for person identification. However, for deceased individuals, such biometric measurements are not available. In such cases, parts of the human skeleton can be used for identification, such as dental records, thorax, vertebrae, shoulder, and frontal sinus. It has been established in prior investigations that the radiographic pattern of frontal sinus is highly variable and unique for every individual. This has stimulated the proposition of measurements of the frontal sinus pattern, obtained from x-ray films, for skeletal identification. This paper presents a frontal sinus recognition method for human identification based on Image Foresting Transform and shape context. Experimental results (ERR = 5,82%) have shown the effectiveness of the proposed method.

The pattern recognition receptors expressed on neutrophils and the associated cytokine profile from different aged patients with Candida-related denture stomatitis

This study evaluated the expression of pattern recognition receptors (PRRs) and activation factors associated with salivary and blood neutrophils from different aged patients diagnosed with Candida-related denture stomatitis (DS). Expression of neutrophil PRRs was determined by flow cytometry and immunofluorescence, and the levels of selected cytokines that influence immune activation were determined by ELISA. The salivary (but not the serum derived) neutrophils of individuals with DS were found to have an increased expression of CD69 regardless of the age of the patient compared to patients without DS. However, these salivary neutrophils had a lower expression of CD66b and CD64. Expression of TLR2 was lower on the salivary-and serum-derived neutrophils from elderly individuals compared to the neutrophils of younger subjects, regardless of whether the individual had DS. Salivary interleukin (IL)-4 was elevated in both of the elderly subject groups (with or without DS). Only elderly DS patients were observed to have increased serum IL-4 levels and reduced salivary IL-12 levels. Younger DS patients showed an increase in salivary IL-10 levels, and both the saliva and the serum levels of IFN-gamma were increased in all of the younger subjects. Our data demonstrated that changes in both the oral immune cells and the protein components could be associated with DS. Furthermore, changes in the blood-derived factors were more associated with age than DS status. (C) 2012 Elsevier Inc. All rights reserved.

Fingerprint Recognition: Enhancement, Feature Extraction and Automatic Evaluation of Algorithms

The identification of people by measuring some traits of individual anatomy or physiology has led to a specific research area called biometric recognition. This thesis is focused on improving fingerprint recognition systems considering three important problems: fingerprint enhancement, fingerprint orientation extraction and automatic evaluation of fingerprint algorithms. An effective extraction of salient fingerprint features depends on the quality of the input fingerprint. If the fingerprint is very noisy, we are not able to detect a reliable set of features. A new fingerprint enhancement method, which is both iterative and contextual, is proposed. This approach detects high-quality regions in fingerprints, selectively applies contextual filtering and iteratively expands like wildfire toward low-quality ones. A precise estimation of the orientation field would greatly simplify the estimation of other fingerprint features (singular points, minutiae) and improve the performance of a fingerprint recognition system. The fingerprint orientation extraction is improved following two directions. First, after the introduction of a new taxonomy of fingerprint orientation extraction methods, several variants of baseline methods are implemented and, pointing out the role of pre- and post- processing, we show how to improve the extraction. Second, the introduction of a new hybrid orientation extraction method, which follows an adaptive scheme, allows to improve significantly the orientation extraction in noisy fingerprints. Scientific papers typically propose recognition systems that integrate many modules and therefore an automatic evaluation of fingerprint algorithms is needed to isolate the contributions that determine an actual progress in the state-of-the-art. The lack of a publicly available framework to compare fingerprint orientation extraction algorithms, motivates the introduction of a new benchmark area called FOE (including fingerprints and manually-marked orientation ground-truth) along with fingerprint matching benchmarks in the FVC-onGoing framework. The success of such framework is discussed by providing relevant statistics: more than 1450 algorithms submitted and two international competitions.

Lower Benefits to the Refugees in Denmark: Missing Recognition?

This article is a study of the contrast between the Danish law concerning reduced economic benefits for newly arrived refugees and immigrants (known as Start Help or as introductory benefit) and the idea of recognition as the condition for individual self-realization and justice. Our assumption is that Start Help both implies economic discrimination against newly arrived persons in Denmark (especially refugees and their families under family reunification rules) and symbolizes a lack of recognition. We have chosen to adopt the theories of recognition (and redistribution) propounded by Axel Honneth and Nancy Fraser to explore our queries about Start Help and the discriminatory impact on its recipients. Empirically the article is based on in-depth qualitative interviews with six refugees who all receive Start Help.

Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data

Smart homes for the aging population have recently started attracting the attention of the research community. The "health state" of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus, our PIR-based smart home technology could improve care and provide valuable information to better understand the functioning of our societies, as well as to inform both individual and collective action in a smart city scenario.

Enhancing Activity Recognition by Fusing Inertial and Biometric Information

Activity recognition is an active research field nowadays, as it enables the development of highly adaptive applications, e.g. in the field of personal health. In this paper, a light high-level fusion algorithm to detect the activity that an individual is performing is presented. The algorithm relies on data gathered from accelerometers placed on different parts of the body, and on biometric sensors. Inertial sensors allow detecting activity by analyzing signal features such as amplitude or peaks. In addition, there is a relationship between the activity intensity and biometric response, which can be considered together with acceleration data to improve the accuracy of activity detection. The proposed algorithm is designed to work with minimum computational cost, being ready to run in a mobile device as part of a context-aware application. In order to enable different user scenarios, the algorithm offers best-effort activity estimation: its quality of estimation depends on the position and number of the available inertial sensors, and also on the presence of biometric information.

Automatic physical activity pattern recognition and meal intake modelling in type 1 diabetes for their inclusion in artificial pancreas systems

La diabetes comprende un conjunto de enfermedades metabólicas que se caracterizan por concentraciones de glucosa en sangre anormalmente altas. En el caso de la diabetes tipo 1 (T1D, por sus siglas en inglés), esta situación es debida a una ausencia total de secreción endógena de insulina, lo que impide a la mayoría de tejidos usar la glucosa. En tales circunstancias, se hace necesario el suministro exógeno de insulina para preservar la vida del paciente; no obstante, siempre con la precaución de evitar caídas agudas de la glucemia por debajo de los niveles recomendados de seguridad. Además de la administración de insulina, las ingestas y la actividad física son factores fundamentales que influyen en la homeostasis de la glucosa. En consecuencia, una gestión apropiada de la T1D debería incorporar estos dos fenómenos fisiológicos, en base a una identificación y un modelado apropiado de los mismos y de sus sorrespondientes efectos en el balance glucosa-insulina. En particular, los sistemas de páncreas artificial –ideados para llevar a cabo un control automático de los niveles de glucemia del paciente– podrían beneficiarse de la integración de esta clase de información. La primera parte de esta tesis doctoral cubre la caracterización del efecto agudo de la actividad física en los perfiles de glucosa. Con este objetivo se ha llevado a cabo una revisión sistemática de la literatura y meta-análisis que determinen las respuestas ante varias modalidades de ejercicio para pacientes con T1D, abordando esta caracterización mediante unas magnitudes que cuantifican las tasas de cambio en la glucemia a lo largo del tiempo. Por otro lado, una identificación fiable de los periodos con actividad física es un requisito imprescindible para poder proveer de esa información a los sistemas de páncreas artificial en condiciones libres y ambulatorias. Por esta razón, la segunda parte de esta tesis está enfocada a la propuesta y evaluación de un sistema automático diseñado para reconocer periodos de actividad física, clasificando su nivel de intensidad (ligera, moderada o vigorosa); así como, en el caso de periodos vigorosos, identificando también la modalidad de ejercicio (aeróbica, mixta o de fuerza). En este sentido, ambos aspectos tienen una influencia específica en el mecanismo metabólico que suministra la energía para llevar a cabo el ejercicio y, por tanto, en las respuestas glucémicas en T1D. En este trabajo se aplican varias combinaciones de técnicas de aprendizaje máquina y reconocimiento de patrones sobre la fusión multimodal de señales de acelerometría y ritmo cardíaco, las cuales describen tanto aspectos mecánicos del movimiento como la respuesta fisiológica del sistema cardiovascular ante el ejercicio. Después del reconocimiento de patrones se incorpora también un módulo de filtrado temporal para sacar partido a la considerable coherencia temporal presente en los datos, una redundancia que se origina en el hecho de que en la práctica, las tendencias en cuanto a actividad física suelen mantenerse estables a lo largo de cierto tiempo, sin fluctuaciones rápidas y repetitivas. El tercer bloque de esta tesis doctoral aborda el tema de las ingestas en el ámbito de la T1D. En concreto, se propone una serie de modelos compartimentales y se evalúan éstos en función de su capacidad para describir matemáticamente el efecto remoto de las concetraciones plasmáticas de insulina exógena sobre las tasas de eleiminación de la glucosa atribuible a la ingesta; un aspecto hasta ahora no incorporado en los principales modelos de paciente para T1D existentes en la literatura. Los datos aquí utilizados se obtuvieron gracias a un experimento realizado por el Institute of Metabolic Science (Universidad de Cambridge, Reino Unido) con 16 pacientes jóvenes. En el experimento, de tipo ‘clamp’ con objetivo variable, se replicaron los perfiles individuales de glucosa, según lo observado durante una visita preliminar tras la ingesta de una cena con o bien alta carga glucémica, o bien baja. Los seis modelos mecanísticos evaluados constaban de: a) submodelos de doble compartimento para las masas de trazadores de glucosa, b) un submodelo de único compartimento para reflejar el efecto remoto de la insulina, c) dos tipos de activación de este mismo efecto remoto (bien lineal, bien con un punto de corte), y d) diversas condiciones iniciales. ABSTRACT Diabetes encompasses a series of metabolic diseases characterized by abnormally high blood glucose concentrations. In the case of type 1 diabetes (T1D), this situation is caused by a total absence of endogenous insulin secretion, which impedes the use of glucose by most tissues. In these circumstances, exogenous insulin supplies are necessary to maintain patient’s life; although caution is always needed to avoid acute decays in glycaemia below safe levels. In addition to insulin administrations, meal intakes and physical activity are fundamental factors influencing glucose homoeostasis. Consequently, a successful management of T1D should incorporate these two physiological phenomena, based on an appropriate identification and modelling of these events and their corresponding effect on the glucose-insulin balance. In particular, artificial pancreas systems –designed to perform an automated control of patient’s glycaemia levels– may benefit from the integration of this type of information. The first part of this PhD thesis covers the characterization of the acute effect of physical activity on glucose profiles. With this aim, a systematic review of literature and metaanalyses are conduced to determine responses to various exercise modalities in patients with T1D, assessed via rates-of-change magnitudes to quantify temporal variations in glycaemia. On the other hand, a reliable identification of physical activity periods is an essential prerequisite to feed artificial pancreas systems with information concerning exercise in ambulatory, free-living conditions. For this reason, the second part of this thesis focuses on the proposal and evaluation of an automatic system devised to recognize physical activity, classifying its intensity level (light, moderate or vigorous) and for vigorous periods, identifying also its exercise modality (aerobic, mixed or resistance); since both aspects have a distinctive influence on the predominant metabolic pathway involved in fuelling exercise, and therefore, in the glycaemic responses in T1D. Various combinations of machine learning and pattern recognition techniques are applied on the fusion of multi-modal signal sources, namely: accelerometry and heart rate measurements, which describe both mechanical aspects of movement and the physiological response of the cardiovascular system to exercise. An additional temporal filtering module is incorporated after recognition in order to exploit the considerable temporal coherence (i.e. redundancy) present in data, which stems from the fact that in practice, physical activity trends are often maintained stable along time, instead of fluctuating rapid and repeatedly. The third block of this PhD thesis addresses meal intakes in the context of T1D. In particular, a number of compartmental models are proposed and compared in terms of their ability to describe mathematically the remote effect of exogenous plasma insulin concentrations on the disposal rates of meal-attributable glucose, an aspect which had not yet been incorporated to the prevailing T1D patient models in literature. Data were acquired in an experiment conduced at the Institute of Metabolic Science (University of Cambridge, UK) on 16 young patients. A variable-target glucose clamp replicated their individual glucose profiles, observed during a preliminary visit after ingesting either a high glycaemic-load or a low glycaemic-load evening meal. The six mechanistic models under evaluation here comprised: a) two-compartmental submodels for glucose tracer masses, b) a single-compartmental submodel for insulin’s remote effect, c) two types of activations for this remote effect (either linear or with a ‘cut-off’ point), and d) diverse forms of initial conditions.

Parent–progeny recognition as a function of MHC odortype identity

The several linked polymorphic genes of the MHC, which has been proposed as a prime determinant of sensed genetic individuality within species, is known to operate in mice by olfactory recognition in aspects of reproductive behavior that concern mate selection, thereby favoring outbreeding and heterozygosity, and also concern the maintenance of pregnancy. A single base-change can alter an individual MHC odortype, and the potential range of combinatorial MHC-determined odortypes is clearly vast. Following our findings that newborn mice already express their MHC odortype (which is detectable at 9 days of gestational age), we sought to determine whether MHC is involved in behavioral aspects of early development, such as rearing. In the studies presented herein, we report the ability and proclivity of mothers to recognize and preferentially retrieve syngeneic (genetically identical) pups from other pups differing only for MHC. Reciprocally, we report the ability of pups to recognize their familial environment, regardless of whether they had been nursed by their biological mothers or by foster mothers. Early learning experiences of the MHC environment are apparently a key element in survival, assuring maternal protection and promoting outbreeding.

Tn5/IS50 target recognition

This communication reports an analysis of Tn5/IS50 target site selection by using an extensive collection of Tn5 and IS50 insertions in two relatively small regions of DNA (less than 1 kb each). For both regions data were collected resulting from in vitro and in vivo transposition events. Since the data sets are consistent and transposase was the only protein present in vitro, this demonstrates that target selection is a property of only transposase. There appear to be two factors governing target selection. A target consensus sequence, which presumably reflects the target selection of individual pairs of Tn5/IS50 bound transposase protomers, was deduced by analyzing all insertion sites. The consensus Tn5/IS50 target site is A-GNTYWRANC-T. However, we observed that independent insertion sites tend to form groups of closely located insertions (clusters), and insertions very often were spaced in a 5-bp periodic fashion. This suggests that Tn5/IS50 target selection is facilitated by more than two transposase protomers binding to the DNA, and, thus, for a site to be a good target, the overlapping neighboring DNA should be a good target, too. Synthetic target sequences were designed and used to test and confirm this model.

Brain-wave recognition of sentences

Electrical and magnetic brain waves of two subjects were recorded for the purpose of recognizing which one of 12 sentences or seven words auditorily presented was processed. The analysis consisted of averaging over trials to create prototypes and test samples, to each of which a Fourier transform was applied, followed by filtering and an inverse transformation to the time domain. The filters used were optimal predictive filters, selected for each subject. A still further improvement was obtained by taking differences between recordings of two electrodes to obtain bipolar pairs that then were used for the same analysis. Recognition rates, based on a least-squares criterion, varied, but the best were above 90%. The first words of prototypes of sentences also were cut and pasted to test, at least partially, the invariance of a word’s brain wave in different sentence contexts. The best result was above 80% correct recognition. Test samples made up only of individual trials also were analyzed. The best result was 134 correct of 288 (47%), which is promising, given that the expected recognition number by chance is just 24 (or 8.3%). The work reported in this paper extends our earlier work on brain-wave recognition of words only. The recognition rates reported here further strengthen the case that recordings of electric brain waves of words or sentences, together with extensive mathematical and statistical analysis, can be the basis of new developments in our understanding of brain processing of language.

Target sequence recognition by the calmodulin superfamily: implications from light chain binding to the regulatory domain of scallop myosin.

Some of the rules for how members of the calmodulin (CaM) superfamily bind to target peptides are revealed by the crystal structure of the regulatory domain of scallop myosin. The structure shows that the IQ motif of the heavy chain in this invertebrate myosin imposes constraints on both the positioning and conformation of the individual lobes of the light chains. In contrast, analysis of the contact residues in the targets bound by Ca(2+)-CaM reveals how the structure of CaM accommodates a broader range of sequences consonant with this protein's functional diversity.

Self-recognition in primates: phylogeny and the salience of species-typical features.

Self-recognition has been explored in nonlinguistic organisms by recording whether individuals touch a dye-marked area on visually inaccessible parts of their face while looking in a mirror or inspect parts of their body while using the mirror's reflection. Only chimpanzees, gorillas, orangutans, and humans over the age of approximately 2 years consistently evidence self-directed mirror-guided behavior without experimenter training. To evaluate the inferred phylogenetic gap between hominoids and other animals, a modified dye-mark test was conducted with cotton-top tamarins (Saguinus oedipus), a New World monkey species. The white hair on the tamarins' head was color-dyed, thereby significantly altering a visually distinctive species-typical feature. Only individuals with dyed hair and prior mirror exposure touched their head while looking in the mirror. They looked longer in the mirror than controls, and some individuals used the mirror to observe visually inaccessible body parts. Prior failures to pass the mirror test may have been due to methodological problems, rather than to phylogenetic differences in the capacity for self-recognition. Specifically, an individual's sensitivity to experimentally modified parts of its body may depend crucially on the relative saliency of the modified part (e.g., face versus hair). Moreover, and in contrast to previous claims, we suggest that the mirror test may not be sufficient for assessing the concept of self or mental state attribution in nonlinguistic organisms.

Toward the ultimate synthesis/recognition system.

This paper predicts speech synthesis, speech recognition, and speaker recognition technology for the year 2001, and it describes the most important research problems to be solved in order to arrive at these ultimate synthesis and recognition systems. The problems for speech synthesis include natural and intelligible voice production, prosody control based on meaning, capability of controlling synthesized voice quality and choosing individual speaking style, multilingual and multidialectal synthesis, choice of application-oriented speaking styles, capability of adding emotion, and synthesis from concepts. The problems for speech recognition include robust recognition against speech variations, adaptation/normalization to variations due to environmental conditions and speakers, automatic knowledge acquisition for acoustic and linguistic modeling, spontaneous speech recognition, naturalness and ease of human-machine interaction, and recognition of emotion. The problems for speaker recognition are similar to those for speech recognition. The research topics related to all these techniques include the use of articulatory and perceptual constraints and evaluation methods for measuring the quality of technology and systems.