Multiparametric evaluation of dysphonic severity

In recent years, the multiparametric approach for evaluating perceptual rating of voice quality has been advocated. This study evaluates the accuracy of predicting perceived overall severity of voice quality with a minimal set of aerodynamic, voice range profile (phonetogram), and acoustic perturbation measures. One hundred and twelve dysphonic persons (93 women and 19 men) with laryngeal pathologies and 41 normal controls (35 women and six men) with normal voices participated in this study. Perceptual severity judgement was carried out by four listeners rating the G (overall grade) parameter of the GRBAS scale.(1) The minimal set of instrumental measures was selected based on the ability of the measure to discriminate between dysphonic and normal voices, and to attain at least a moderate correlation with perceived overall severity. Results indicated that perceived overall severity was best described by maximum phonation time of sustained /a/, peak intraoral pressure of the consonant-vowel /pi/ strings production, voice range profile area, and acoustic jitter. Direct-entry discriminant function analysis revealed that these four voice measures in combination correctly predicted 67.3% of perceived overall severity levels.

The clinical implication of the vocal cords-carina distance in anaesthetized Chinese adults during orotracheal intubation

Background. Previous studies have identified no strong correlation between patients' height and tracheal length in anaesthetized patients. We have attempted to compare vocal cords-carina distance (VCD) in Chinese patients with the dimensions of five commonly used tracheal tubes. In addition, we attempted to find a surface anatomy measurement that would identify patients with 'short tracheas'. Methods. We measured VCD in 130 anaesthetized Chinese patients with a fibreoptic bronchoscope. Also measurements were obtained of the distal ends of five commonly used tracheal tubes. We undertook various surface anatomy measurements on the patients' chest and neck region to predict those patients with short tracheas. Results. VCD averaged 12.6 ((SD) 1.4) cm. In seven patients (5%) this distance was particularly short (between 8.8 and 10.4 cm). Many of the commonly used tracheal tubes would be placed close to or beyond the carina when the black intubation guide mark(s) is (are) at the level of the vocal cords. The VCD of

Quantum Information Processing with spin systems: from modeling to possible implementations

The physical implementation of quantum information processing is one of the major challenges of current research. In the last few years, several theoretical proposals and experimental demonstrations on a small number of qubits have been carried out, but a quantum computing architecture that is straightforwardly scalable, universal, and realizable with state-of-the-art technology is still lacking. In particular, a major ultimate objective is the construction of quantum simulators, yielding massively increased computational power in simulating quantum systems. Here we investigate promising routes towards the actual realization of a quantum computer, based on spin systems. The first one employs molecular nanomagnets with a doublet ground state to encode each qubit and exploits the wide chemical tunability of these systems to obtain the proper topology of inter-qubit interactions. Indeed, recent advances in coordination chemistry allow us to arrange these qubits in chains, with tailored interactions mediated by magnetic linkers. These act as switches of the effective qubit-qubit coupling, thus enabling the implementation of one- and two-qubit gates. Molecular qubits can be controlled either by uniform magnetic pulses, either by local electric fields. We introduce here two different schemes for quantum information processing with either global or local control of the inter-qubit interaction and demonstrate the high performance of these platforms by simulating the system time evolution with state-of-the-art parameters. The second architecture we propose is based on a hybrid spin-photon qubit encoding, which exploits the best characteristic of photons, whose mobility is exploited to efficiently establish long-range entanglement, and spin systems, which ensure long coherence times. The setup consists of spin ensembles coherently coupled to single photons within superconducting coplanar waveguide resonators. The tunability of the resonators frequency is exploited as the only manipulation tool to implement a universal set of quantum gates, by bringing the photons into/out of resonance with the spin transition. The time evolution of the system subject to the pulse sequence used to implement complex quantum algorithms has been simulated by numerically integrating the master equation for the system density matrix, thus including the harmful effects of decoherence. Finally a scheme to overcome the leakage of information due to inhomogeneous broadening of the spin ensemble is pointed out. Both the proposed setups are based on state-of-the-art technological achievements. By extensive numerical experiments we show that their performance is remarkably good, even for the implementation of long sequences of gates used to simulate interesting physical models. Therefore, the here examined systems are really promising buildingblocks of future scalable architectures and can be used for proof-of-principle experiments of quantum information processing and quantum simulation.

The ‘first three years’ movement and the infant brain:a review of critiques

This article reviews a particular aspect of the critique of the increasing focus on the brain and neuroscience; what has been termed by some, 'neuromania'. It engages with the growing literature produced in response to the 'first three years' movement: an alliance of child welfare advocates and politicians that draws on the authority of neuroscience to argue that social problems such as inequality, poverty, educational underachievement, violence and mental illness are best addressed through 'early intervention' programmes to protect or enhance emotional and cognitive aspects of children's brain development. The movement began in the United States in the early 1990s and has become increasingly vocal and influential since then, achieving international legitimacy in the United States, Canada, New Zealand, Australia, the UK and elsewhere. The movement, and the brain-based culture of expert-led parent training that has grown with it, has been criticised for claiming scientific authority whilst taking a cavalier approach to scientific method and evidence; for being overly deterministic about the early years of life; for focusing attention on individual parental failings rather than societal or structural problems, for adding to the expanding anxieties of parents and strengthening the intensification of parenting and, ultimately, for redefining the parent-child relationship in biologised, instrumental and dehumanised terms. © 2014 John Wiley & Sons Ltd.

Fluctuations and correlations in sandpiles and interfaces with boundary pinning

Interfaces are studied in an inhomogeneous critical state where boundary pinning is compensated with a ramped force. Sandpiles driven off the self-organized critical point provide an example of this ensemble in the Edwards-Wilkinson (EW) model of kinetic roughening. A crossover from quenched to thermal noise violates spatial and temporal translational invariances. The bulk temporal correlation functions have the effective exponents β1D∼0.88±0.03 and β2D∼0.52±0.05, while at the boundaries βb,1D/2D∼0.47±0.05. The bulk β1D is shown to be reproduced in a randomly kicked thermal EW model.

Fluctuations and correlations in sandpiles and interfaces with boundary pinning

Interfaces are studied in an inhomogeneous critical state where boundary pinning is compensated with a ramped force. Sandpiles driven off the self-organized critical point provide an example of this ensemble in the Edwards-Wilkinson (EW) model of kinetic roughening. A crossover from quenched to thermal noise violates spatial and temporal translational invariances. The bulk temporal correlation functions have the effective exponents β1D∼0.88±0.03 and β2D∼0.52±0.05, while at the boundaries βb,1D/2D∼0.47±0.05. The bulk β1D is shown to be reproduced in a randomly kicked thermal EW model.

EnDNA-Prot:identification of DNA-binding proteins by applying ensemble learning

DNA-binding proteins are crucial for various cellular processes, such as recognition of specific nucleotide, regulation of transcription, and regulation of gene expression. Developing an effective model for identifying DNA-binding proteins is an urgent research problem. Up to now, many methods have been proposed, but most of them focus on only one classifier and cannot make full use of the large number of negative samples to improve predicting performance. This study proposed a predictor called enDNA-Prot for DNA-binding protein identification by employing the ensemble learning technique. Experiential results showed that enDNA-Prot was comparable with DNA-Prot and outperformed DNAbinder and iDNA-Prot with performance improvement in the range of 3.97-9.52% in ACC and 0.08-0.19 in MCC. Furthermore, when the benchmark dataset was expanded with negative samples, the performance of enDNA-Prot outperformed the three existing methods by 2.83-16.63% in terms of ACC and 0.02-0.16 in terms of MCC. It indicated that enDNA-Prot is an effective method for DNA-binding protein identification and expanding training dataset with negative samples can improve its performance. For the convenience of the vast majority of experimental scientists, we developed a user-friendly web-server for enDNA-Prot which is freely accessible to the public. © 2014 Ruifeng Xu et al.

Towards the knowledge-based design of universal influenza epitope ensemble vaccines

Motivation: Influenza A viral heterogeneity remains a significant threat due to unpredictable antigenic drift in seasonal influenza and antigenic shifts caused by the emergence of novel subtypes. Annual review of multivalent influenza vaccines targets strains of influenza A and B likely to be predominant in future influenza seasons. This does not induce broad, cross protective immunity against emergent subtypes. Better strategies are needed to prevent future pandemics. Cross-protection can be achieved by activating CD8+ and CD4+ T cells against highly-conserved regions of the influenza genome. We combine available experimental data with informatics-based immunological predictions to help design vaccines potentially able to induce cross-protective T-cells against multiple influenza subtypes. Results: To exemplify our approach we designed two epitope ensemble vaccines comprising highly-conserved and experimentally-verified immunogenic influenza A epitopes as putative non-seasonal influenza vaccines; one specifically targets the US population and the other is a universal vaccine. The USA-specific vaccine comprised 6 CD8+ T cell epitopes (GILGFVFTL, FMYSDFHFI, GMDPRMCSL, SVKEKDMTK, FYIQMCTEL, DTVNRTHQY) and 3 CD4+ epitopes (KGILGFVFTLTVPSE, EYIMKGVYINTALLN, ILGFVFTLTVPSERG). The universal vaccine comprised 8 CD8+ epitopes: (FMYSDFHFI, GILGFVFTL, ILRGSVAHK, FYIQMCTEL, ILKGKFQTA, YYLEKANKI, VSDGGPNLY, YSHGTGTGY) and the same 3 CD4+ epitopes. Our USA-specific vaccine has a population protection coverage (portion of the population potentially responsive to one or more component epitopes of the vaccine, PPC) of over 96% and 95% coverage of observed influenza subtypes. The universal vaccine has a PPC value of over 97% and 88% coverage of observed subtypes.

Ensemble fuzzy belief intrusion detection design

With the rapid growth of the Internet, computer attacks are increasing at a fast pace and can easily cause millions of dollar in damage to an organization. Detecting these attacks is an important issue of computer security. There are many types of attacks and they fall into four main categories, Denial of Service (DoS) attacks, Probe, User to Root (U2R) attacks, and Remote to Local (R2L) attacks. Within these categories, DoS and Probe attacks continuously show up with greater frequency in a short period of time when they attack systems. They are different from the normal traffic data and can be easily separated from normal activities. On the contrary, U2R and R2L attacks are embedded in the data portions of the packets and normally involve only a single connection. It becomes difficult to achieve satisfactory detection accuracy for detecting these two attacks. Therefore, we focus on studying the ambiguity problem between normal activities and U2R/R2L attacks. The goal is to build a detection system that can accurately and quickly detect these two attacks. In this dissertation, we design a two-phase intrusion detection approach. In the first phase, a correlation-based feature selection algorithm is proposed to advance the speed of detection. Features with poor prediction ability for the signatures of attacks and features inter-correlated with one or more other features are considered redundant. Such features are removed and only indispensable information about the original feature space remains. In the second phase, we develop an ensemble intrusion detection system to achieve accurate detection performance. The proposed method includes multiple feature selecting intrusion detectors and a data mining intrusion detector. The former ones consist of a set of detectors, and each of them uses a fuzzy clustering technique and belief theory to solve the ambiguity problem. The latter one applies data mining technique to automatically extract computer users’ normal behavior from training network traffic data. The final decision is a combination of the outputs of feature selecting and data mining detectors. The experimental results indicate that our ensemble approach not only significantly reduces the detection time but also effectively detect U2R and R2L attacks that contain degrees of ambiguous information.

Variables affecting the collection and preservation of human scent components through instrumental and biological evaluations

In certain European countries and the United States of America, canines have been successfully used in human scent identification. There is however, limited scientific knowledge on the composition of human scent and the detection mechanism that produces an alert from canines. This lack of information has resulted in successful legal challenges to human scent evidence in the courts of law. ^ The main objective of this research was to utilize science to validate the current practices of using human scent evidence in criminal cases. The goals of this study were to utilize Headspace Solid Phase Micro Extraction Gas Chromatography Mass Spectrometry (HS-SPME-GC/MS) to determine the optimum collection and storage conditions for human scent samples, to investigate whether the amount of DNA deposited upon contact with an object affects the alerts produced by human scent identification canines, and to create a prototype pseudo human scent which could be used for training purposes. ^ Hand odor samples which were collected on different sorbent materials and exposed to various environmental conditions showed that human scent samples should be stored without prolonged exposure to UVA/UVB light to allow minimal changes to the overall scent profile. Various methods of collecting human scent from objects were also investigated and it was determined that passive collection methods yields ten times more VOCs by mass than active collection methods. ^ Through the use of polymerase chain reaction (PCR) no correlation was found between the amount of DNA that was deposited upon contact with an object and the alerts that were produced by human scent identification canines. Preliminary studies conducted to create a prototype pseudo human scent showed that it is possible to produce fractions of a human scent sample which can be presented to the canines to determine whether specific fractions or the entire sample is needed to produce alerts by the human scent identification canines. ^

Ensemble Stream Model for Data-Cleaning in Sensor Networks

Ensemble Stream Modeling and Data-cleaning are sensor information processing systems have different training and testing methods by which their goals are cross-validated. This research examines a mechanism, which seeks to extract novel patterns by generating ensembles from data. The main goal of label-less stream processing is to process the sensed events to eliminate the noises that are uncorrelated, and choose the most likely model without over fitting thus obtaining higher model confidence. Higher quality streams can be realized by combining many short streams into an ensemble which has the desired quality. The framework for the investigation is an existing data mining tool. First, to accommodate feature extraction such as a bush or natural forest-fire event we make an assumption of the burnt area (BA*), sensed ground truth as our target variable obtained from logs. Even though this is an obvious model choice the results are disappointing. The reasons for this are two: One, the histogram of fire activity is highly skewed. Two, the measured sensor parameters are highly correlated. Since using non descriptive features does not yield good results, we resort to temporal features. By doing so we carefully eliminate the averaging effects; the resulting histogram is more satisfactory and conceptual knowledge is learned from sensor streams. Second is the process of feature induction by cross-validating attributes with single or multi-target variables to minimize training error. We use F-measure score, which combines precision and accuracy to determine the false alarm rate of fire events. The multi-target data-cleaning trees use information purity of the target leaf-nodes to learn higher order features. A sensitive variance measure such as ƒ-test is performed during each node's split to select the best attribute. Ensemble stream model approach proved to improve when using complicated features with a simpler tree classifier. The ensemble framework for data-cleaning and the enhancements to quantify quality of fitness (30% spatial, 10% temporal, and 90% mobility reduction) of sensor led to the formation of streams for sensor-enabled applications. Which further motivates the novelty of stream quality labeling and its importance in solving vast amounts of real-time mobile streams generated today.

Ensemble Fuzzy Belief Intrusion Detection Design

With the rapid growth of the Internet, computer attacks are increasing at a fast pace and can easily cause millions of dollar in damage to an organization. Detecting these attacks is an important issue of computer security. There are many types of attacks and they fall into four main categories, Denial of Service (DoS) attacks, Probe, User to Root (U2R) attacks, and Remote to Local (R2L) attacks. Within these categories, DoS and Probe attacks continuously show up with greater frequency in a short period of time when they attack systems. They are different from the normal traffic data and can be easily separated from normal activities. On the contrary, U2R and R2L attacks are embedded in the data portions of the packets and normally involve only a single connection. It becomes difficult to achieve satisfactory detection accuracy for detecting these two attacks. Therefore, we focus on studying the ambiguity problem between normal activities and U2R/R2L attacks. The goal is to build a detection system that can accurately and quickly detect these two attacks. In this dissertation, we design a two-phase intrusion detection approach. In the first phase, a correlation-based feature selection algorithm is proposed to advance the speed of detection. Features with poor prediction ability for the signatures of attacks and features inter-correlated with one or more other features are considered redundant. Such features are removed and only indispensable information about the original feature space remains. In the second phase, we develop an ensemble intrusion detection system to achieve accurate detection performance. The proposed method includes multiple feature selecting intrusion detectors and a data mining intrusion detector. The former ones consist of a set of detectors, and each of them uses a fuzzy clustering technique and belief theory to solve the ambiguity problem. The latter one applies data mining technique to automatically extract computer users’ normal behavior from training network traffic data. The final decision is a combination of the outputs of feature selecting and data mining detectors. The experimental results indicate that our ensemble approach not only significantly reduces the detection time but also effectively detect U2R and R2L attacks that contain degrees of ambiguous information.

"Cantata para louvor e glória", de Cleide Dorta Benjamim: preparo para a execução

This study aims to document the preparation process for the implementation of "Cantata To Praise and Glory"(Cantata Para Louvor e Glória), composed in 1985 by composer Cleide Dorta Benjamin (1936 - ), in order to anticipate and address the possible difficulties faced by future conductors, singers and instrumentalists. Issues that relate to the chosen text as well as its structure and importance in the compositional process, are initially addressed through a brief presentation of the work. Consequently, by addressing every detail of the work, solutions to vocal and instrumental execution difficulties, as well as with the conducting gesture, are analyzed, identified and proposed. The work also presents a report that describes all the practical processes of preparation of the Cantata: reissue of the score; selection and profile of singers and instrumentalists; rehearsals; changes and adjustments to the score; and a brief interview with four singers and two musicians who participated in the process. The interaction with the composer throughout a significant part of the work, brings a notable increase of possible clarifications and idea development, enabling a coherent performance of the work.

Prevalência e fatores associados à alteração vocal em idosos institucionalizados com capacidade cognitiva preservada

Voice disorders (VD) in the elderly can interfere negatively in communication, emotional well-being and quality of life, conditions that correspond to greater exposure to illness and social isolation bringing consequent economic impact for the health system. It is assumed that institutionalized confinement, weakness and morbidity associated to nursing home (NH) contribute to transform VD an especially prevalent condition in institutionalized elderly, including those without cognitive impairment. Thus, the aim of this study was to determine the prevalence and associated factors of VD in NH elderly residents without cognitive impairment. There is no epidemiological diagnostic instruments of VD for elderly populations, so the first step of this study was dedicated to prepare and analyze the psychometric properties of a short, inexpensive and easy to use questionnaire named Screening for Voice Disorders in Older Adults (Rastreamento de Alterações Vocais em Idosos—RAVI). The methodological procedures of this step followed the guidelines of the Standards for Educational and Psychological Testing and contemplated validity evidence based on test content, based on response processes, based on internal structure and based on relations with other variables, as well as reliability analysis and clinical consistency. The result of the validation process showed that the RAVI final score generate valid and reliable interpretations for the epidemiological diagnosis of VD in the elderly, which endorsed the use of the questionnaire in the second stage of the study, performed in ten NH located in the city of Natal, Rio Grande do Norte. At this stage, data from socioeconomic and demographic variables, lifestyle, general health conditions and characterization of the institution were collected. It was performed a bivariate analysis and it was calculated the prevalence ratio as a magnitude association measure, with a confidence interval of 95%. The variables with p-value less than 0.20 were included in the multiple logistic regression model that followed the Forward selection method. The odds ratio found in the multivariate model was converted into prevalence ratio and the level of significance was 5%. The sample consisted of 117 subjects with predominance of females and average of 79.68 (± 7.92) years old. The prevalence of VD was 39.3% (95% CI: 30.4-48.1%). The multivariate model showed statistically significant association between VD and depressive symptoms, smoking for a year or more and selfreported hearing loss. In conclusion, VD is a prevalent health condition in NH elderly residents without cognitive impairment and is associated with factors involving psychosocial, lifestyle and communicative disability that require attention of managers and professionals involved with NH environment. Strategies to encourage communication and social integration, actions to combat smoking and minimizing the effects of hearing loss could stimulate the physical well-being, emotional and mental health of institutionalized elderly population, contributing to the vocal and communicative maintenance, a more effective social inclusion and better overall health condition.

Sociophonetic deception: the elements of vocal disguise

The focus of my research is vocal disguise within forensic linguistics. Specifically, I answer the question of what individuals are most likely to do with their voice when they do not want to be recognized by a listener. I also focus on whether specific sociolinguistic characteristics – gender and place of origin – have an effect on the disguise choices that individuals make. My research has found that participants show a preference for altering pitch and/or duration across conditions, as well as taking on a foreign accent. Gender and origin were found to be significant for respect to differences in duration, and significance was also found between origin and pitch. These results suggest that disguise might contain elements of style shifting, and that a speaker's choice is more systematic than random.