Insights into mechanism kinematics for protein motion simulation

Background: The high demanding computational requirements necessary to carry out protein motion simulations make it difficult to obtain information related to protein motion. On the one hand, molecular dynamics simulation requires huge computational resources to achieve satisfactory motion simulations. On the other hand, less accurate procedures such as interpolation methods, do not generate realistic morphs from the kinematic point of view. Analyzing a protein's movement is very similar to serial robots; thus, it is possible to treat the protein chain as a serial mechanism composed of rotational degrees of freedom. Recently, based on this hypothesis, new methodologies have arisen, based on mechanism and robot kinematics, to simulate protein motion. Probabilistic roadmap method, which discretizes the protein configurational space against a scoring function, or the kinetostatic compliance method that minimizes the torques that appear in bonds, aim to simulate protein motion with a reduced computational cost. Results: In this paper a new viewpoint for protein motion simulation, based on mechanism kinematics is presented. The paper describes a set of methodologies, combining different techniques such as structure normalization normalization processes, simulation algorithms and secondary structure detection procedures. The combination of all these procedures allows to obtain kinematic morphs of proteins achieving a very good computational cost-error rate, while maintaining the biological meaning of the obtained structures and the kinematic viability of the obtained motion. Conclusions: The procedure presented in this paper, implements different modules to perform the simulation of the conformational change suffered by a protein when exerting its function. The combination of a main simulation procedure assisted by a secondary structure process, and a side chain orientation strategy, allows to obtain a fast and reliable simulations of protein motion.

Autenticacão de Assinaturas Online: Estudo dos Parâmetros do Dynamic Time Warping e da Representação da Assinatura

O reconhecimento de padões é uma área da inteligência computacional que apoia a resolução de problemas utilizando ferramentas computacionais. Dentre esses problemas podem ser citados o reconhecimento de faces, a identificação de impressões digitais e a autenticação de assinaturas. A autenticação de assinaturas de forma automática tem sua relevância pois está ligada ao reconhecimento de indivíduos e suas credenciais em sistemas complexos e a questões financeiras. Neste trabalho é apresentado um estudo dos parâmetros do Dynamic Time Warping, um algoritmo utilizado para alinhar duas assinaturas e medir a similaridade existente entre elas. Variando-se os principais parâmetros desse algoritmo, sobre uma faixa ampla de valores, foram obtidas as médias dos resultados de erros na classificação, e assim, estas médias foram avaliadas. Com base nas primeiras avaliação, foi identificada a necessidade de se calcular um desses parâmetros de forma dinâmica, o gap cost, a fim de ajustá-lo no uso de uma aplicação prática. Uma proposta para a realização deste cálculo é apresentada e também avaliada. É também proposta e avaliada uma maneira alternativa de representação dos atributos da assinatura, de forma a considerar sua curvatura em cada ponto adquirido no processo de aquisição, utilizando os vetores normais como forma de representação. As avaliações realizadas durante as diversas etapas do estudo consideraram o Equal Error Rate (EER) como indicação de qualidade e as técnicas propostas foram comparadas com técnicas já estabelecidas, obtendo uma média percentual de EER de 3,47%.

Segurança do paciente na utilização de heparina sódica intravenosa: cuidados de enfermagem baseados na análise dos fatores de risco para eventos hemorrágicos

O objeto deste estudo são os eventos hemorrágicos em pacientes críticos que utilizam infusão contínua de heparina sódica. Tem como objetivo geral propor cuidados de enfermagem para pacientes que recebem infusão contínua de heparina, a fim de aumentar a segurança do paciente e reduzir a ocorrência de hemorragia, com base nos fatores de risco. Esta pesquisa procura contribuir com a farmacovigilância da heparina e com a qualidade da assistência de enfermagem. Trata-se de um estudo de coorte retrospectivo, com análise em prontuário, desenvolvido em unidade intensiva e semi-intensiva de um hospital público do Rio de Janeiro. Foram investigados 867 prontuários de 2010 a 2011, encontrando-se uma população de 79 pacientes que fizeram uso de heparina sódica em infusão contínua. As variáveis do estudo foram submetidas a tratamentos estatísticos não paramétricos e a medidas de associação. Os resultados apontam entre os pacientes três diagnósticos: fibrilação atrial, trombose venosa profunda e síndrome coronariana; percebe-se ainda predomínio do sexo feminino (58,23%) e de idosos (md=65 anos). A taxa de eventos hemorrágicos foi de 21,52% e se mostrou mais elevada quando comparada a outros estudos. Evidencia-se que pacientes com TTPa maior do que 100s tem um risco 9,29 vezes maior de apresentar eventos hemorrágicos. Todos os fatores de risco idade maior do que sessenta anos, hipertensão arterial sistêmica, TTPa maior do que 100s, uso prévio de anticoagulante e insuficiência renal apresentam associação positiva com a presença de evento hemorrágico. Entre os pacientes com eventos hemorrágicos, 94,16% apresentam um ou mais fatores de risco para sangramento. Os eventos hemorrágicos foram identificados na pele (47,37%), em sítio de punção, nas vias aéreas, no sistema geniturinário (15,79%) e no sistema gastrointestinal (10,53%). A maioria (55%) dos eventos hemorrágicos foi classificada com tipo 2 de BARC. Na associação entre o dispositivo invasivo utilizado e o tipo de sangramento, 100% dos pacientes com sangramento de via aérea ou do sistema gastrointestinal utilizavam sonda nasoentérica. Paciente com cateter vesical de demora (CVD) tem sete vezes mais risco de hematúria quando comparados com pacientes sem CVD; já pacientes com acesso venoso periférico tem menos risco de sangramento de sítio de punção quando comparados ao pacientes com acesso venoso central (RR= 0,74; 1,29). Essas associações norteiam a assistência de enfermagem e sugerem que o enfermeiro seja cauteloso ao realizar esses procedimentos nos pacientes com heparina sódica. Frente às variações no TTPa dosado, analisou-se o seguimento do protocolo e detectou-se que, nos pacientes com eventos hemorrágicos, a taxa de erro no ajuste da infusão foi maior (76,24%) quando comparada com os pacientes sem eventos hemorrágicos (39,05%). Ao se associar a taxa de erro da infusão com a presença de evento hemorrágico, evidencia-se que, quando a heparina não é ajustada segundo o protocolo, aumenta-se em 3,3 vezes o risco de evento hemorrágico. Portanto, para garantir o uso seguro na infusão de heparina, descrevem-se alguns cuidados específicos de enfermagem baseados nos fatores de risco e na indicação clínica de cada paciente.

Large port count high-speed optical switch fabric for use within datacenters [Invited]

This paper reviews advances in the technology of integrated semiconductor optical amplifier based photonic switch fabrics, with particular emphasis on their suitability for high performance network switches for use within a datacenter. The key requirements for large port count optical switch fabrics are addressed noting the need for switches with substantial port counts. The design options for a 16×16 port photonic switch fabric architecture are discussed and the choice of a Clos-tree design is described. The control strategy, based on arbitration and scheduling, for an integrated switch fabric is explained. The detailed design and fabrication of the switch is followed by experimental characterization, showing net optical gain and operation at 10 Gb/s with bit error rates lower than 10-9. Finally improvements to the switch are suggested, which should result in 100 Gb/s per port operation at energy efficiencies of 3 pJ/bit. © 2011 Optical Society of America.

Limited genetic structure of Gulf Menhaden (Brevoortia patronus), as revealed by microsatellite markers developed for the genus Brevoortia (Clupeidae)

Long-term sustainable management of wild populations should be based on management actions that account for the genetic structure among populations. Knowledge of genetic structure and of the degree of demographic exchange between discreet [sic] populations allows managers to better define management units. However, adequate gene loci for population assessments are not always available. In this study, variable co-dominant DNA loci in the heavily exploited marine genus Brevoortia were developed with a microsatellite-enriched DNA library for the Gulf Menhaden (Brevoortia patronus). Microsatellite marker discovery was followed by genetic characterization of 4 endemic North American Brevoortia species, by using 14 novel loci as well as 5 previously described loci. Power analysis of these loci for use in species identification and genetic stock structure was used to assess their potential to improve the stock definition in the menhaden fishery of the Gulf of Mexico. These loci could be used to reliably identify menhaden species in the Gulf of Mexico with an estimated error rate of α=0.0001. Similarly, a power analysis completed on the basis of observed allele frequencies in Gulf Menhaden indicated that these markers can be used to detect very small levels of genetic divergence (Fst≈0.004) among simulated populations, with sample sizes as small as n=50 individuals. A cursory analysis of genetic structure among Gulf Menhaden sampled throughout the Gulf of Mexico indicated limited genetic structure among sampling locations, although the available sampling did not reach the target number (n=50) necessary to detect minimal values of significant structure.

Spoken document representations for probabilistic retrieval

This paper presents some developments in query expansion and document representation of our spoken document retrieval system and shows how various retrieval techniques affect performance for different sets of transcriptions derived from a common speech source. Modifications of the document representation are used, which combine several techniques for query expansion, knowledge-based on one hand and statistics-based on the other. Taken together, these techniques can improve Average Precision by over 19% relative to a system similar to that which we presented at TREC-7. These new experiments have also confirmed that the degradation of Average Precision due to a word error rate (WER) of 25% is quite small (3.7% relative) and can be reduced to almost zero (0.2% relative). The overall improvement of the retrieval system can also be observed for seven different sets of transcriptions from different recognition engines with a WER ranging from 24.8% to 61.5%. We hope to repeat these experiments when larger document collections become available, in order to evaluate the scalability of these techniques.

The modified Kanerva model for automatic speech recognition

A parallel processing network derived from Kanerva's associative memory theory Kanerva 1984 is shown to be able to train rapidly on connected speech data and recognize further speech data with a label error rate of 0·68%. This modified Kanerva model can be trained substantially faster than other networks with comparable pattern discrimination properties. Kanerva presented his theory of a self-propagating search in 1984, and showed theoretically that large-scale versions of his model would have powerful pattern matching properties. This paper describes how the design for the modified Kanerva model is derived from Kanerva's original theory. Several designs are tested to discover which form may be implemented fastest while still maintaining versatile recognition performance. A method is developed to deal with the time varying nature of the speech signal by recognizing static patterns together with a fixed quantity of contextual information. In order to recognize speech features in different contexts it is necessary for a network to be able to model disjoint pattern classes. This type of modelling cannot be performed by a single layer of links. Network research was once held back by the inability of single-layer networks to solve this sort of problem, and the lack of a training algorithm for multi-layer networks. Rumelhart, Hinton & Williams 1985 provided one solution by demonstrating the "back propagation" training algorithm for multi-layer networks. A second alternative is used in the modified Kanerva model. A non-linear fixed transformation maps the pattern space into a space of higher dimensionality in which the speech features are linearly separable. A single-layer network may then be used to perform the recognition. The advantage of this solution over the other using multi-layer networks lies in the greater power and speed of the single-layer network training algorithm. © 1989.

Investigation of acoustic units for LVCSR systems

One important issue in designing state-of-the-art LVCSR systems is the choice of acoustic units. Context dependent (CD) phones remain the dominant form of acoustic units. They can capture the co-articulatory effect in speech via explicit modelling. However, for other more complicated phonological processes, they rely on the implicit modelling ability of the underlying statistical models. Alternatively, it is possible to construct acoustic models based on higher level linguistic units, for example, syllables, to explicitly capture these complex patterns. When sufficient training data is available, this approach may show an advantage over implicit acoustic modelling. In this paper a wide range of acoustic units are investigated to improve LVCSR system performance. Significant error rate gains up to 7.1% relative (0.8% abs.) were obtained on a state-of-the-art Mandarin Chinese broadcast audio recognition task using word and syllable position dependent triphone and quinphone models. © 2011 IEEE.

20 Gb/s QPSK transmission using an electro-optically modulated vertical-cavity surface-emitting laser

20 Gb/s QPSK transmission over 100 m of OM3 fibre using an EOM VCSEL under QPSK modulation is reported. Bit-error-ratio measurements are carried out to express the quality of the transmission scheme. © 2011 OSA.

Spoken Dialog Challenge 2010: Comparison of live and control test results

The Spoken Dialog Challenge 2010 was an exercise to investigate how different spoken dialog systems perform on the same task. The existing Let's Go Pittsburgh Bus Information System was used as a task and four teams provided systems that were first tested in controlled conditions with speech researchers as users. The three most stable systems were then deployed to real callers. This paper presents the results of the live tests, and compares them with the control test results. Results show considerable variation both between systems and between the control and live tests. Interestingly, relatively high task completion for controlled tests did not always predict relatively high task completion for live tests. Moreover, even though the systems were quite different in their designs, we saw very similar correlations between word error rate and task completion for all the systems. The dialog data collected is available to the research community. © 2011 Association for Computational Linguistics.

Improving LVCSR system combination using neural network language model cross adaptation

State-of-the-art large vocabulary continuous speech recognition (LVCSR) systems often combine outputs from multiple subsystems developed at different sites. Cross system adaptation can be used as an alternative to direct hypothesis level combination schemes such as ROVER. The standard approach involves only cross adapting acoustic models. To fully exploit the complimentary features among sub-systems, language model (LM) cross adaptation techniques can be used. Previous research on multi-level n-gram LM cross adaptation is extended to further include the cross adaptation of neural network LMs in this paper. Using this improved LM cross adaptation framework, significant error rate gains of 4.0%-7.1% relative were obtained over acoustic model only cross adaptation when combining a range of Chinese LVCSR sub-systems used in the 2010 and 2011 DARPA GALE evaluations. Copyright © 2011 ISCA.

Word boundary modelling and full covariance gaussians for Arabic Speech-to-Text systems

This paper describes recent improvements to the Cambridge Arabic Large Vocabulary Continuous Speech Recognition (LVCSR) Speech-to-Text (STT) system. It is shown that wordboundary context markers provide a powerful method to enhance graphemic systems by implicit phonetic information, improving the modelling capability of graphemic systems. In addition, a robust technique for full covariance Gaussian modelling in the Minimum Phone Error (MPE) training framework is introduced. This reduces the full covariance training to a diagonal covariance training problem, thereby solving related robustness problems. The full system results show that the combined use of these and other techniques within a multi-branch combination framework reduces the Word Error Rate (WER) of the complete system by up to 5.9% relative. Copyright © 2011 ISCA.

Use of contexts in language model interpolation and adaptation

Language models (LMs) are often constructed by building multiple individual component models that are combined using context independent interpolation weights. By tuning these weights, using either perplexity or discriminative approaches, it is possible to adapt LMs to a particular task. This paper investigates the use of context dependent weighting in both interpolation and test-time adaptation of language models. Depending on the previous word contexts, a discrete history weighting function is used to adjust the contribution from each component model. As this dramatically increases the number of parameters to estimate, robust weight estimation schemes are required. Several approaches are described in this paper. The first approach is based on MAP estimation where interpolation weights of lower order contexts are used as smoothing priors. The second approach uses training data to ensure robust estimation of LM interpolation weights. This can also serve as a smoothing prior for MAP adaptation. A normalized perplexity metric is proposed to handle the bias of the standard perplexity criterion to corpus size. A range of schemes to combine weight information obtained from training data and test data hypotheses are also proposed to improve robustness during context dependent LM adaptation. In addition, a minimum Bayes' risk (MBR) based discriminative training scheme is also proposed. An efficient weighted finite state transducer (WFST) decoding algorithm for context dependent interpolation is also presented. The proposed technique was evaluated using a state-of-the-art Mandarin Chinese broadcast speech transcription task. Character error rate (CER) reductions up to 7.3 relative were obtained as well as consistent perplexity improvements. © 2012 Elsevier Ltd. All rights reserved.

Syllable language models for Mandarin speech recognition: exploiting character language models.

Mandarin Chinese is based on characters which are syllabic in nature and morphological in meaning. All spoken languages have syllabiotactic rules which govern the construction of syllables and their allowed sequences. These constraints are not as restrictive as those learned from word sequences, but they can provide additional useful linguistic information. Hence, it is possible to improve speech recognition performance by appropriately combining these two types of constraints. For the Chinese language considered in this paper, character level language models (LMs) can be used as a first level approximation to allowed syllable sequences. To test this idea, word and character level n-gram LMs were trained on 2.8 billion words (equivalent to 4.3 billion characters) of texts from a wide collection of text sources. Both hypothesis and model based combination techniques were investigated to combine word and character level LMs. Significant character error rate reductions up to 7.3% relative were obtained on a state-of-the-art Mandarin Chinese broadcast audio recognition task using an adapted history dependent multi-level LM that performs a log-linearly combination of character and word level LMs. This supports the hypothesis that character or syllable sequence models are useful for improving Mandarin speech recognition performance.

Language model cross adaptation for LVCSR system combination

State-of-the-art large vocabulary continuous speech recognition (LVCSR) systems often combine outputs from multiple sub-systems that may even be developed at different sites. Cross system adaptation, in which model adaptation is performed using the outputs from another sub-system, can be used as an alternative to hypothesis level combination schemes such as ROVER. Normally cross adaptation is only performed on the acoustic models. However, there are many other levels in LVCSR systems' modelling hierarchy where complimentary features may be exploited, for example, the sub-word and the word level, to further improve cross adaptation based system combination. It is thus interesting to also cross adapt language models (LMs) to capture these additional useful features. In this paper cross adaptation is applied to three forms of language models, a multi-level LM that models both syllable and word sequences, a word level neural network LM, and the linear combination of the two. Significant error rate reductions of 4.0-7.1% relative were obtained over ROVER and acoustic model only cross adaptation when combining a range of Chinese LVCSR sub-systems used in the 2010 and 2011 DARPA GALE evaluations. © 2012 Elsevier Ltd. All rights reserved.