Identification of divergent protein domains by combining HMM-HMM comparisons and co-occurrence detection.

PubMed

Ghouila, Amel; Florent, Isabelle; Guerfali, Fatma Zahra; Terrapon, Nicolas; Laouini, Dhafer; Yahia, Sadok Ben; Gascuel, Olivier; Bréhélin, Laurent

2014-01-01

Identification of protein domains is a key step for understanding protein function. Hidden Markov Models (HMMs) have proved to be a powerful tool for this task. The Pfam database notably provides a large collection of HMMs which are widely used for the annotation of proteins in sequenced organisms. This is done via sequence/HMM comparisons. However, this approach may lack sensitivity when searching for domains in divergent species. Recently, methods for HMM/HMM comparisons have been proposed and proved to be more sensitive than sequence/HMM approaches in certain cases. However, these approaches are usually not used for protein domain discovery at a genome scale, and the benefit that could be expected from their utilization for this problem has not been investigated. Using proteins of P. falciparum and L. major as examples, we investigate the extent to which HMM/HMM comparisons can identify new domain occurrences not already identified by sequence/HMM approaches. We show that although HMM/HMM comparisons are much more sensitive than sequence/HMM comparisons, they are not sufficiently accurate to be used as a standalone complement of sequence/HMM approaches at the genome scale. Hence, we propose to use domain co-occurrence--the general domain tendency to preferentially appear along with some favorite domains in the proteins--to improve the accuracy of the approach. We show that the combination of HMM/HMM comparisons and co-occurrence domain detection boosts protein annotations. At an estimated False Discovery Rate of 5%, it revealed 901 and 1098 new domains in Plasmodium and Leishmania proteins, respectively. Manual inspection of part of these predictions shows that it contains several domain families that were missing in the two organisms. All new domain occurrences have been integrated in the EuPathDomains database, along with the GO annotations that can be deduced.

Identification of Divergent Protein Domains by Combining HMM-HMM Comparisons and Co-Occurrence Detection

PubMed Central

Ghouila, Amel; Florent, Isabelle; Guerfali, Fatma Zahra; Terrapon, Nicolas; Laouini, Dhafer; Yahia, Sadok Ben; Gascuel, Olivier; Bréhélin, Laurent

2014-01-01

Identification of protein domains is a key step for understanding protein function. Hidden Markov Models (HMMs) have proved to be a powerful tool for this task. The Pfam database notably provides a large collection of HMMs which are widely used for the annotation of proteins in sequenced organisms. This is done via sequence/HMM comparisons. However, this approach may lack sensitivity when searching for domains in divergent species. Recently, methods for HMM/HMM comparisons have been proposed and proved to be more sensitive than sequence/HMM approaches in certain cases. However, these approaches are usually not used for protein domain discovery at a genome scale, and the benefit that could be expected from their utilization for this problem has not been investigated. Using proteins of P. falciparum and L. major as examples, we investigate the extent to which HMM/HMM comparisons can identify new domain occurrences not already identified by sequence/HMM approaches. We show that although HMM/HMM comparisons are much more sensitive than sequence/HMM comparisons, they are not sufficiently accurate to be used as a standalone complement of sequence/HMM approaches at the genome scale. Hence, we propose to use domain co-occurrence — the general domain tendency to preferentially appear along with some favorite domains in the proteins — to improve the accuracy of the approach. We show that the combination of HMM/HMM comparisons and co-occurrence domain detection boosts protein annotations. At an estimated False Discovery Rate of 5%, it revealed 901 and 1098 new domains in Plasmodium and Leishmania proteins, respectively. Manual inspection of part of these predictions shows that it contains several domain families that were missing in the two organisms. All new domain occurrences have been integrated in the EuPathDomains database, along with the GO annotations that can be deduced. PMID:24901648

Seqping: gene prediction pipeline for plant genomes using self-training gene models and transcriptomic data.

PubMed

Chan, Kuang-Lim; Rosli, Rozana; Tatarinova, Tatiana V; Hogan, Michael; Firdaus-Raih, Mohd; Low, Eng-Ti Leslie

2017-01-27

Gene prediction is one of the most important steps in the genome annotation process. A large number of software tools and pipelines developed by various computing techniques are available for gene prediction. However, these systems have yet to accurately predict all or even most of the protein-coding regions. Furthermore, none of the currently available gene-finders has a universal Hidden Markov Model (HMM) that can perform gene prediction for all organisms equally well in an automatic fashion. We present an automated gene prediction pipeline, Seqping that uses self-training HMM models and transcriptomic data. The pipeline processes the genome and transcriptome sequences of the target species using GlimmerHMM, SNAP, and AUGUSTUS pipelines, followed by MAKER2 program to combine predictions from the three tools in association with the transcriptomic evidence. Seqping generates species-specific HMMs that are able to offer unbiased gene predictions. The pipeline was evaluated using the Oryza sativa and Arabidopsis thaliana genomes. Benchmarking Universal Single-Copy Orthologs (BUSCO) analysis showed that the pipeline was able to identify at least 95% of BUSCO's plantae dataset. Our evaluation shows that Seqping was able to generate better gene predictions compared to three HMM-based programs (MAKER2, GlimmerHMM and AUGUSTUS) using their respective available HMMs. Seqping had the highest accuracy in rice (0.5648 for CDS, 0.4468 for exon, and 0.6695 nucleotide structure) and A. thaliana (0.5808 for CDS, 0.5955 for exon, and 0.8839 nucleotide structure). Seqping provides researchers a seamless pipeline to train species-specific HMMs and predict genes in newly sequenced or less-studied genomes. We conclude that the Seqping pipeline predictions are more accurate than gene predictions using the other three approaches with the default or available HMMs.

State-space model with deep learning for functional dynamics estimation in resting-state fMRI.

PubMed

Suk, Heung-Il; Wee, Chong-Yaw; Lee, Seong-Whan; Shen, Dinggang

2016-04-01

Studies on resting-state functional Magnetic Resonance Imaging (rs-fMRI) have shown that different brain regions still actively interact with each other while a subject is at rest, and such functional interaction is not stationary but changes over time. In terms of a large-scale brain network, in this paper, we focus on time-varying patterns of functional networks, i.e., functional dynamics, inherent in rs-fMRI, which is one of the emerging issues along with the network modelling. Specifically, we propose a novel methodological architecture that combines deep learning and state-space modelling, and apply it to rs-fMRI based Mild Cognitive Impairment (MCI) diagnosis. We first devise a Deep Auto-Encoder (DAE) to discover hierarchical non-linear functional relations among regions, by which we transform the regional features into an embedding space, whose bases are complex functional networks. Given the embedded functional features, we then use a Hidden Markov Model (HMM) to estimate dynamic characteristics of functional networks inherent in rs-fMRI via internal states, which are unobservable but can be inferred from observations statistically. By building a generative model with an HMM, we estimate the likelihood of the input features of rs-fMRI as belonging to the corresponding status, i.e., MCI or normal healthy control, based on which we identify the clinical label of a testing subject. In order to validate the effectiveness of the proposed method, we performed experiments on two different datasets and compared with state-of-the-art methods in the literature. We also analyzed the functional networks learned by DAE, estimated the functional connectivities by decoding hidden states in HMM, and investigated the estimated functional connectivities by means of a graph-theoretic approach. Copyright © 2016 Elsevier Inc. All rights reserved.

State-space model with deep learning for functional dynamics estimation in resting-state fMRI

PubMed Central

Suk, Heung-Il; Wee, Chong-Yaw; Lee, Seong-Whan; Shen, Dinggang

2017-01-01

Studies on resting-state functional Magnetic Resonance Imaging (rs-fMRI) have shown that different brain regions still actively interact with each other while a subject is at rest, and such functional interaction is not stationary but changes over time. In terms of a large-scale brain network, in this paper, we focus on time-varying patterns of functional networks, i.e., functional dynamics, inherent in rs-fMRI, which is one of the emerging issues along with the network modelling. Specifically, we propose a novel methodological architecture that combines deep learning and state-space modelling, and apply it to rs-fMRI based Mild Cognitive Impairment (MCI) diagnosis. We first devise a Deep Auto-Encoder (DAE) to discover hierarchical non-linear functional relations among regions, by which we transform the regional features into an embedding space, whose bases are complex functional networks. Given the embedded functional features, we then use a Hidden Markov Model (HMM) to estimate dynamic characteristics of functional networks inherent in rs-fMRI via internal states, which are unobservable but can be inferred from observations statistically. By building a generative model with an HMM, we estimate the likelihood of the input features of rs-fMRI as belonging to the corresponding status, i.e., MCI or normal healthy control, based on which we identify the clinical label of a testing subject. In order to validate the effectiveness of the proposed method, we performed experiments on two different datasets and compared with state-of-the-art methods in the literature. We also analyzed the functional networks learned by DAE, estimated the functional connectivities by decoding hidden states in HMM, and investigated the estimated functional connectivities by means of a graph-theoretic approach. PMID:26774612

Maritime Threat Detection Using Probabilistic Graphical Models

DTIC Science & Technology

2012-01-01

CRF, unlike an HMM, can represent local features, and does not require feature concatenation. MLNs For MLNs, we used Alchemy ( Alchemy 2011), an...open source statistical relational learning and probabilistic inferencing package. Alchemy supports generative and discriminative weight learning, and...that Alchemy creates a new formula for every possible combination of the values for a1 and a2 that fit the type specified in their predicate

An economic evaluation of home management of malaria in Uganda: an interactive Markov model.

PubMed

Lubell, Yoel; Mills, Anne J; Whitty, Christopher J M; Staedke, Sarah G

2010-08-27

Home management of malaria (HMM), promoting presumptive treatment of febrile children in the community, is advocated to improve prompt appropriate treatment of malaria in Africa. The cost-effectiveness of HMM is likely to vary widely in different settings and with the antimalarial drugs used. However, no data on the cost-effectiveness of HMM programmes are available. A Markov model was constructed to estimate the cost-effectiveness of HMM as compared to conventional care for febrile illnesses in children without HMM. The model was populated with data from Uganda, but is designed to be interactive, allowing the user to adjust certain parameters, including the antimalarials distributed. The model calculates the cost per disability adjusted life year averted and presents the incremental cost-effectiveness ratio compared to a threshold value. Model output is stratified by level of malaria transmission and the probability that a child would receive appropriate care from a health facility, to indicate the circumstances in which HMM is likely to be cost-effective. The model output suggests that the cost-effectiveness of HMM varies with malaria transmission, the probability of appropriate care, and the drug distributed. Where transmission is high and the probability of appropriate care is limited, HMM is likely to be cost-effective from a provider perspective. Even with the most effective antimalarials, HMM remains an attractive intervention only in areas of high malaria transmission and in medium transmission areas with a lower probability of appropriate care. HMM is generally not cost-effective in low transmission areas, regardless of which antimalarial is distributed. Considering the analysis from the societal perspective decreases the attractiveness of HMM. Syndromic HMM for children with fever may be a useful strategy for higher transmission settings with limited health care and diagnosis, but is not appropriate for all settings. HMM may need to be tailored to specific settings, accounting for local malaria transmission intensity and availability of health services.

Biologically-Inspired Spike-Based Automatic Speech Recognition of Isolated Digits Over a Reproducing Kernel Hilbert Space

PubMed Central

Li, Kan; Príncipe, José C.

2018-01-01

This paper presents a novel real-time dynamic framework for quantifying time-series structure in spoken words using spikes. Audio signals are converted into multi-channel spike trains using a biologically-inspired leaky integrate-and-fire (LIF) spike generator. These spike trains are mapped into a function space of infinite dimension, i.e., a Reproducing Kernel Hilbert Space (RKHS) using point-process kernels, where a state-space model learns the dynamics of the multidimensional spike input using gradient descent learning. This kernelized recurrent system is very parsimonious and achieves the necessary memory depth via feedback of its internal states when trained discriminatively, utilizing the full context of the phoneme sequence. A main advantage of modeling nonlinear dynamics using state-space trajectories in the RKHS is that it imposes no restriction on the relationship between the exogenous input and its internal state. We are free to choose the input representation with an appropriate kernel, and changing the kernel does not impact the system nor the learning algorithm. Moreover, we show that this novel framework can outperform both traditional hidden Markov model (HMM) speech processing as well as neuromorphic implementations based on spiking neural network (SNN), yielding accurate and ultra-low power word spotters. As a proof of concept, we demonstrate its capabilities using the benchmark TI-46 digit corpus for isolated-word automatic speech recognition (ASR) or keyword spotting. Compared to HMM using Mel-frequency cepstral coefficient (MFCC) front-end without time-derivatives, our MFCC-KAARMA offered improved performance. For spike-train front-end, spike-KAARMA also outperformed state-of-the-art SNN solutions. Furthermore, compared to MFCCs, spike trains provided enhanced noise robustness in certain low signal-to-noise ratio (SNR) regime. PMID:29666568

Biologically-Inspired Spike-Based Automatic Speech Recognition of Isolated Digits Over a Reproducing Kernel Hilbert Space.

PubMed

Li, Kan; Príncipe, José C

2018-01-01

This paper presents a novel real-time dynamic framework for quantifying time-series structure in spoken words using spikes. Audio signals are converted into multi-channel spike trains using a biologically-inspired leaky integrate-and-fire (LIF) spike generator. These spike trains are mapped into a function space of infinite dimension, i.e., a Reproducing Kernel Hilbert Space (RKHS) using point-process kernels, where a state-space model learns the dynamics of the multidimensional spike input using gradient descent learning. This kernelized recurrent system is very parsimonious and achieves the necessary memory depth via feedback of its internal states when trained discriminatively, utilizing the full context of the phoneme sequence. A main advantage of modeling nonlinear dynamics using state-space trajectories in the RKHS is that it imposes no restriction on the relationship between the exogenous input and its internal state. We are free to choose the input representation with an appropriate kernel, and changing the kernel does not impact the system nor the learning algorithm. Moreover, we show that this novel framework can outperform both traditional hidden Markov model (HMM) speech processing as well as neuromorphic implementations based on spiking neural network (SNN), yielding accurate and ultra-low power word spotters. As a proof of concept, we demonstrate its capabilities using the benchmark TI-46 digit corpus for isolated-word automatic speech recognition (ASR) or keyword spotting. Compared to HMM using Mel-frequency cepstral coefficient (MFCC) front-end without time-derivatives, our MFCC-KAARMA offered improved performance. For spike-train front-end, spike-KAARMA also outperformed state-of-the-art SNN solutions. Furthermore, compared to MFCCs, spike trains provided enhanced noise robustness in certain low signal-to-noise ratio (SNR) regime.

Self-Organizing Hidden Markov Model Map (SOHMMM).

PubMed

Ferles, Christos; Stafylopatis, Andreas

2013-12-01

A hybrid approach combining the Self-Organizing Map (SOM) and the Hidden Markov Model (HMM) is presented. The Self-Organizing Hidden Markov Model Map (SOHMMM) establishes a cross-section between the theoretic foundations and algorithmic realizations of its constituents. The respective architectures and learning methodologies are fused in an attempt to meet the increasing requirements imposed by the properties of deoxyribonucleic acid (DNA), ribonucleic acid (RNA), and protein chain molecules. The fusion and synergy of the SOM unsupervised training and the HMM dynamic programming algorithms bring forth a novel on-line gradient descent unsupervised learning algorithm, which is fully integrated into the SOHMMM. Since the SOHMMM carries out probabilistic sequence analysis with little or no prior knowledge, it can have a variety of applications in clustering, dimensionality reduction and visualization of large-scale sequence spaces, and also, in sequence discrimination, search and classification. Two series of experiments based on artificial sequence data and splice junction gene sequences demonstrate the SOHMMM's characteristics and capabilities. Copyright © 2013 Elsevier Ltd. All rights reserved.

Effect of frozen storage on the structure and enzymatic activities of myofibrillar proteins of rabbit skeletal muscle.

PubMed

Kang, J O; Ito, T; Fukazawa, T

1983-01-01

The effect of frozen storage on the biochemical properties of myofibrils, and of their major constituents, actin and myosin, was investigated. Extractability of myofibrillar proteins increased slightly for 3 weeks during frozen storage of muscle, decreasing thereafter. The change in myofibrillar ATPase activity during frozen storage was consistent with that of a reconstituted acto-heavy meromyosin (HMM) complex prepared from frozen stored muscle at the same weight ratio of actin to myosin as in situ. However, myosin ATPase activity showed a different pattern of change when compared with myofibrillar ATPase activity. The maximum velocity of acto-HMM ATPase activity and the apparent dissociation constant of the acto-HMM complex decreased for 1 week during frozen storage, increasing thereafter, indicating that the affinity of actin for myosin was greatest in muscle which had been frozen for 1 week. Copyright © 1983. Published by Elsevier Ltd.

Diagnosis of the OCD Patients using Drawing Features of the Bender Gestalt Shapes

PubMed Central

Boostani, R.; Asadi, F.; Mohammadi, N.

2017-01-01

Background: Since psychological tests such as questionnaire or drawing tests are almost qualitative, their results carry a degree of uncertainty and sometimes subjectivity. The deficiency of all drawing tests is that the assessment is carried out after drawing the objects and lots of information such as pen angle, speed, curvature and pressure are missed through the test. In other words, the psychologists cannot assess their patients while running the tests. One of the famous drawing tests to measure the degree of Obsession Compulsion Disorder (OCD) is the Bender Gestalt, though its reliability is not promising. Objective: The main objective of this study is to make the Bender Gestalt test quantitative; therefore, an optical pen along with a digital tablet is utilized to preserve the key drawing features of OCD patients during the test. Materials and Methods: Among a large population of patients who referred to a special clinic of OCD, 50 under therapy subjects voluntarily took part in this study. In contrast, 50 subjects with no sign of OCD performed the test as a control group. This test contains 9 shapes and the participants were not constraint to draw the shapes in a certain interval of time; consequently, to classify the stream of feature vectors (samples through drawing) Hidden Markov Model (HMM) is employed and its flexibility increased by incorporating the fuzzy technique into its learning scheme. Results: Applying fuzzy HMM classifier to the data stream of subjects could classify two groups up to 95.2% accuracy, whereas the results by applying the standard HMM resulted in 94.5%. In addition, multi-layer perceptron (MLP), as a strong static classifier, is applied to the features and resulted in 86.6% accuracy. Conclusion: Applying the pair of T-test to the results implies a significant supremacy of the fuzzy HMM to the standard HMM and MLP classifiers. PMID:28462208

Automated EEG sleep staging in the term-age baby using a generative modelling approach.

PubMed

Pillay, Kirubin; Dereymaeker, Anneleen; Jansen, Katrien; Naulaers, Gunnar; Van Huffel, Sabine; De Vos, Maarten

2018-06-01

We develop a method for automated four-state sleep classification of preterm and term-born babies at term-age of 38-40 weeks postmenstrual age (the age since the last menstrual cycle of the mother) using multichannel electroencephalogram (EEG) recordings. At this critical age, EEG differentiates from broader quiet sleep (QS) and active sleep (AS) stages to four, more complex states, and the quality and timing of this differentiation is indicative of the level of brain development. However, existing methods for automated sleep classification remain focussed only on QS and AS sleep classification. EEG features were calculated from 16 EEG recordings, in 30 s epochs, and personalized feature scaling used to correct for some of the inter-recording variability, by standardizing each recording's feature data using its mean and standard deviation. Hidden Markov models (HMMs) and Gaussian mixture models (GMMs) were trained, with the HMM incorporating knowledge of the sleep state transition probabilities. Performance of the GMM and HMM (with and without scaling) were compared, and Cohen's kappa agreement calculated between the estimates and clinicians' visual labels. For four-state classification, the HMM proved superior to the GMM. With the inclusion of personalized feature scaling, mean kappa (±standard deviation) was 0.62 (±0.16) compared to the GMM value of 0.55 (±0.15). Without feature scaling, kappas for the HMM and GMM dropped to 0.56 (±0.18) and 0.51 (±0.15), respectively. This is the first study to present a successful method for the automated staging of four states in term-age sleep using multichannel EEG. Results suggested a benefit in incorporating transition information using an HMM, and correcting for inter-recording variability through personalized feature scaling. Determining the timing and quality of these states are indicative of developmental delays in both preterm and term-born babies that may lead to learning problems by school age.

Diagnosis of the OCD Patients using Drawing Features of the Bender Gestalt Shapes.

PubMed

Boostani, R; Asadi, F; Mohammadi, N

2017-03-01

Since psychological tests such as questionnaire or drawing tests are almost qualitative, their results carry a degree of uncertainty and sometimes subjectivity. The deficiency of all drawing tests is that the assessment is carried out after drawing the objects and lots of information such as pen angle, speed, curvature and pressure are missed through the test. In other words, the psychologists cannot assess their patients while running the tests. One of the famous drawing tests to measure the degree of Obsession Compulsion Disorder (OCD) is the Bender Gestalt, though its reliability is not promising. The main objective of this study is to make the Bender Gestalt test quantitative; therefore, an optical pen along with a digital tablet is utilized to preserve the key drawing features of OCD patients during the test. Among a large population of patients who referred to a special clinic of OCD, 50 under therapy subjects voluntarily took part in this study. In contrast, 50 subjects with no sign of OCD performed the test as a control group. This test contains 9 shapes and the participants were not constraint to draw the shapes in a certain interval of time; consequently, to classify the stream of feature vectors (samples through drawing) Hidden Markov Model (HMM) is employed and its flexibility increased by incorporating the fuzzy technique into its learning scheme. Applying fuzzy HMM classifier to the data stream of subjects could classify two groups up to 95.2% accuracy, whereas the results by applying the standard HMM resulted in 94.5%. In addition, multi-layer perceptron (MLP), as a strong static classifier, is applied to the features and resulted in 86.6% accuracy. Applying the pair of T-test to the results implies a significant supremacy of the fuzzy HMM to the standard HMM and MLP classifiers.

Automated EEG sleep staging in the term-age baby using a generative modelling approach

NASA Astrophysics Data System (ADS)

Pillay, Kirubin; Dereymaeker, Anneleen; Jansen, Katrien; Naulaers, Gunnar; Van Huffel, Sabine; De Vos, Maarten

2018-06-01

Objective. We develop a method for automated four-state sleep classification of preterm and term-born babies at term-age of 38-40 weeks postmenstrual age (the age since the last menstrual cycle of the mother) using multichannel electroencephalogram (EEG) recordings. At this critical age, EEG differentiates from broader quiet sleep (QS) and active sleep (AS) stages to four, more complex states, and the quality and timing of this differentiation is indicative of the level of brain development. However, existing methods for automated sleep classification remain focussed only on QS and AS sleep classification. Approach. EEG features were calculated from 16 EEG recordings, in 30 s epochs, and personalized feature scaling used to correct for some of the inter-recording variability, by standardizing each recording’s feature data using its mean and standard deviation. Hidden Markov models (HMMs) and Gaussian mixture models (GMMs) were trained, with the HMM incorporating knowledge of the sleep state transition probabilities. Performance of the GMM and HMM (with and without scaling) were compared, and Cohen’s kappa agreement calculated between the estimates and clinicians’ visual labels. Main results. For four-state classification, the HMM proved superior to the GMM. With the inclusion of personalized feature scaling, mean kappa (±standard deviation) was 0.62 (±0.16) compared to the GMM value of 0.55 (±0.15). Without feature scaling, kappas for the HMM and GMM dropped to 0.56 (±0.18) and 0.51 (±0.15), respectively. Significance. This is the first study to present a successful method for the automated staging of four states in term-age sleep using multichannel EEG. Results suggested a benefit in incorporating transition information using an HMM, and correcting for inter-recording variability through personalized feature scaling. Determining the timing and quality of these states are indicative of developmental delays in both preterm and term-born babies that may lead to learning problems by school age.

Computer-Vision-Assisted Palm Rehabilitation With Supervised Learning.

PubMed

Vamsikrishna, K M; Dogra, Debi Prosad; Desarkar, Maunendra Sankar

2016-05-01

Physical rehabilitation supported by the computer-assisted-interface is gaining popularity among health-care fraternity. In this paper, we have proposed a computer-vision-assisted contactless methodology to facilitate palm and finger rehabilitation. Leap motion controller has been interfaced with a computing device to record parameters describing 3-D movements of the palm of a user undergoing rehabilitation. We have proposed an interface using Unity3D development platform. Our interface is capable of analyzing intermediate steps of rehabilitation without the help of an expert, and it can provide online feedback to the user. Isolated gestures are classified using linear discriminant analysis (DA) and support vector machines (SVM). Finally, a set of discrete hidden Markov models (HMM) have been used to classify gesture sequence performed during rehabilitation. Experimental validation using a large number of samples collected from healthy volunteers reveals that DA and SVM perform similarly while applied on isolated gesture recognition. We have compared the results of HMM-based sequence classification with CRF-based techniques. Our results confirm that both HMM and CRF perform quite similarly when tested on gesture sequences. The proposed system can be used for home-based palm or finger rehabilitation in the absence of experts.

Justification of Fuzzy Declustering Vector Quantization Modeling in Classification of Genotype-Image Phenotypes

NASA Astrophysics Data System (ADS)

Ng, Theam Foo; Pham, Tuan D.; Zhou, Xiaobo

2010-01-01

With the fast development of multi-dimensional data compression and pattern classification techniques, vector quantization (VQ) has become a system that allows large reduction of data storage and computational effort. One of the most recent VQ techniques that handle the poor estimation of vector centroids due to biased data from undersampling is to use fuzzy declustering-based vector quantization (FDVQ) technique. Therefore, in this paper, we are motivated to propose a justification of FDVQ based hidden Markov model (HMM) for investigating its effectiveness and efficiency in classification of genotype-image phenotypes. The performance evaluation and comparison of the recognition accuracy between a proposed FDVQ based HMM (FDVQ-HMM) and a well-known LBG (Linde, Buzo, Gray) vector quantization based HMM (LBG-HMM) will be carried out. The experimental results show that the performances of both FDVQ-HMM and LBG-HMM are almost similar. Finally, we have justified the competitiveness of FDVQ-HMM in classification of cellular phenotype image database by using hypotheses t-test. As a result, we have validated that the FDVQ algorithm is a robust and an efficient classification technique in the application of RNAi genome-wide screening image data.

A Feasibility Study of View-independent Gait Identification

DTIC Science & Technology

2012-03-01

ice skates . For walking, the footprint records for single pixels form clusters that are well separated in space and time. (Any overlap of contact...Pattern Recognition 2007, 1-8. Cheng M-H, Ho M-F & Huang C-L (2008), "Gait Analysis for Human Identification Through Manifold Learning and HMM... Learning and Cybernetics 2005, 4516-4521 Moeslund T B & Granum E (2001), "A Survey of Computer Vision-Based Human Motion Capture", Computer Vision

Assignment of protein sequences to existing domain and family classification systems: Pfam and the PDB.

PubMed

Xu, Qifang; Dunbrack, Roland L

2012-11-01

Automating the assignment of existing domain and protein family classifications to new sets of sequences is an important task. Current methods often miss assignments because remote relationships fail to achieve statistical significance. Some assignments are not as long as the actual domain definitions because local alignment methods often cut alignments short. Long insertions in query sequences often erroneously result in two copies of the domain assigned to the query. Divergent repeat sequences in proteins are often missed. We have developed a multilevel procedure to produce nearly complete assignments of protein families of an existing classification system to a large set of sequences. We apply this to the task of assigning Pfam domains to sequences and structures in the Protein Data Bank (PDB). We found that HHsearch alignments frequently scored more remotely related Pfams in Pfam clans higher than closely related Pfams, thus, leading to erroneous assignment at the Pfam family level. A greedy algorithm allowing for partial overlaps was, thus, applied first to sequence/HMM alignments, then HMM-HMM alignments and then structure alignments, taking care to join partial alignments split by large insertions into single-domain assignments. Additional assignment of repeat Pfams with weaker E-values was allowed after stronger assignments of the repeat HMM. Our database of assignments, presented in a database called PDBfam, contains Pfams for 99.4% of chains >50 residues. The Pfam assignment data in PDBfam are available at http://dunbrack2.fccc.edu/ProtCid/PDBfam, which can be searched by PDB codes and Pfam identifiers. They will be updated regularly.

A constraint-based evolutionary learning approach to the expectation maximization for optimal estimation of the hidden Markov model for speech signal modeling.

PubMed

Huda, Shamsul; Yearwood, John; Togneri, Roberto

2009-02-01

This paper attempts to overcome the tendency of the expectation-maximization (EM) algorithm to locate a local rather than global maximum when applied to estimate the hidden Markov model (HMM) parameters in speech signal modeling. We propose a hybrid algorithm for estimation of the HMM in automatic speech recognition (ASR) using a constraint-based evolutionary algorithm (EA) and EM, the CEL-EM. The novelty of our hybrid algorithm (CEL-EM) is that it is applicable for estimation of the constraint-based models with many constraints and large numbers of parameters (which use EM) like HMM. Two constraint-based versions of the CEL-EM with different fusion strategies have been proposed using a constraint-based EA and the EM for better estimation of HMM in ASR. The first one uses a traditional constraint-handling mechanism of EA. The other version transforms a constrained optimization problem into an unconstrained problem using Lagrange multipliers. Fusion strategies for the CEL-EM use a staged-fusion approach where EM has been plugged with the EA periodically after the execution of EA for a specific period of time to maintain the global sampling capabilities of EA in the hybrid algorithm. A variable initialization approach (VIA) has been proposed using a variable segmentation to provide a better initialization for EA in the CEL-EM. Experimental results on the TIMIT speech corpus show that CEL-EM obtains higher recognition accuracies than the traditional EM algorithm as well as a top-standard EM (VIA-EM, constructed by applying the VIA to EM).

Damage evaluation by a guided wave-hidden Markov model based method

NASA Astrophysics Data System (ADS)

Mei, Hanfei; Yuan, Shenfang; Qiu, Lei; Zhang, Jinjin

2016-02-01

Guided wave based structural health monitoring has shown great potential in aerospace applications. However, one of the key challenges of practical engineering applications is the accurate interpretation of the guided wave signals under time-varying environmental and operational conditions. This paper presents a guided wave-hidden Markov model based method to improve the damage evaluation reliability of real aircraft structures under time-varying conditions. In the proposed approach, an HMM based unweighted moving average trend estimation method, which can capture the trend of damage propagation from the posterior probability obtained by HMM modeling is used to achieve a probabilistic evaluation of the structural damage. To validate the developed method, experiments are performed on a hole-edge crack specimen under fatigue loading condition and a real aircraft wing spar under changing structural boundary conditions. Experimental results show the advantage of the proposed method.

Application of hidden Markov models to biological data mining: a case study

NASA Astrophysics Data System (ADS)

Yin, Michael M.; Wang, Jason T.

2000-04-01

In this paper we present an example of biological data mining: the detection of splicing junction acceptors in eukaryotic genes. Identification or prediction of transcribed sequences from within genomic DNA has been a major rate-limiting step in the pursuit of genes. Programs currently available are far from being powerful enough to elucidate the gene structure completely. Here we develop a hidden Markov model (HMM) to represent the degeneracy features of splicing junction acceptor sites in eukaryotic genes. The HMM system is fully trained using an expectation maximization (EM) algorithm and the system performance is evaluated using the 10-way cross- validation method. Experimental results show that our HMM system can correctly classify more than 94% of the candidate sequences (including true and false acceptor sites) into right categories. About 90% of the true acceptor sites and 96% of the false acceptor sites in the test data are classified correctly. These results are very promising considering that only the local information in DNA is used. The proposed model will be a very important component of an effective and accurate gene structure detection system currently being developed in our lab.

Long-range propagation of plasmon and phonon polaritons in hyperbolic-metamaterial waveguides

NASA Astrophysics Data System (ADS)

Babicheva, Viktoriia E.

2017-12-01

We study photonic multilayer waveguides that include layers of materials and metamaterials with a hyperbolic dispersion (HMM). We consider the long-range propagation of plasmon and phonon polaritons at the dielectric-HMM interface in different waveguide geometries (single boundary or different layers of symmetric cladding). In contrast to the traditional analysis of geometrical parameters, we make an emphasis on the optical properties of constituent materials: solving dispersion equations, we analyze how dielectric and HMM permittivities affect propagation length and mode size of waveguide eigenmodes. We derive figures of merit that should be used for each waveguide in a broad range of permittivity values as well as compare them with plasmonic waveguides. We show that the conventional plasmonic quality factor, which is the ratio of real to imaginary parts of permittivity, is not applicable to the case of waveguides with complex structure. Both telecommunication wavelengths and mid-infrared spectral ranges are of interest considering recent advances in van der Waals materials, such as hexagonal boron nitride. We evaluate the performance of the waveguides with hexagonal boron nitride in the range where it possesses hyperbolic dispersion (wavelength 6.3-7.3 μm), and we show that these waveguides with natural hyperbolic properties have higher propagation lengths than metal-based HMM waveguides.

ATPase activity and light scattering of acto-heavy meromyosin: dependence on ATP concentration and on ionic strength.

PubMed

Dancker, P

1975-01-01

1. The dependence on ATP concentration of ATPase activity and light scattering decrease of acto-HMM could be described at very low ionic strength by one hyperbolic adsorption isotherm with a dissociation constant of 3 X 10(-6)M. Hence the increase of ATP ase activity was paralleled by a decrease in light scattering. At higher values of ionic strength ATPase activity stopped rising before HMM was completely saturated with ATP. Higher ionic strength prevented ATPase activity from further increasing when the rigor links (links between actin and nucleotide-free myosin), which have formerly protected the ATPase against the suppressing action of higher ionic strength have fallen below a certain amount. This protecting influence of rigor links did not require tropomyosin-troponin. 2. For complete activation of ATPase activity by actin less actin was needed when HMM was incompletely saturated with ATP than when it was completely saturated with ATP. 3. The apparent affinity of ATP to regulated acto-HMM (which contained tropomyosin-troponin) was lower than to unregulated acto-HMM (which was devoid of tropomyosin-troponin). In the presence of rigor complexes (indicated by an incomplete decrease of light scattering) the ATPase activity of regulated acto-HMM was higher than that of unregulated acto-HMM. At increasing ATP concentrations the ATPase activity of regulated acto-HMM stopped rising at a similar degree of saturation with ATP as the ATPase activity of unregulated acto-HMM at the same ionic strength.

Hierarchical HMM based learning of navigation primitives for cooperative robotic endovascular catheterization.

PubMed

Rafii-Tari, Hedyeh; Liu, Jindong; Payne, Christopher J; Bicknell, Colin; Yang, Guang-Zhong

2014-01-01

Despite increased use of remote-controlled steerable catheter navigation systems for endovascular intervention, most current designs are based on master configurations which tend to alter natural operator tool interactions. This introduces problems to both ergonomics and shared human-robot control. This paper proposes a novel cooperative robotic catheterization system based on learning-from-demonstration. By encoding the higher-level structure of a catheterization task as a sequence of primitive motions, we demonstrate how to achieve prospective learning for complex tasks whilst incorporating subject-specific variations. A hierarchical Hidden Markov Model is used to model each movement primitive as well as their sequential relationship. This model is applied to generation of motion sequences, recognition of operator input, and prediction of future movements for the robot. The framework is validated by comparing catheter tip motions against the manual approach, showing significant improvements in the quality of catheterization. The results motivate the design of collaborative robotic systems that are intuitive to use, while reducing the cognitive workload of the operator.

The B2 Alternatively Spliced Isoform of Nonmuscle Myosin II-B Lacks Actin-activated MgATPase Activity and In Vitro Motility

PubMed Central

Kim, Kye-Young; Kawamoto, Sachiyo; Bao, Jianjun; Sellers, James R.; Adelstein, Robert S.

2008-01-01

We report the initial biochemical characterization of an alternatively spliced isoform of nonmuscle heavy meromyosin (HMM) II-B2 and compare it with HMM II-B0, the non-spliced isoform. HMM II-B2 is the HMM derivative of an alternatively spliced isoform of endogenous nonmuscle myosin (NM) II-B, which has 21-amino acids inserted into loop 2, near the actin-binding region. NM II-B2 is expressed in the Purkinje cells of the cerebellum as well as in other neuronal cells (Ma et al., Mol. Biol. Cell 15 (2006) 2138-2149). In contrast to any of the previously described isoforms of NM II (II-A, II-B0, II-B1, II-C0 and II-C1) or to smooth muscle myosin, the actin-activated MgATPase activity of HMM II-B2 is not significantly increased from a low, basal level by phosphorylation of the 20 kDa myosin light chain (MLC-20). Moreover, although HMM II-B2 can bind to actin in the absence of ATP and is released in its presence, it cannot propel actin in the sliding actin filament assay following MLC-20 phosphorylation. Unlike HMM II-B2, the actin-activated MgATPase activity of a chimeric HMM with the 21-amino acids II-B2 sequence inserted into the homologous location in the heavy chain of HMM II-C is increased following MLC-20 phosphorylation. This indicates that the effect of the II-B2 insert is myosin heavy chain specific. PMID:18060863

Text Categorization for Multi-Page Documents: A Hybrid Naive Bayes HMM Approach.

ERIC Educational Resources Information Center

Frasconi, Paolo; Soda, Giovanni; Vullo, Alessandro

Text categorization is typically formulated as a concept learning problem where each instance is a single isolated document. This paper is interested in a more general formulation where documents are organized as page sequences, as naturally occurring in digital libraries of scanned books and magazines. The paper describes a method for classifying…

Adolescents and Heavy Metal Music: From the Mouths of Metalheads.

ERIC Educational Resources Information Center

Arnett, Jeffrey

1991-01-01

Attitudes and characteristics of adolescents who like heavy metal music (HMM) were explored in a study of 52 adolescents (largely White males) who liked HMM and 123 who did not in suburban Atlanta (Georgia). HMM is discussed as a reflection of, rather than a cause of, adolescent alienation. (SLD)

SMURFLite: combining simplified Markov random fields with simulated evolution improves remote homology detection for beta-structural proteins into the twilight zone.

PubMed

Daniels, Noah M; Hosur, Raghavendra; Berger, Bonnie; Cowen, Lenore J

2012-05-01

One of the most successful methods to date for recognizing protein sequences that are evolutionarily related has been profile hidden Markov models (HMMs). However, these models do not capture pairwise statistical preferences of residues that are hydrogen bonded in beta sheets. These dependencies have been partially captured in the HMM setting by simulated evolution in the training phase and can be fully captured by Markov random fields (MRFs). However, the MRFs can be computationally prohibitive when beta strands are interleaved in complex topologies. We introduce SMURFLite, a method that combines both simplified MRFs and simulated evolution to substantially improve remote homology detection for beta structures. Unlike previous MRF-based methods, SMURFLite is computationally feasible on any beta-structural motif. We test SMURFLite on all propeller and barrel folds in the mainly-beta class of the SCOP hierarchy in stringent cross-validation experiments. We show a mean 26% (median 16%) improvement in area under curve (AUC) for beta-structural motif recognition as compared with HMMER (a well-known HMM method) and a mean 33% (median 19%) improvement as compared with RAPTOR (a well-known threading method) and even a mean 18% (median 10%) improvement in AUC over HHPred (a profile-profile HMM method), despite HHpred's use of extensive additional training data. We demonstrate SMURFLite's ability to scale to whole genomes by running a SMURFLite library of 207 beta-structural SCOP superfamilies against the entire genome of Thermotoga maritima, and make over a 100 new fold predictions. Availability and implementaion: A webserver that runs SMURFLite is available at: http://smurf.cs.tufts.edu/smurflite/

Hidden Semi-Markov Models and Their Application

NASA Astrophysics Data System (ADS)

Beyreuther, M.; Wassermann, J.

2008-12-01

In the framework of detection and classification of seismic signals there are several different approaches. Our choice for a more robust detection and classification algorithm is to adopt Hidden Markov Models (HMM), a technique showing major success in speech recognition. HMM provide a powerful tool to describe highly variable time series based on a double stochastic model and therefore allow for a broader class description than e.g. template based pattern matching techniques. Being a fully probabilistic model, HMM directly provide a confidence measure of an estimated classification. Furthermore and in contrast to classic artificial neuronal networks or support vector machines, HMM are incorporating the time dependence explicitly in the models thus providing a adequate representation of the seismic signal. As the majority of detection algorithms, HMM are not based on the time and amplitude dependent seismogram itself but on features estimated from the seismogram which characterize the different classes. Features, or in other words characteristic functions, are e.g. the sonogram bands, instantaneous frequency, instantaneous bandwidth or centroid time. In this study we apply continuous Hidden Semi-Markov Models (HSMM), an extension of continuous HMM. The duration probability of a HMM is an exponentially decaying function of the time, which is not a realistic representation of the duration of an earthquake. In contrast HSMM use Gaussians as duration probabilities, which results in an more adequate model. The HSMM detection and classification system is running online as an EARTHWORM module at the Bavarian Earthquake Service. Here the signals that are to be classified simply differ in epicentral distance. This makes it possible to easily decide whether a classification is correct or wrong and thus allows to better evaluate the advantages and disadvantages of the proposed algorithm. The evaluation is based on several month long continuous data and the results are additionally compared to the previously published discrete HMM, continuous HMM and a classic STA/LTA. The intermediate evaluation results are very promising.

Social networks, cooperative breeding, and the human milk microbiome.

PubMed

Meehan, Courtney L; Lackey, Kimberly A; Hagen, Edward H; Williams, Janet E; Roulette, Jennifer; Helfrecht, Courtney; McGuire, Mark A; McGuire, Michelle K

2018-04-26

We present the first available data on the human milk microbiome (HMM) from small-scale societies (hunter-gatherers and horticulturalists in the Central African Republic [CAR]) and explore relationships among subsistence type and seasonality on HMM diversity and composition. Additionally, as humans are cooperative breeders and, throughout our evolutionary history and today, we rear offspring within social networks, we examine associations between the social environment and the HMM. Childrearing and breastfeeding exist in a biosocial nexus, which we hypothesize influences the HMM. Milk samples from hunter-gatherer and horticultural mothers (n = 41) collected over two seasons, were analyzed for their microbial composition. A subsample of these women's infants (n = 33) also participated in detailed naturalistic behavioral observations which identified the breadth of infants' social and caregiving networks and the frequency of contact they had with caregivers. Analyses of milk produced by CAR women indicated that HMM diversity and community composition were related to the size of the mother-infant dyad's social network and frequency of care that infants receive. The abundance of some microbial taxa also varied significantly across populations and seasons. Alpha diversity, however, was not related to subsistence type or seasonality. While the origins of the HMM are not fully understood, our results provide evidence regarding possible feedback loops among the infant, the mother, and the mother's social network that might influence HMM composition. © 2018 Wiley Periodicals, Inc.

Predicting domain-domain interaction based on domain profiles with feature selection and support vector machines

PubMed Central

2010-01-01

Background Protein-protein interaction (PPI) plays essential roles in cellular functions. The cost, time and other limitations associated with the current experimental methods have motivated the development of computational methods for predicting PPIs. As protein interactions generally occur via domains instead of the whole molecules, predicting domain-domain interaction (DDI) is an important step toward PPI prediction. Computational methods developed so far have utilized information from various sources at different levels, from primary sequences, to molecular structures, to evolutionary profiles. Results In this paper, we propose a computational method to predict DDI using support vector machines (SVMs), based on domains represented as interaction profile hidden Markov models (ipHMM) where interacting residues in domains are explicitly modeled according to the three dimensional structural information available at the Protein Data Bank (PDB). Features about the domains are extracted first as the Fisher scores derived from the ipHMM and then selected using singular value decomposition (SVD). Domain pairs are represented by concatenating their selected feature vectors, and classified by a support vector machine trained on these feature vectors. The method is tested by leave-one-out cross validation experiments with a set of interacting protein pairs adopted from the 3DID database. The prediction accuracy has shown significant improvement as compared to InterPreTS (Interaction Prediction through Tertiary Structure), an existing method for PPI prediction that also uses the sequences and complexes of known 3D structure. Conclusions We show that domain-domain interaction prediction can be significantly enhanced by exploiting information inherent in the domain profiles via feature selection based on Fisher scores, singular value decomposition and supervised learning based on support vector machines. Datasets and source code are freely available on the web at http://liao.cis.udel.edu/pub/svdsvm. Implemented in Matlab and supported on Linux and MS Windows. PMID:21034480

groHMM: a computational tool for identifying unannotated and cell type-specific transcription units from global run-on sequencing data.

PubMed

Chae, Minho; Danko, Charles G; Kraus, W Lee

2015-07-16

Global run-on coupled with deep sequencing (GRO-seq) provides extensive information on the location and function of coding and non-coding transcripts, including primary microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and enhancer RNAs (eRNAs), as well as yet undiscovered classes of transcripts. However, few computational tools tailored toward this new type of sequencing data are available, limiting the applicability of GRO-seq data for identifying novel transcription units. Here, we present groHMM, a computational tool in R, which defines the boundaries of transcription units de novo using a two state hidden-Markov model (HMM). A systematic comparison of the performance between groHMM and two existing peak-calling methods tuned to identify broad regions (SICER and HOMER) favorably supports our approach on existing GRO-seq data from MCF-7 breast cancer cells. To demonstrate the broader utility of our approach, we have used groHMM to annotate a diverse array of transcription units (i.e., primary transcripts) from four GRO-seq data sets derived from cells representing a variety of different human tissue types, including non-transformed cells (cardiomyocytes and lung fibroblasts) and transformed cells (LNCaP and MCF-7 cancer cells), as well as non-mammalian cells (from flies and worms). As an example of the utility of groHMM and its application to questions about the transcriptome, we show how groHMM can be used to analyze cell type-specific enhancers as defined by newly annotated enhancer transcripts. Our results show that groHMM can reveal new insights into cell type-specific transcription by identifying novel transcription units, and serve as a complete and useful tool for evaluating functional genomic elements in cells.

Tuning subwavelength-structured focus in the hyperbolic metamaterials

NASA Astrophysics Data System (ADS)

Pan, Rong; Tang, Zhixiang; Pan, Jin; Peng, Runwu

2016-10-01

In this paper, we have systematically investigated light propagating in the hyperbolic metamaterials (HMMs) covered by a subwavelength grating. Based on the equal-frequency contour analyses, light in the HMM is predicted to propagate along a defined direction because of its hyperbolic dispersion, which is similar to the self-collimating effects in photonic crystals. By using the finite-difference time-domain, numerical simulations demonstrate a subwavelength bright spot at the intersection of the adjacent directional beams. Different from the images in homogeneous media, the magnetic fields and electric fields at the spot are layered, especially for the electric fields Ez that is polarized to the propagating direction, i.e., the layer normal direction. Moreover, the Ez is hollow in the layer plane and is stronger than the other electric field component Ex. Therefore, the whole electric field is structured and its pattern can be tuned by the HMM's effective anisotropic electromagnetic parameters. Our results may be useful for generating subwavelength structured light.

Study of environmental sound source identification based on hidden Markov model for robust speech recognition

NASA Astrophysics Data System (ADS)

Nishiura, Takanobu; Nakamura, Satoshi

2003-10-01

Humans communicate with each other through speech by focusing on the target speech among environmental sounds in real acoustic environments. We can easily identify the target sound from other environmental sounds. For hands-free speech recognition, the identification of the target speech from environmental sounds is imperative. This mechanism may also be important for a self-moving robot to sense the acoustic environments and communicate with humans. Therefore, this paper first proposes hidden Markov model (HMM)-based environmental sound source identification. Environmental sounds are modeled by three states of HMMs and evaluated using 92 kinds of environmental sounds. The identification accuracy was 95.4%. This paper also proposes a new HMM composition method that composes speech HMMs and an HMM of categorized environmental sounds for robust environmental sound-added speech recognition. As a result of the evaluation experiments, we confirmed that the proposed HMM composition outperforms the conventional HMM composition with speech HMMs and a noise (environmental sound) HMM trained using noise periods prior to the target speech in a captured signal. [Work supported by Ministry of Public Management, Home Affairs, Posts and Telecommunications of Japan.

A Score of the Ability of a Three-Dimensional Protein Model to Retrieve Its Own Sequence as a Quantitative Measure of Its Quality and Appropriateness

PubMed Central

Martínez-Castilla, León P.; Rodríguez-Sotres, Rogelio

2010-01-01

Background Despite the remarkable progress of bioinformatics, how the primary structure of a protein leads to a three-dimensional fold, and in turn determines its function remains an elusive question. Alignments of sequences with known function can be used to identify proteins with the same or similar function with high success. However, identification of function-related and structure-related amino acid positions is only possible after a detailed study of every protein. Folding pattern diversity seems to be much narrower than sequence diversity, and the amino acid sequences of natural proteins have evolved under a selective pressure comprising structural and functional requirements acting in parallel. Principal Findings The approach described in this work begins by generating a large number of amino acid sequences using ROSETTA [Dantas G et al. (2003) J Mol Biol 332:449–460], a program with notable robustness in the assignment of amino acids to a known three-dimensional structure. The resulting sequence-sets showed no conservation of amino acids at active sites, or protein-protein interfaces. Hidden Markov models built from the resulting sequence sets were used to search sequence databases. Surprisingly, the models retrieved from the database sequences belonged to proteins with the same or a very similar function. Given an appropriate cutoff, the rate of false positives was zero. According to our results, this protocol, here referred to as Rd.HMM, detects fine structural details on the folding patterns, that seem to be tightly linked to the fitness of a structural framework for a specific biological function. Conclusion Because the sequence of the native protein used to create the Rd.HMM model was always amongst the top hits, the procedure is a reliable tool to score, very accurately, the quality and appropriateness of computer-modeled 3D-structures, without the need for spectroscopy data. However, Rd.HMM is very sensitive to the conformational features of the models' backbone. PMID:20830209

Multi-Observation Continuous Density Hidden Markov Models for Anomaly Detection in Full Motion Video

DTIC Science & Technology

2012-06-01

response profiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.5 Method for measuring angular movement versus average direction...of movement 49 3.6 Method for calculating Angular Deviation, Θ . . . . . . . . . . . . . . . . . . 50 4.1 HMM produced by K Means Learning for agent H... Angular Deviation. A random variable, the difference in heading (in degrees) from the overall direction of movement over the sequence • S : Speed. A

Ensemble Learning Method for Hidden Markov Models

DTIC Science & Technology

2014-12-01

Ensemble HMM landmine detector Mine signatures vary according to the mine type, mine size , and burial depth. Similarly, clutter signatures vary with soil ...approaches for the di erent K groups depending on their size and homogeneity. In particular, we investigate the maximum likelihood (ML), the minimum...propose using and optimizing various training approaches for the different K groups depending on their size and homogeneity. In particular, we

Multi-sensor physical activity recognition in free-living.

PubMed

Ellis, Katherine; Godbole, Suneeta; Kerr, Jacqueline; Lanckriet, Gert

Physical activity monitoring in free-living populations has many applications for public health research, weight-loss interventions, context-aware recommendation systems and assistive technologies. We present a system for physical activity recognition that is learned from a free-living dataset of 40 women who wore multiple sensors for seven days. The multi-level classification system first learns low-level codebook representations for each sensor and uses a random forest classifier to produce minute-level probabilities for each activity class. Then a higher-level HMM layer learns patterns of transitions and durations of activities over time to smooth the minute-level predictions. [Formula: see text].

DNA motif elucidation using belief propagation.

PubMed

Wong, Ka-Chun; Chan, Tak-Ming; Peng, Chengbin; Li, Yue; Zhang, Zhaolei

2013-09-01

Protein-binding microarray (PBM) is a high-throughout platform that can measure the DNA-binding preference of a protein in a comprehensive and unbiased manner. A typical PBM experiment can measure binding signal intensities of a protein to all the possible DNA k-mers (k=8∼10); such comprehensive binding affinity data usually need to be reduced and represented as motif models before they can be further analyzed and applied. Since proteins can often bind to DNA in multiple modes, one of the major challenges is to decompose the comprehensive affinity data into multimodal motif representations. Here, we describe a new algorithm that uses Hidden Markov Models (HMMs) and can derive precise and multimodal motifs using belief propagations. We describe an HMM-based approach using belief propagations (kmerHMM), which accepts and preprocesses PBM probe raw data into median-binding intensities of individual k-mers. The k-mers are ranked and aligned for training an HMM as the underlying motif representation. Multiple motifs are then extracted from the HMM using belief propagations. Comparisons of kmerHMM with other leading methods on several data sets demonstrated its effectiveness and uniqueness. Especially, it achieved the best performance on more than half of the data sets. In addition, the multiple binding modes derived by kmerHMM are biologically meaningful and will be useful in interpreting other genome-wide data such as those generated from ChIP-seq. The executables and source codes are available at the authors' websites: e.g. http://www.cs.toronto.edu/∼wkc/kmerHMM.

Zoledronic acid overcomes chemoresistance and immunosuppression of malignant mesothelioma

PubMed Central

Kopecka, Joanna; Gazzano, Elena; Sara, Orecchia; Ghigo, Dario; Riganti, Chiara

2015-01-01

The human malignant mesothelioma (HMM) is characterized by a chemoresistant and immunosuppressive phenotype. An effective strategy to restore chemosensitivity and immune reactivity against HMM is lacking. We investigated whether the use of zoledronic acid is an effective chemo-immunosensitizing strategy. We compared primary HMM samples with non-transformed mesothelial cells. HMM cells had higher rate of cholesterol and isoprenoid synthesis, constitutive activation of Ras/extracellular signal-regulated kinase1/2 (ERK1/2)/hypoxia inducible factor-1α (HIF-1α) pathway and up-regulation of the drug efflux transporter P-glycoprotein (Pgp). By decreasing the isoprenoid supply, zoledronic acid down-regulated the Ras/ERK1/2/HIF-1α/Pgp axis and chemosensitized the HMM cells to Pgp substrates. The HMM cells also produced higher amounts of kynurenine, decreased the proliferation of T-lymphocytes and expanded the number of T-regulatory (Treg) cells. Kynurenine synthesis was due to the transcription of the indoleamine 1,2 dioxygenase (IDO) enzyme, consequent to the activation of the signal transducer and activator of transcription-3 (STAT3). By reducing the activity of the Ras/ERK1/2/STAT3/IDO axis, zoledronic acid lowered the kyurenine synthesis and the expansion of Treg cells, and increased the proliferation of T-lymphocytes. Thanks to its ability to decrease Ras/ERK1/2 activity, which is responsible for both Pgp-mediated chemoresistance and IDO-mediated immunosuppression, zoledronic acid is an effective chemo-immunosensitizing agent in HMM cells. PMID:25544757

Bayesian structural inference for hidden processes.

PubMed

Strelioff, Christopher C; Crutchfield, James P

2014-04-01

We introduce a Bayesian approach to discovering patterns in structurally complex processes. The proposed method of Bayesian structural inference (BSI) relies on a set of candidate unifilar hidden Markov model (uHMM) topologies for inference of process structure from a data series. We employ a recently developed exact enumeration of topological ε-machines. (A sequel then removes the topological restriction.) This subset of the uHMM topologies has the added benefit that inferred models are guaranteed to be ε-machines, irrespective of estimated transition probabilities. Properties of ε-machines and uHMMs allow for the derivation of analytic expressions for estimating transition probabilities, inferring start states, and comparing the posterior probability of candidate model topologies, despite process internal structure being only indirectly present in data. We demonstrate BSI's effectiveness in estimating a process's randomness, as reflected by the Shannon entropy rate, and its structure, as quantified by the statistical complexity. We also compare using the posterior distribution over candidate models and the single, maximum a posteriori model for point estimation and show that the former more accurately reflects uncertainty in estimated values. We apply BSI to in-class examples of finite- and infinite-order Markov processes, as well to an out-of-class, infinite-state hidden process.

Bayesian structural inference for hidden processes

NASA Astrophysics Data System (ADS)

Strelioff, Christopher C.; Crutchfield, James P.

2014-04-01

We introduce a Bayesian approach to discovering patterns in structurally complex processes. The proposed method of Bayesian structural inference (BSI) relies on a set of candidate unifilar hidden Markov model (uHMM) topologies for inference of process structure from a data series. We employ a recently developed exact enumeration of topological ɛ-machines. (A sequel then removes the topological restriction.) This subset of the uHMM topologies has the added benefit that inferred models are guaranteed to be ɛ-machines, irrespective of estimated transition probabilities. Properties of ɛ-machines and uHMMs allow for the derivation of analytic expressions for estimating transition probabilities, inferring start states, and comparing the posterior probability of candidate model topologies, despite process internal structure being only indirectly present in data. We demonstrate BSI's effectiveness in estimating a process's randomness, as reflected by the Shannon entropy rate, and its structure, as quantified by the statistical complexity. We also compare using the posterior distribution over candidate models and the single, maximum a posteriori model for point estimation and show that the former more accurately reflects uncertainty in estimated values. We apply BSI to in-class examples of finite- and infinite-order Markov processes, as well to an out-of-class, infinite-state hidden process.

Challenges in analysis of high-molar mass dextrans: comparison of HPSEC, AsFlFFF and DOSY NMR spectroscopy.

PubMed

Maina, Ndegwa Henry; Pitkänen, Leena; Heikkinen, Sami; Tuomainen, Päivi; Virkki, Liisa; Tenkanen, Maija

2014-01-01

Dilute solutions of various dextran standards, a high-molar mass (HMM) commercial dextran from Leuconostoc spp., and HMM dextrans isolated from Weissella confusa and Leuconostoc citreum were analyzed with high-performance size-exclusion chromatography (HPSEC), asymmetric flow field-flow fractionation (AsFlFFF), and diffusion-ordered NMR spectroscopy (DOSY). HPSEC analyses were performed in aqueous and dimethyl sulfoxide (DMSO) solutions, while only aqueous solutions were utilized in AsFlFFF and DOSY. The study showed that all methods were applicable to dextran analysis, but differences between the aqueous and DMSO-based solutions were obtained for HMM samples. These differences were attributed to the presence of aggregates in aqueous solution that were less prevalent in DMSO. The study showed that DOSY provides an estimate of the size of HMM dextrans, though calibration standards may be required for each experimental set-up. To our knowledge, this is the first study utilizing these three methods in analyzing HMM dextrans. Copyright © 2013 Elsevier Ltd. All rights reserved.

Low-viscosity hydroxypropylcellulose (HPC) grades SL and SSL: versatile pharmaceutical polymers for dissolution enhancement, controlled release, and pharmaceutical processing.

PubMed

Sarode, Ashish; Wang, Peng; Cote, Catherine; Worthen, David R

2013-03-01

Hydroxypropylcellulose (HPC)-SL and -SSL, low-viscosity hydroxypropylcellulose polymers, are versatile pharmaceutical excipients. The utility of HPC polymers was assessed for both dissolution enhancement and sustained release of pharmaceutical drugs using various processing techniques. The BCS class II drugs carbamazepine (CBZ), hydrochlorthiazide, and phenytoin (PHT) were hot melt mixed (HMM) with various polymers. PHT formulations produced by solvent evaporation (SE) and ball milling (BM) were prepared using HPC-SSL. HMM formulations of BCS class I chlorpheniramine maleate (CPM) were prepared using HPC-SL and -SSL. These solid dispersions (SDs) manufactured using different processes were evaluated for amorphous transformation and dissolution characteristics. Drug degradation because of HMM processing was also assessed. Amorphous conversion using HMM could be achieved only for relatively low-melting CBZ and CPM. SE and BM did not produce amorphous SDs of PHT using HPC-SSL. Chemical stability of all the drugs was maintained using HPC during the HMM process. Dissolution enhancement was observed in HPC-based HMMs and compared well to other polymers. The dissolution enhancement of PHT was in the order of SE>BM>HMM>physical mixtures, as compared to the pure drug, perhaps due to more intimate mixing that occurred during SE and BM than in HMM. Dissolution of CPM could be significantly sustained in simulated gastric and intestinal fluids using HPC polymers. These studies revealed that low-viscosity HPC-SL and -SSL can be employed to produce chemically stable SDs of poorly as well as highly water-soluble drugs using various pharmaceutical processes in order to control drug dissolution.

Using hidden Markov models and observed evolution to annotate viral genomes.

PubMed

McCauley, Stephen; Hein, Jotun

2006-06-01

ssRNA (single stranded) viral genomes are generally constrained in length and utilize overlapping reading frames to maximally exploit the coding potential within the genome length restrictions. This overlapping coding phenomenon leads to complex evolutionary constraints operating on the genome. In regions which code for more than one protein, silent mutations in one reading frame generally have a protein coding effect in another. To maximize coding flexibility in all reading frames, overlapping regions are often compositionally biased towards amino acids which are 6-fold degenerate with respect to the 64 codon alphabet. Previous methodologies have used this fact in an ad hoc manner to look for overlapping genes by motif matching. In this paper differentiated nucleotide compositional patterns in overlapping regions are incorporated into a probabilistic hidden Markov model (HMM) framework which is used to annotate ssRNA viral genomes. This work focuses on single sequence annotation and applies an HMM framework to ssRNA viral annotation. A description of how the HMM is parameterized, whilst annotating within a missing data framework is given. A Phylogenetic HMM (Phylo-HMM) extension, as applied to 14 aligned HIV2 sequences is also presented. This evolutionary extension serves as an illustration of the potential of the Phylo-HMM framework for ssRNA viral genomic annotation. The single sequence annotation procedure (SSA) is applied to 14 different strains of the HIV2 virus. Further results on alternative ssRNA viral genomes are presented to illustrate more generally the performance of the method. The results of the SSA method are encouraging however there is still room for improvement, and since there is overwhelming evidence to indicate that comparative methods can improve coding sequence (CDS) annotation, the SSA method is extended to a Phylo-HMM to incorporate evolutionary information. The Phylo-HMM extension is applied to the same set of 14 HIV2 sequences which are pre-aligned. The performance improvement that results from including the evolutionary information in the analysis is illustrated.

Modeling strategic use of human computer interfaces with novel hidden Markov models

PubMed Central

Mariano, Laura J.; Poore, Joshua C.; Krum, David M.; Schwartz, Jana L.; Coskren, William D.; Jones, Eric M.

2015-01-01

Immersive software tools are virtual environments designed to give their users an augmented view of real-world data and ways of manipulating that data. As virtual environments, every action users make while interacting with these tools can be carefully logged, as can the state of the software and the information it presents to the user, giving these actions context. This data provides a high-resolution lens through which dynamic cognitive and behavioral processes can be viewed. In this report, we describe new methods for the analysis and interpretation of such data, utilizing a novel implementation of the Beta Process Hidden Markov Model (BP-HMM) for analysis of software activity logs. We further report the results of a preliminary study designed to establish the validity of our modeling approach. A group of 20 participants were asked to play a simple computer game, instrumented to log every interaction with the interface. Participants had no previous experience with the game's functionality or rules, so the activity logs collected during their naïve interactions capture patterns of exploratory behavior and skill acquisition as they attempted to learn the rules of the game. Pre- and post-task questionnaires probed for self-reported styles of problem solving, as well as task engagement, difficulty, and workload. We jointly modeled the activity log sequences collected from all participants using the BP-HMM approach, identifying a global library of activity patterns representative of the collective behavior of all the participants. Analyses show systematic relationships between both pre- and post-task questionnaires, self-reported approaches to analytic problem solving, and metrics extracted from the BP-HMM decomposition. Overall, we find that this novel approach to decomposing unstructured behavioral data within software environments provides a sensible means for understanding how users learn to integrate software functionality for strategic task pursuit. PMID:26191026

Popularity Modeling for Mobile Apps: A Sequential Approach.

PubMed

Zhu, Hengshu; Liu, Chuanren; Ge, Yong; Xiong, Hui; Chen, Enhong

2015-07-01

The popularity information in App stores, such as chart rankings, user ratings, and user reviews, provides an unprecedented opportunity to understand user experiences with mobile Apps, learn the process of adoption of mobile Apps, and thus enables better mobile App services. While the importance of popularity information is well recognized in the literature, the use of the popularity information for mobile App services is still fragmented and under-explored. To this end, in this paper, we propose a sequential approach based on hidden Markov model (HMM) for modeling the popularity information of mobile Apps toward mobile App services. Specifically, we first propose a popularity based HMM (PHMM) to model the sequences of the heterogeneous popularity observations of mobile Apps. Then, we introduce a bipartite based method to precluster the popularity observations. This can help to learn the parameters and initial values of the PHMM efficiently. Furthermore, we demonstrate that the PHMM is a general model and can be applicable for various mobile App services, such as trend based App recommendation, rating and review spam detection, and ranking fraud detection. Finally, we validate our approach on two real-world data sets collected from the Apple Appstore. Experimental results clearly validate both the effectiveness and efficiency of the proposed popularity modeling approach.

Strong convective storm nowcasting using a hybrid approach of convolutional neural network and hidden Markov model

NASA Astrophysics Data System (ADS)

Zhang, Wei; Jiang, Ling; Han, Lei

2018-04-01

Convective storm nowcasting refers to the prediction of the convective weather initiation, development, and decay in a very short term (typically 0 2 h) .Despite marked progress over the past years, severe convective storm nowcasting still remains a challenge. With the boom of machine learning, it has been well applied in various fields, especially convolutional neural network (CNN). In this paper, we build a servere convective weather nowcasting system based on CNN and hidden Markov model (HMM) using reanalysis meteorological data. The goal of convective storm nowcasting is to predict if there is a convective storm in 30min. In this paper, we compress the VDRAS reanalysis data to low-dimensional data by CNN as the observation vector of HMM, then obtain the development trend of strong convective weather in the form of time series. It shows that, our method can extract robust features without any artificial selection of features, and can capture the development trend of strong convective storm.

Linear Arrangement of Motor Protein on a Mechanically Deposited Fluoropolymer Thin Film

NASA Astrophysics Data System (ADS)

Suzuki, Hitoshi; Oiwa, Kazuhiro; Yamada, Akira; Sakakibara, Hitoshi; Nakayama, Haruto; Mashiko, Shinro

1995-07-01

Motor protein molecules such as heavy meromyosin (HMM), one of the major components of skeletal muscle, were arranged linearly on a mechanically deposited fluoropolymer thin film substrate in order to regulate the direction of movement generated by the motor protein. The fluoropolymer film consisted of many linear parallel ridges whose heights and widths were 10 to 20 nm and 10 to 100 nm, respectively. The fluoropolymer ridges adsorbed HMM molecules that were applied onto the film. Actin filaments labeled with rhodamine-phalloidin were observed under a fluorescence microscope moving linearly on the HMM-coated ridges. The observation indicates that HMM molecules were aligned on the fluoropolymer ridges while retaining their function. The velocity of actin movement was measured in this system.

Offline handwritten word recognition using MQDF-HMMs

NASA Astrophysics Data System (ADS)

Ramachandrula, Sitaram; Hambarde, Mangesh; Patial, Ajay; Sahoo, Dushyant; Kochar, Shaivi

2015-01-01

We propose an improved HMM formulation for offline handwriting recognition (HWR). The main contribution of this work is using modified quadratic discriminant function (MQDF) [1] within HMM framework. In an MQDF-HMM the state observation likelihood is calculated by a weighted combination of MQDF likelihoods of individual Gaussians of GMM (Gaussian Mixture Model). The quadratic discriminant function (QDF) of a multivariate Gaussian can be rewritten by avoiding the inverse of covariance matrix by using the Eigen values and Eigen vectors of it. The MQDF is derived from QDF by substituting few of badly estimated lower-most Eigen values by an appropriate constant. The estimation errors of non-dominant Eigen vectors and Eigen values of covariance matrix for which the training data is insufficient can be controlled by this approach. MQDF has been successfully shown to improve the character recognition performance [1]. The usage of MQDF in HMM improves the computation, storage and modeling power of HMM when there is limited training data. We have got encouraging results on offline handwritten character (NIST database) and word recognition in English using MQDF HMMs.

Theory, Characterization and Applications of Infrared Hyperbolic Metamaterials

NASA Astrophysics Data System (ADS)

Fullager, Daniel B.

Hyperbolic Metamaterials (HMMs) are engineered structures capable of supporting lightmatter interactions that are not normally observed in naturally occuring material systems. These unusual responses are enabled by an enhancement of the photonic density of states (PDOS) in the material. The PDOS enhancement is a result of deliberately introduced anisotropy via a permittivity sign-change in HMM structures which increases the number and frequency spread of possible wave vectors that propagate in the material. Subwavelength structural features allow effective medium theories to be invoked to construct the k-space isofrequency quadratic curves that, for HMMs, result in the k-space isofrequency contour transitioning from being a bounded surface to an unbounded one. Since the PDOS is the integral of the differential volume between k-space contours, unbounded manifolds lead to the implication of an infinite or otherwise drastically enhanced PDOS. Since stored heat can be thought of as a set of non-radiative electromagnetic modes, in this dissertation we demonstrate that HMMs provide an ideal platform to attempt to modify the thermal/IR emissivity of a material. We also show that HMMs provide a platform for broadband plasmonic sensing. The advent of commercial two photon polymerization tools has enabled the rapid production of nano- and microstructures which can be used as scaffolds for directive infrared scatterers. We describe how such directive components can be used to address thermal management needs in vacuum environments in order to maximize radiative thermal transfer. In this context, the fundamental limitations of enhanced spon- taneous emission due to conjugate impedance matched scatterers are also explored. The HMM/conjugate scatterer system's performance is strongly correlated with the dielectric function of the negative permittivity component of the HMM. In order to fully understand the significance of these engineered materials, we examine in detail the electromagnetic response of one ternary material system, aluminium-doped zinc oxide (AZO), whose tuneable plasma frequency makes it ideal for HMM and thermal transfer applications. This study draws upon first principle calculations from the open literature utilizing a Hubbard-U corrected model for the non-local interaction of charge carriers in AZO crystalline systems. We present the first complete dielectric function of industrially produced AZO samples from DC to 30,000 cm -1 and conclude with an assessment of this material's suitability fo the applications described.

Cluster-based adaptive power control protocol using Hidden Markov Model for Wireless Sensor Networks

NASA Astrophysics Data System (ADS)

Vinutha, C. B.; Nalini, N.; Nagaraja, M.

2017-06-01

This paper presents strategies for an efficient and dynamic transmission power control technique, in order to reduce packet drop and hence energy consumption of power-hungry sensor nodes operated in highly non-linear channel conditions of Wireless Sensor Networks. Besides, we also focus to prolong network lifetime and scalability by designing cluster-based network structure. Specifically we consider weight-based clustering approach wherein, minimum significant node is chosen as Cluster Head (CH) which is computed stemmed from the factors distance, remaining residual battery power and received signal strength (RSS). Further, transmission power control schemes to fit into dynamic channel conditions are meticulously implemented using Hidden Markov Model (HMM) where probability transition matrix is formulated based on the observed RSS measurements. Typically, CH estimates initial transmission power of its cluster members (CMs) from RSS using HMM and broadcast this value to its CMs for initialising their power value. Further, if CH finds that there are variations in link quality and RSS of the CMs, it again re-computes and optimises the transmission power level of the nodes using HMM to avoid packet loss due noise interference. We have demonstrated our simulation results to prove that our technique efficiently controls the power levels of sensing nodes to save significant quantity of energy for different sized network.

Hidden Markov model approach for identifying the modular framework of the protein backbone.

PubMed

Camproux, A C; Tuffery, P; Chevrolat, J P; Boisvieux, J F; Hazout, S

1999-12-01

The hidden Markov model (HMM) was used to identify recurrent short 3D structural building blocks (SBBs) describing protein backbones, independently of any a priori knowledge. Polypeptide chains are decomposed into a series of short segments defined by their inter-alpha-carbon distances. Basically, the model takes into account the sequentiality of the observed segments and assumes that each one corresponds to one of several possible SBBs. Fitting the model to a database of non-redundant proteins allowed us to decode proteins in terms of 12 distinct SBBs with different roles in protein structure. Some SBBs correspond to classical regular secondary structures. Others correspond to a significant subdivision of their bounding regions previously considered to be a single pattern. The major contribution of the HMM is that this model implicitly takes into account the sequential connections between SBBs and thus describes the most probable pathways by which the blocks are connected to form the framework of the protein structures. Validation of the SBBs code was performed by extracting SBB series repeated in recoding proteins and examining their structural similarities. Preliminary results on the sequence specificity of SBBs suggest promising perspectives for the prediction of SBBs or series of SBBs from the protein sequences.

MEGGASENSE - The Metagenome/Genome Annotated Sequence Natural Language Search Engine: A Platform for 
the Construction of Sequence Data Warehouses.

PubMed

Gacesa, Ranko; Zucko, Jurica; Petursdottir, Solveig K; Gudmundsdottir, Elisabet Eik; Fridjonsson, Olafur H; Diminic, Janko; Long, Paul F; Cullum, John; Hranueli, Daslav; Hreggvidsson, Gudmundur O; Starcevic, Antonio

2017-06-01

The MEGGASENSE platform constructs relational databases of DNA or protein sequences. The default functional analysis uses 14 106 hidden Markov model (HMM) profiles based on sequences in the KEGG database. The Solr search engine allows sophisticated queries and a BLAST search function is also incorporated. These standard capabilities were used to generate the SCATT database from the predicted proteome of Streptomyces cattleya . The implementation of a specialised metagenome database (AMYLOMICS) for bioprospecting of carbohydrate-modifying enzymes is described. In addition to standard assembly of reads, a novel 'functional' assembly was developed, in which screening of reads with the HMM profiles occurs before the assembly. The AMYLOMICS database incorporates additional HMM profiles for carbohydrate-modifying enzymes and it is illustrated how the combination of HMM and BLAST analyses helps identify interesting genes. A variety of different proteome and metagenome databases have been generated by MEGGASENSE.

Combination of dynamic Bayesian network classifiers for the recognition of degraded characters

NASA Astrophysics Data System (ADS)

Likforman-Sulem, Laurence; Sigelle, Marc

2009-01-01

We investigate in this paper the combination of DBN (Dynamic Bayesian Network) classifiers, either independent or coupled, for the recognition of degraded characters. The independent classifiers are a vertical HMM and a horizontal HMM whose observable outputs are the image columns and the image rows respectively. The coupled classifiers, presented in a previous study, associate the vertical and horizontal observation streams into single DBNs. The scores of the independent and coupled classifiers are then combined linearly at the decision level. We compare the different classifiers -independent, coupled or linearly combined- on two tasks: the recognition of artificially degraded handwritten digits and the recognition of real degraded old printed characters. Our results show that coupled DBNs perform better on degraded characters than the linear combination of independent HMM scores. Our results also show that the best classifier is obtained by linearly combining the scores of the best coupled DBN and the best independent HMM.

End-to-End ASR-Free Keyword Search From Speech

NASA Astrophysics Data System (ADS)

Audhkhasi, Kartik; Rosenberg, Andrew; Sethy, Abhinav; Ramabhadran, Bhuvana; Kingsbury, Brian

2017-12-01

End-to-end (E2E) systems have achieved competitive results compared to conventional hybrid hidden Markov model (HMM)-deep neural network based automatic speech recognition (ASR) systems. Such E2E systems are attractive due to the lack of dependence on alignments between input acoustic and output grapheme or HMM state sequence during training. This paper explores the design of an ASR-free end-to-end system for text query-based keyword search (KWS) from speech trained with minimal supervision. Our E2E KWS system consists of three sub-systems. The first sub-system is a recurrent neural network (RNN)-based acoustic auto-encoder trained to reconstruct the audio through a finite-dimensional representation. The second sub-system is a character-level RNN language model using embeddings learned from a convolutional neural network. Since the acoustic and text query embeddings occupy different representation spaces, they are input to a third feed-forward neural network that predicts whether the query occurs in the acoustic utterance or not. This E2E ASR-free KWS system performs respectably despite lacking a conventional ASR system and trains much faster.

Dissecting protein loops with a statistical scalpel suggests a functional implication of some structural motifs.

PubMed

Regad, Leslie; Martin, Juliette; Camproux, Anne-Claude

2011-06-20

One of the strategies for protein function annotation is to search particular structural motifs that are known to be shared by proteins with a given function. Here, we present a systematic extraction of structural motifs of seven residues from protein loops and we explore their correspondence with functional sites. Our approach is based on the structural alphabet HMM-SA (Hidden Markov Model - Structural Alphabet), which allows simplification of protein structures into uni-dimensional sequences, and advanced pattern statistics adapted to short sequences. Structural motifs of interest are selected by looking for structural motifs significantly over-represented in SCOP superfamilies in protein loops. We discovered two types of structural motifs significantly over-represented in SCOP superfamilies: (i) ubiquitous motifs, shared by several superfamilies and (ii) superfamily-specific motifs, over-represented in few superfamilies. A comparison of ubiquitous words with known small structural motifs shows that they contain well-described motifs as turn, niche or nest motifs. A comparison between superfamily-specific motifs and biological annotations of Swiss-Prot reveals that some of them actually correspond to functional sites involved in the binding sites of small ligands, such as ATP/GTP, NAD(P) and SAH/SAM. Our findings show that statistical over-representation in SCOP superfamilies is linked to functional features. The detection of over-represented motifs within structures simplified by HMM-SA is therefore a promising approach for prediction of functional sites and annotation of uncharacterized proteins.

Dissecting protein loops with a statistical scalpel suggests a functional implication of some structural motifs

PubMed Central

2011-01-01

Background One of the strategies for protein function annotation is to search particular structural motifs that are known to be shared by proteins with a given function. Results Here, we present a systematic extraction of structural motifs of seven residues from protein loops and we explore their correspondence with functional sites. Our approach is based on the structural alphabet HMM-SA (Hidden Markov Model - Structural Alphabet), which allows simplification of protein structures into uni-dimensional sequences, and advanced pattern statistics adapted to short sequences. Structural motifs of interest are selected by looking for structural motifs significantly over-represented in SCOP superfamilies in protein loops. We discovered two types of structural motifs significantly over-represented in SCOP superfamilies: (i) ubiquitous motifs, shared by several superfamilies and (ii) superfamily-specific motifs, over-represented in few superfamilies. A comparison of ubiquitous words with known small structural motifs shows that they contain well-described motifs as turn, niche or nest motifs. A comparison between superfamily-specific motifs and biological annotations of Swiss-Prot reveals that some of them actually correspond to functional sites involved in the binding sites of small ligands, such as ATP/GTP, NAD(P) and SAH/SAM. Conclusions Our findings show that statistical over-representation in SCOP superfamilies is linked to functional features. The detection of over-represented motifs within structures simplified by HMM-SA is therefore a promising approach for prediction of functional sites and annotation of uncharacterized proteins. PMID:21689388

Antidepressant-like synergism of extracts from magnolia bark and ginger rhizome alone and in combination in mice.

PubMed

Yi, Li-Tao; Xu, Qun; Li, Yu-Cheng; Yang, Lei; Kong, Ling-Dong

2009-06-15

Magnolia bark and ginger rhizome is a drug pair in many prescriptions for treatment of mental disorders in traditional Chinese medicine (TCM). However, compatibility and synergism mechanism of two herbs on antidepressant actions have not been reported. The aim of this study was to approach the rationale of the drug pair in TCM. We evaluated antidepressant-like effects of mixture of honokiol and magnolol (HMM), polysaccharides (PMB) from magnolia bark, essential oil (OGR) and polysaccharides (PGR) from ginger rhizome alone, and the possibility of synergistic interactions in their combinations in the mouse forced swimming test (FST) and tail suspension test (TST). Serotonin (5-HT) and noradrenaline (NE) levels in prefrontal cortex, hippocampus and striatum were also examined. 30 mg/kg HMM decreased immobility in the FST and TST in mice after one- and two-week treatment. OGR (19.5 or 39 mg/kg) alone was ineffective. The combination of an ineffective dose of 39 mg/kg OGR with 15 mg/kg HMM was the most effective and produced a synergistic action on behaviors after two-week treatment. Significant increase in 5-HT and synergistic increase in NE in prefrontal cortex were observed after co-administration of HMM with OGR. These results demonstrated that HMM was the principal component of this drug pair, whereas OGR served as adjuvant fraction. Compatibility of HMM with OGR was suggested to exert synergistic antidepressant actions by attenuating abnormalities in serotonergic and noradrenergic system functions. Therefore, we confirmed the rationality of drug pair in clinical application and provided a novel perspective in drug pair of TCM researches.

Selective radiative heating of nanostructures using hyperbolic metamaterials

DOE PAGES

Ding, Ding; Minnich, Austin J

2015-01-01

Hyperbolic metamaterials (HMM) are of great interest due to their ability to break the diffraction limit for imaging and enhance near-field radiative heat transfer. Here we demonstrate that an annular, transparent HMM enables selective heating of a sub-wavelength plasmonic nanowire by controlling the angular mode number of a plasmonic resonance. A nanowire emitter, surrounded by an HMM, appears dark to incoming radiation from an adjacent nanowire emitter unless the second emitter is surrounded by an identical lens such that the wavelength and angular mode of the plasmonic resonance match. Our result can find applications in radiative thermal management.

Accelerated Profile HMM Searches

PubMed Central

Eddy, Sean R.

2011-01-01

Profile hidden Markov models (profile HMMs) and probabilistic inference methods have made important contributions to the theory of sequence database homology search. However, practical use of profile HMM methods has been hindered by the computational expense of existing software implementations. Here I describe an acceleration heuristic for profile HMMs, the “multiple segment Viterbi” (MSV) algorithm. The MSV algorithm computes an optimal sum of multiple ungapped local alignment segments using a striped vector-parallel approach previously described for fast Smith/Waterman alignment. MSV scores follow the same statistical distribution as gapped optimal local alignment scores, allowing rapid evaluation of significance of an MSV score and thus facilitating its use as a heuristic filter. I also describe a 20-fold acceleration of the standard profile HMM Forward/Backward algorithms using a method I call “sparse rescaling”. These methods are assembled in a pipeline in which high-scoring MSV hits are passed on for reanalysis with the full HMM Forward/Backward algorithm. This accelerated pipeline is implemented in the freely available HMMER3 software package. Performance benchmarks show that the use of the heuristic MSV filter sacrifices negligible sensitivity compared to unaccelerated profile HMM searches. HMMER3 is substantially more sensitive and 100- to 1000-fold faster than HMMER2. HMMER3 is now about as fast as BLAST for protein searches. PMID:22039361

Objective laparoscopic skills assessments of surgical residents using Hidden Markov Models based on haptic information and tool/tissue interactions.

PubMed

Rosen, J; Solazzo, M; Hannaford, B; Sinanan, M

2001-01-01

Laparoscopic surgical skills evaluation of surgery residents is usually a subjective process, carried out in the operating room by senior surgeons. By its nature, this process is performed using fuzzy criteria. The objective of the current study was to develop and assess an objective laparoscopic surgical skill scale using Hidden Markov Models (HMM) based on haptic information, tool/tissue interactions and visual task decomposition. Eight subjects (six surgical trainees: first year surgical residents 2 x R1, third year surgical residents 2 x R3 fifth year surgical residents 2 x R5; and two expert laparoscopic surgeons: 2 x ES) performed laparoscopic cholecystectomy following a specific 7 steps protocol on a pig. An instrumented laparoscopic grasper equipped with a three-axis force/torque sensor located at the proximal end with an additional force sensor located on the handle, was used to measure the forces and torques. The hand/tool interface force/torque data was synchronized with a video of the tool operative maneuvers. A synthesis of frame-by-frame video analysis was used to define 14 different types of tool/tissue interactions, each one associated with unique force/torque (F/T) signatures. HMMs were developed for each subject representing the surgical skills by defining the various tool/tissue interactions as states and the associated F/T signatures as observations. The statistical distance between the HMMs representing residents at different levels of their training and the HMMs of expert surgeons were calculated in order to generate a learning curve of selected steps during laparoscopic cholecystectomy. Comparison of HMM's between groups showed significant differences between all skill levels, supporting the objective definition of a learning curve. The major differences between skill levels were: (i) magnitudes of F/T applied (ii) types of tool/tissue interactions used and the transition between them and (iii) time intervals spent in each tool/tissue interaction and the overall completion time. The objective HMM analysis showed that the greatest difference in performance was between R1 and R3 groups and then decreased as the level of expertise increased, suggesting that significant laparoscopic surgical capability develops between the first and the third years of their residency training. The power of the methodology using HMM for objective surgical skill assessment arises from the fact that it compiles enormous amount of data regarding different aspects of surgical skill into a very compact model that can be translated into a single number representing the distance from expert performance. Moreover, the methodology is not limited to in-vivo condition as demonstrated in the current study. It can be extended to other modalities such as measuring performance in surgical simulators and robotic systems.

Improving prokaryotic transposable elements identification using a combination of de novo and profile HMM methods.

PubMed

Kamoun, Choumouss; Payen, Thibaut; Hua-Van, Aurélie; Filée, Jonathan

2013-10-11

Insertion Sequences (ISs) and their non-autonomous derivatives (MITEs) are important components of prokaryotic genomes inducing duplication, deletion, rearrangement or lateral gene transfers. Although ISs and MITEs are relatively simple and basic genetic elements, their detection remains a difficult task due to their remarkable sequence diversity. With the advent of high-throughput genome and metagenome sequencing technologies, the development of fast, reliable and sensitive methods of ISs and MITEs detection become an important challenge. So far, almost all studies dealing with prokaryotic transposons have used classical BLAST-based detection methods against reference libraries. Here we introduce alternative methods of detection either taking advantages of the structural properties of the elements (de novo methods) or using an additional library-based method using profile HMM searches. In this study, we have developed three different work flows dedicated to ISs and MITEs detection: the first two use de novo methods detecting either repeated sequences or presence of Inverted Repeats; the third one use 28 in-house transposase alignment profiles with HMM search methods. We have compared the respective performances of each method using a reference dataset of 30 archaeal and 30 bacterial genomes in addition to simulated and real metagenomes. Compared to a BLAST-based method using ISFinder as library, de novo methods significantly improve ISs and MITEs detection. For example, in the 30 archaeal genomes, we discovered 30 new elements (+20%) in addition to the 141 multi-copies elements already detected by the BLAST approach. Many of the new elements correspond to ISs belonging to unknown or highly divergent families. The total number of MITEs has even doubled with the discovery of elements displaying very limited sequence similarities with their respective autonomous partners (mainly in the Inverted Repeats of the elements). Concerning metagenomes, with the exception of short reads data (<300 bp) for which both techniques seem equally limited, profile HMM searches considerably ameliorate the detection of transposase encoding genes (up to +50%) generating low level of false positives compare to BLAST-based methods. Compared to classical BLAST-based methods, the sensitivity of de novo and profile HMM methods developed in this study allow a better and more reliable detection of transposons in prokaryotic genomes and metagenomes. We believed that future studies implying ISs and MITEs identification in genomic data should combine at least one de novo and one library-based method, with optimal results obtained by running the two de novo methods in addition to a library-based search. For metagenomic data, profile HMM search should be favored, a BLAST-based step is only useful to the final annotation into groups and families.

Investigation of an HMM/ANN hybrid structure in pattern recognition application using cepstral analysis of dysarthric (distorted) speech signals.

PubMed

Polur, Prasad D; Miller, Gerald E

2006-10-01

Computer speech recognition of individuals with dysarthria, such as cerebral palsy patients requires a robust technique that can handle conditions of very high variability and limited training data. In this study, application of a 10 state ergodic hidden Markov model (HMM)/artificial neural network (ANN) hybrid structure for a dysarthric speech (isolated word) recognition system, intended to act as an assistive tool, was investigated. A small size vocabulary spoken by three cerebral palsy subjects was chosen. The effect of such a structure on the recognition rate of the system was investigated by comparing it with an ergodic hidden Markov model as a control tool. This was done in order to determine if this modified technique contributed to enhanced recognition of dysarthric speech. The speech was sampled at 11 kHz. Mel frequency cepstral coefficients were extracted from them using 15 ms frames and served as training input to the hybrid model setup. The subsequent results demonstrated that the hybrid model structure was quite robust in its ability to handle the large variability and non-conformity of dysarthric speech. The level of variability in input dysarthric speech patterns sometimes limits the reliability of the system. However, its application as a rehabilitation/control tool to assist dysarthric motor impaired individuals holds sufficient promise.

Insights into the evolution of enzyme substrate promiscuity after the discovery of (βα)₈ isomerase evolutionary intermediates from a diverse metagenome.

PubMed

Noda-García, Lianet; Juárez-Vázquez, Ana L; Ávila-Arcos, María C; Verduzco-Castro, Ernesto A; Montero-Morán, Gabriela; Gaytán, Paul; Carrillo-Tripp, Mauricio; Barona-Gómez, Francisco

2015-06-10

Current sequence-based approaches to identify enzyme functional shifts, such as enzyme promiscuity, have proven to be highly dependent on a priori functional knowledge, hampering our ability to reconstruct evolutionary history behind these mechanisms. Hidden Markov Model (HMM) profiles, broadly used to classify enzyme families, can be useful to distinguish between closely related enzyme families with different specificities. The (βα)8-isomerase HisA/PriA enzyme family, involved in L-histidine (HisA, mono-substrate) biosynthesis in most bacteria and plants, but also in L-tryptophan (HisA/TrpF or PriA, dual-substrate) biosynthesis in most Actinobacteria, has been used as model system to explore evolutionary hypotheses and therefore has a considerable amount of evolutionary, functional and structural knowledge available. We searched for functional evolutionary intermediates between the HisA and PriA enzyme families in order to understand the functional divergence between these families. We constructed a HMM profile that correctly classifies sequences of unknown function into the HisA and PriA enzyme sub-families. Using this HMM profile, we mined a large metagenome to identify plausible evolutionary intermediate sequences between HisA and PriA. These sequences were used to perform phylogenetic reconstructions and to identify functionally conserved amino acids. Biochemical characterization of one selected enzyme (CAM1) with a mutation within the functionally essential N-terminus phosphate-binding site, namely, an alanine instead of a glycine in HisA or a serine in PriA, showed that this evolutionary intermediate has dual-substrate specificity. Moreover, site-directed mutagenesis of this alanine residue, either backwards into a glycine or forward into a serine, revealed the robustness of this enzyme. None of these mutations, presumably upon functionally essential amino acids, significantly abolished its enzyme activities. A truncated version of this enzyme (CAM2) predicted to adopt a (βα)6-fold, and thus entirely lacking a C-terminus phosphate-binding site, was identified and shown to have HisA activity. As expected, reconstruction of the evolution of PriA from HisA with HMM profiles suggest that functional shifts involve mutations in evolutionarily intermediate enzymes of otherwise functionally essential residues or motifs. These results are in agreement with a link between promiscuous enzymes and intragenic epistasis. HMM provides a convenient approach for gaining insights into these evolutionary processes.

Dietary flavonoid fisetin increases abundance of high-molecular-mass hyaluronan conferring resistance to prostate oncogenesis.

PubMed

Lall, Rahul K; Syed, Deeba N; Khan, Mohammad Imran; Adhami, Vaqar M; Gong, Yuansheng; Lucey, John A; Mukhtar, Hasan

2016-09-01

We and others have shown previously that fisetin, a plant flavonoid, has therapeutic potential against many cancer types. Here, we examined the probable mechanism of its action in prostate cancer (PCa) using a global metabolomics approach. HPLC-ESI-MS analysis of tumor xenografts from fisetin-treated animals identified several metabolic targets with hyaluronan (HA) as the most affected. Efficacy of fisetin on HA was then evaluated in vitro and also in vivo in the transgenic TRAMP mouse model of PCa. Size exclusion chromatography-multiangle laser light scattering (SEC-MALS) was performed to analyze the molar mass (Mw) distribution of HA. Fisetin treatment downregulated intracellular and secreted HA levels both in vitro and in vivo Fisetin inhibited HA synthesis and degradation enzymes, which led to cessation of HA synthesis and also repressed the degradation of the available high-molecular-mass (HMM)-HA. SEC-MALS analysis of intact HA fragment size revealed that cells and animals have more abundance of HMM-HA and less of low-molecular-mass (LMM)-HA upon fisetin treatment. Elevated HA levels have been shown to be associated with disease progression in certain cancer types. Biological responses triggered by HA mainly depend on the HA polymer length where HMM-HA represses mitogenic signaling and has anti-inflammatory properties whereas LMM-HA promotes proliferation and inflammation. Similarly, Mw analysis of secreted HA fragment size revealed less HMM-HA is secreted that allowed more HMM-HA to be retained within the cells and tissues. Our findings establish that fisetin is an effective, non-toxic, potent HA synthesis inhibitor, which increases abundance of antiangiogenic HMM-HA and could be used for the management of PCa. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Identification and agreement of first turn point by mathematical analysis applied to heart rate, carbon dioxide output and electromyography

PubMed Central

Zamunér, Antonio R.; Catai, Aparecida M.; Martins, Luiz E. B.; Sakabe, Daniel I.; Silva, Ester Da

2013-01-01

Background The second heart rate (HR) turn point has been extensively studied, however there are few studies determining the first HR turn point. Also, the use of mathematical and statistical models for determining changes in dynamic characteristics of physiological variables during an incremental cardiopulmonary test has been suggested. Objectives To determine the first turn point by analysis of HR, surface electromyography (sEMG), and carbon dioxide output () using two mathematical models and to compare the results to those of the visual method. Method Ten sedentary middle-aged men (53.9±3.2 years old) were submitted to cardiopulmonary exercise testing on an electromagnetic cycle ergometer until exhaustion. Ventilatory variables, HR, and sEMG of the vastus lateralis were obtained in real time. Three methods were used to determine the first turn point: 1) visual analysis based on loss of parallelism between and oxygen uptake (); 2) the linear-linear model, based on fitting the curves to the set of data (Lin-Lin ); 3) a bi-segmental linear regression of Hinkley' s algorithm applied to HR (HMM-HR), (HMM- ), and sEMG data (HMM-RMS). Results There were no differences between workload, HR, and ventilatory variable values at the first ventilatory turn point as determined by the five studied parameters (p>0.05). The Bland-Altman plot showed an even distribution of the visual analysis method with Lin-Lin , HMM-HR, HMM-CO2, and HMM-RMS. Conclusion The proposed mathematical models were effective in determining the first turn point since they detected the linear pattern change and the deflection point of , HR responses, and sEMG. PMID:24346296

Identification and agreement of first turn point by mathematical analysis applied to heart rate, carbon dioxide output and electromyography.

PubMed

Zamunér, Antonio R; Catai, Aparecida M; Martins, Luiz E B; Sakabe, Daniel I; Da Silva, Ester

2013-01-01

The second heart rate (HR) turn point has been extensively studied, however there are few studies determining the first HR turn point. Also, the use of mathematical and statistical models for determining changes in dynamic characteristics of physiological variables during an incremental cardiopulmonary test has been suggested. To determine the first turn point by analysis of HR, surface electromyography (sEMG), and carbon dioxide output (VCO2) using two mathematical models and to compare the results to those of the visual method. Ten sedentary middle-aged men (53.9 ± 3.2 years old) were submitted to cardiopulmonary exercise testing on an electromagnetic cycle ergometer until exhaustion. Ventilatory variables, HR, and sEMG of the vastus lateralis were obtained in real time. Three methods were used to determine the first turn point: 1) visual analysis based on loss of parallelism between VCO2 and oxygen uptake (VO2); 2) the linear-linear model, based on fitting the curves to the set of VCO2 data (Lin-LinVCO2); 3) a bi-segmental linear regression of Hinkley's algorithm applied to HR (HMM-HR), VCO2 (HMM-VCO2), and sEMG data (HMM-RMS). There were no differences between workload, HR, and ventilatory variable values at the first ventilatory turn point as determined by the five studied parameters (p>0.05). The Bland-Altman plot showed an even distribution of the visual analysis method with Lin-LinVCO2, HMM-HR, HMM-VCO2, and HMM-RMS. The proposed mathematical models were effective in determining the first turn point since they detected the linear pattern change and the deflection point of VCO2, HR responses, and sEMG.

Efficient view based 3-D object retrieval using Hidden Markov Model

NASA Astrophysics Data System (ADS)

Jain, Yogendra Kumar; Singh, Roshan Kumar

2013-12-01

Recent research effort has been dedicated to view based 3-D object retrieval, because of highly discriminative property of 3-D object and has multi view representation. The state-of-art method is highly depending on their own camera array setting for capturing views of 3-D object and use complex Zernike descriptor, HAC for representative view selection which limit their practical application and make it inefficient for retrieval. Therefore, an efficient and effective algorithm is required for 3-D Object Retrieval. In order to move toward a general framework for efficient 3-D object retrieval which is independent of camera array setting and avoidance of representative view selection, we propose an Efficient View Based 3-D Object Retrieval (EVBOR) method using Hidden Markov Model (HMM). In this framework, each object is represented by independent set of view, which means views are captured from any direction without any camera array restriction. In this, views are clustered (including query view) to generate the view cluster, which is then used to build the query model with HMM. In our proposed method, HMM is used in twofold: in the training (i.e. HMM estimate) and in the retrieval (i.e. HMM decode). The query model is trained by using these view clusters. The EVBOR query model is worked on the basis of query model combining with HMM. The proposed approach remove statically camera array setting for view capturing and can be apply for any 3-D object database to retrieve 3-D object efficiently and effectively. Experimental results demonstrate that the proposed scheme has shown better performance than existing methods. [Figure not available: see fulltext.

Assignment of protein sequences to existing domain and family classification systems: Pfam and the PDB

PubMed Central

Dunbrack, Roland L.

2012-01-01

Motivation: Automating the assignment of existing domain and protein family classifications to new sets of sequences is an important task. Current methods often miss assignments because remote relationships fail to achieve statistical significance. Some assignments are not as long as the actual domain definitions because local alignment methods often cut alignments short. Long insertions in query sequences often erroneously result in two copies of the domain assigned to the query. Divergent repeat sequences in proteins are often missed. Results: We have developed a multilevel procedure to produce nearly complete assignments of protein families of an existing classification system to a large set of sequences. We apply this to the task of assigning Pfam domains to sequences and structures in the Protein Data Bank (PDB). We found that HHsearch alignments frequently scored more remotely related Pfams in Pfam clans higher than closely related Pfams, thus, leading to erroneous assignment at the Pfam family level. A greedy algorithm allowing for partial overlaps was, thus, applied first to sequence/HMM alignments, then HMM–HMM alignments and then structure alignments, taking care to join partial alignments split by large insertions into single-domain assignments. Additional assignment of repeat Pfams with weaker E-values was allowed after stronger assignments of the repeat HMM. Our database of assignments, presented in a database called PDBfam, contains Pfams for 99.4% of chains >50 residues. Availability: The Pfam assignment data in PDBfam are available at http://dunbrack2.fccc.edu/ProtCid/PDBfam, which can be searched by PDB codes and Pfam identifiers. They will be updated regularly. Contact: Roland.Dunbracks@fccc.edu PMID:22942020

Hidden Markov models incorporating fuzzy measures and integrals for protein sequence identification and alignment.

PubMed

Bidargaddi, Niranjan P; Chetty, Madhu; Kamruzzaman, Joarder

2008-06-01

Profile hidden Markov models (HMMs) based on classical HMMs have been widely applied for protein sequence identification. The formulation of the forward and backward variables in profile HMMs is made under statistical independence assumption of the probability theory. We propose a fuzzy profile HMM to overcome the limitations of that assumption and to achieve an improved alignment for protein sequences belonging to a given family. The proposed model fuzzifies the forward and backward variables by incorporating Sugeno fuzzy measures and Choquet integrals, thus further extends the generalized HMM. Based on the fuzzified forward and backward variables, we propose a fuzzy Baum-Welch parameter estimation algorithm for profiles. The strong correlations and the sequence preference involved in the protein structures make this fuzzy architecture based model as a suitable candidate for building profiles of a given family, since the fuzzy set can handle uncertainties better than classical methods.

Multi-category micro-milling tool wear monitoring with continuous hidden Markov models

NASA Astrophysics Data System (ADS)

Zhu, Kunpeng; Wong, Yoke San; Hong, Geok Soon

2009-02-01

In-process monitoring of tool conditions is important in micro-machining due to the high precision requirement and high tool wear rate. Tool condition monitoring in micro-machining poses new challenges compared to conventional machining. In this paper, a multi-category classification approach is proposed for tool flank wear state identification in micro-milling. Continuous Hidden Markov models (HMMs) are adapted for modeling of the tool wear process in micro-milling, and estimation of the tool wear state given the cutting force features. For a noise-robust approach, the HMM outputs are connected via a medium filter to minimize the tool state before entry into the next state due to high noise level. A detailed study on the selection of HMM structures for tool condition monitoring (TCM) is presented. Case studies on the tool state estimation in the micro-milling of pure copper and steel demonstrate the effectiveness and potential of these methods.

HMM-ModE: implementation, benchmarking and validation with HMMER3

PubMed Central

2014-01-01

Background HMM-ModE is a computational method that generates family specific profile HMMs using negative training sequences. The method optimizes the discrimination threshold using 10 fold cross validation and modifies the emission probabilities of profiles to reduce common fold based signals shared with other sub-families. The protocol depends on the program HMMER for HMM profile building and sequence database searching. The recent release of HMMER3 has improved database search speed by several orders of magnitude, allowing for the large scale deployment of the method in sequence annotation projects. We have rewritten our existing scripts both at the level of parsing the HMM profiles and modifying emission probabilities to upgrade HMM-ModE using HMMER3 that takes advantage of its probabilistic inference with high computational speed. The method is benchmarked and tested on GPCR dataset as an accurate and fast method for functional annotation. Results The implementation of this method, which now works with HMMER3, is benchmarked with the earlier version of HMMER, to show that the effect of local-local alignments is marked only in the case of profiles containing a large number of discontinuous match states. The method is tested on a gold standard set of families and we have reported a significant reduction in the number of false positive hits over the default HMM profiles. When implemented on GPCR sequences, the results showed an improvement in the accuracy of classification compared with other methods used to classify the familyat different levels of their classification hierarchy. Conclusions The present findings show that the new version of HMM-ModE is a highly specific method used to differentiate between fold (superfamily) and function (family) specific signals, which helps in the functional annotation of protein sequences. The use of modified profile HMMs of GPCR sequences provides a simple yet highly specific method for classification of the family, being able to predict the sub-family specific sequences with high accuracy even though sequences share common physicochemical characteristics between sub-families. PMID:25073805

Wavelet-based Gaussian-mixture hidden Markov model for the detection of multistage seizure dynamics: A proof-of-concept study

PubMed Central

2011-01-01

Background Epilepsy is a common neurological disorder characterized by recurrent electrophysiological activities, known as seizures. Without the appropriate detection strategies, these seizure episodes can dramatically affect the quality of life for those afflicted. The rationale of this study is to develop an unsupervised algorithm for the detection of seizure states so that it may be implemented along with potential intervention strategies. Methods Hidden Markov model (HMM) was developed to interpret the state transitions of the in vitro rat hippocampal slice local field potentials (LFPs) during seizure episodes. It can be used to estimate the probability of state transitions and the corresponding characteristics of each state. Wavelet features were clustered and used to differentiate the electrophysiological characteristics at each corresponding HMM states. Using unsupervised training method, the HMM and the clustering parameters were obtained simultaneously. The HMM states were then assigned to the electrophysiological data using expert guided technique. Minimum redundancy maximum relevance (mRMR) analysis and Akaike Information Criterion (AICc) were applied to reduce the effect of over-fitting. The sensitivity, specificity and optimality index of chronic seizure detection were compared for various HMM topologies. The ability of distinguishing early and late tonic firing patterns prior to chronic seizures were also evaluated. Results Significant improvement in state detection performance was achieved when additional wavelet coefficient rates of change information were used as features. The final HMM topology obtained using mRMR and AICc was able to detect non-ictal (interictal), early and late tonic firing, chronic seizures and postictal activities. A mean sensitivity of 95.7%, mean specificity of 98.9% and optimality index of 0.995 in the detection of chronic seizures was achieved. The detection of early and late tonic firing was validated with experimental intracellular electrical recordings of seizures. Conclusions The HMM implementation of a seizure dynamics detector is an improvement over existing approaches using visual detection and complexity measures. The subjectivity involved in partitioning the observed data prior to training can be eliminated. It can also decipher the probabilities of seizure state transitions using the magnitude and rate of change wavelet information of the LFPs. PMID:21504608

A robust omnifont open-vocabulary Arabic OCR system using pseudo-2D-HMM

NASA Astrophysics Data System (ADS)

Rashwan, Abdullah M.; Rashwan, Mohsen A.; Abdel-Hameed, Ahmed; Abdou, Sherif; Khalil, A. H.

2012-01-01

Recognizing old documents is highly desirable since the demand for quickly searching millions of archived documents has recently increased. Using Hidden Markov Models (HMMs) has been proven to be a good solution to tackle the main problems of recognizing typewritten Arabic characters. These attempts however achieved a remarkable success for omnifont OCR under very favorable conditions, they didn't achieve the same performance in practical conditions, i.e. noisy documents. In this paper we present an omnifont, large-vocabulary Arabic OCR system using Pseudo Two Dimensional Hidden Markov Model (P2DHMM), which is a generalization of the HMM. P2DHMM offers a more efficient way to model the Arabic characters, such model offer both minimal dependency on the font size/style (omnifont), and high level of robustness against noise. The evaluation results of this system are very promising compared to a baseline HMM system and best OCRs available in the market (Sakhr and NovoDynamics). The recognition accuracy of the P2DHMM classifier is measured against the classic HMM classifier, the average word accuracy rates for P2DHMM and HMM classifiers are 79% and 66% respectively. The overall system accuracy is measured against Sakhr and NovoDynamics OCR systems, the average word accuracy rates for P2DHMM, NovoDynamics, and Sakhr are 74%, 71%, and 61% respectively.

Efficacy of hidden markov model over support vector machine on multiclass classification of healthy and cancerous cervical tissues

NASA Astrophysics Data System (ADS)

Mukhopadhyay, Sabyasachi; Kurmi, Indrajit; Pratiher, Sawon; Mukherjee, Sukanya; Barman, Ritwik; Ghosh, Nirmalya; Panigrahi, Prasanta K.

2018-02-01

In this paper, a comparative study between SVM and HMM has been carried out for multiclass classification of cervical healthy and cancerous tissues. In our study, the HMM methodology is more promising to produce higher accuracy in classification.

Computational intelligence techniques for biological data mining: An overview

NASA Astrophysics Data System (ADS)

Faye, Ibrahima; Iqbal, Muhammad Javed; Said, Abas Md; Samir, Brahim Belhaouari

2014-10-01

Computational techniques have been successfully utilized for a highly accurate analysis and modeling of multifaceted and raw biological data gathered from various genome sequencing projects. These techniques are proving much more effective to overcome the limitations of the traditional in-vitro experiments on the constantly increasing sequence data. However, most critical problems that caught the attention of the researchers may include, but not limited to these: accurate structure and function prediction of unknown proteins, protein subcellular localization prediction, finding protein-protein interactions, protein fold recognition, analysis of microarray gene expression data, etc. To solve these problems, various classification and clustering techniques using machine learning have been extensively used in the published literature. These techniques include neural network algorithms, genetic algorithms, fuzzy ARTMAP, K-Means, K-NN, SVM, Rough set classifiers, decision tree and HMM based algorithms. Major difficulties in applying the above algorithms include the limitations found in the previous feature encoding and selection methods while extracting the best features, increasing classification accuracy and decreasing the running time overheads of the learning algorithms. The application of this research would be potentially useful in the drug design and in the diagnosis of some diseases. This paper presents a concise overview of the well-known protein classification techniques.

Capture, Learning, and Classification of Upper Extremity Movement Primitives in Healthy Controls and Stroke Patients

PubMed Central

Guerra, Jorge; Uddin, Jasim; Nilsen, Dawn; Mclnerney, James; Fadoo, Ammarah; Omofuma, Isirame B.; Hughes, Shatif; Agrawal, Sunil; Allen, Peter; Schambra, Heidi M.

2017-01-01

There currently exist no practical tools to identify functional movements in the upper extremities (UEs). This absence has limited the precise therapeutic dosing of patients recovering from stroke. In this proof-of-principle study, we aimed to develop an accurate approach for classifying UE functional movement primitives, which comprise functional movements. Data were generated from inertial measurement units (IMUs) placed on upper body segments of older healthy individuals and chronic stroke patients. Subjects performed activities commonly trained during rehabilitation after stroke. Data processing involved the use of a sliding window to obtain statistical descriptors, and resulting features were processed by a Hidden Markov Model (HMM). The likelihoods of the states, resulting from the HMM, were segmented by a second sliding window and their averages were calculated. The final predictions were mapped to human functional movement primitives using a Logistic Regression algorithm. Algorithm performance was assessed with a leave-one-out analysis, which determined its sensitivity, specificity, and positive and negative predictive values for all classified primitives. In healthy control and stroke participants, our approach identified functional movement primitives embedded in training activities with, on average, 80% precision. This approach may support functional movement dosing in stroke rehabilitation. PMID:28813877

Research on gait-based human identification

NASA Astrophysics Data System (ADS)

Li, Youguo

Gait recognition refers to automatic identification of individual based on his/her style of walking. This paper proposes a gait recognition method based on Continuous Hidden Markov Model with Mixture of Gaussians(G-CHMM). First, we initialize a Gaussian mix model for training image sequence with K-means algorithm, then train the HMM parameters using a Baum-Welch algorithm. These gait feature sequences can be trained and obtain a Continuous HMM for every person, therefore, the 7 key frames and the obtained HMM can represent each person's gait sequence. Finally, the recognition is achieved by Front algorithm. The experiments made on CASIA gait databases obtain comparatively high correction identification ratio and comparatively strong robustness for variety of bodily angle.

Hidden Markov models and neural networks for fault detection in dynamic systems

NASA Technical Reports Server (NTRS)

Smyth, Padhraic

1994-01-01

Neural networks plus hidden Markov models (HMM) can provide excellent detection and false alarm rate performance in fault detection applications, as shown in this viewgraph presentation. Modified models allow for novelty detection. Key contributions of neural network models are: (1) excellent nonparametric discrimination capability; (2) a good estimator of posterior state probabilities, even in high dimensions, and thus can be embedded within overall probabilistic model (HMM); and (3) simple to implement compared to other nonparametric models. Neural network/HMM monitoring model is currently being integrated with the new Deep Space Network (DSN) antenna controller software and will be on-line monitoring a new DSN 34-m antenna (DSS-24) by July, 1994.

Hidden Markov models-based system (HMMSPECTR) for detecting structural homologies on the basis of sequential information.

PubMed

Tsigelny, Igor; Sharikov, Yuriy; Ten Eyck, Lynn F

2002-05-01

HMMSPECTR is a tool for finding putative structural homologs for proteins with known primary sequences. HMMSPECTR contains four major components: a data warehouse with the hidden Markov models (HMM) and alignment libraries; a search program which compares the initial protein sequences with the libraries of HMMs; a secondary structure prediction and comparison program; and a dominant protein selection program that prepares the set of 10-15 "best" proteins from the chosen HMMs. The data warehouse contains four libraries of HMMs. The first two libraries were constructed using different HHM preparation options of the HAMMER program. The third library contains parts ("partial HMM") of initial alignments. The fourth library contains trained HMMs. We tested our program against all of the protein targets proposed in the CASP4 competition. The data warehouse included libraries of structural alignments and HMMs constructed on the basis of proteins publicly available in the Protein Data Bank before the CASP4 meeting. The newest fully automated versions of HMMSPECTR 1.02 and 1.02ss produced better results than the best result reported at CASP4 either by r.m.s.d. or by length (or both) in 64% (HMMSPECTR 1.02) and 79% (HMMSPECTR 1.02ss) of the cases. The improvement is most notable for the targets with complexity 4 (difficult fold recognition cases).

Role of Heavy Meromyosin in Heat-Induced Gelation in Low Ionic Strength Solution Containing L-Histidine.

PubMed

Hayakawa, Toru; Yoshida, Yuri; Yasui, Masanori; Ito, Toshiaki; Wakamatsu, Jun-ichi; Hattori, Akihito; Nishimura, Takanori

2015-08-01

The gelation of myosin has a very important role in meat products. We have already shown that myosin in low ionic strength solution containing L-histidine forms a transparent gel after heating. To clarify the mechanism of this unique gelation, we investigated the changes in the nature of myosin subfragments during heating in solutions with low and high ionic strengths with and without L-histidine. The hydrophobicity of myosin and heavy meromyosin (HMM) in low ionic strength solution containing L-histidine was lower than in high ionic strength solution. The SH contents of myosin and HMM in low ionic strength solution containing l-histidine did not change during the heating process, whereas in high ionic strength solution they decreased slightly. The heat-induced globular masses of HMM in low ionic strength solution containing L-histidine were smaller than those in high ionic strength solution. These findings suggested that the polymerization of HMM molecules by heating was suppressed in low ionic strength solution containing L-histidine, resulting in formation of the unique gel. © 2015 Institute of Food Technologists®

An HMM model for coiled-coil domains and a comparison with PSSM-based predictions.

PubMed

Delorenzi, Mauro; Speed, Terry

2002-04-01

Large-scale sequence data require methods for the automated annotation of protein domains. Many of the predictive methods are based either on a Position Specific Scoring Matrix (PSSM) of fixed length or on a window-less Hidden Markov Model (HMM). The performance of the two approaches is tested for Coiled-Coil Domains (CCDs). The prediction of CCDs is used frequently, and its optimization seems worthwhile. We have conceived MARCOIL, an HMM for the recognition of proteins with a CCD on a genomic scale. A cross-validated study suggests that MARCOIL improves predictions compared to the traditional PSSM algorithm, especially for some protein families and for short CCDs. The study was designed to reveal differences inherent in the two methods. Potential confounding factors such as differences in the dimension of parameter space and in the parameter values were avoided by using the same amino acid propensities and by keeping the transition probabilities of the HMM constant during cross-validation. The prediction program and the databases are available at http://www.wehi.edu.au/bioweb/Mauro/Marcoil

Using hidden Markov models to align multiple sequences.

PubMed

Mount, David W

2009-07-01

A hidden Markov model (HMM) is a probabilistic model of a multiple sequence alignment (msa) of proteins. In the model, each column of symbols in the alignment is represented by a frequency distribution of the symbols (called a "state"), and insertions and deletions are represented by other states. One moves through the model along a particular path from state to state in a Markov chain (i.e., random choice of next move), trying to match a given sequence. The next matching symbol is chosen from each state, recording its probability (frequency) and also the probability of going to that state from a previous one (the transition probability). State and transition probabilities are multiplied to obtain a probability of the given sequence. The hidden nature of the HMM is due to the lack of information about the value of a specific state, which is instead represented by a probability distribution over all possible values. This article discusses the advantages and disadvantages of HMMs in msa and presents algorithms for calculating an HMM and the conditions for producing the best HMM.

Impact of home-based management of malaria on health outcomes in Africa: a systematic review of the evidence.

PubMed

Hopkins, Heidi; Talisuna, Ambrose; Whitty, Christopher Jm; Staedke, Sarah G

2007-10-08

Home-based management of malaria (HMM) is promoted as a major strategy to improve prompt delivery of effective malaria treatment in Africa. HMM involves presumptively treating febrile children with pre-packaged antimalarial drugs distributed by members of the community. HMM has been implemented in several African countries, and artemisinin-based combination therapies (ACTs) will likely be introduced into these programmes on a wide scale. The published literature was searched for studies that evaluated the health impact of community- and home-based treatment for malaria in Africa. Criteria for inclusion were: 1) the intervention consisted of antimalarial treatment administered presumptively for febrile illness; 2) the treatment was administered by local community members who had no formal education in health care; 3) measured outcomes included specific health indicators such as malaria morbidity (incidence, severity, parasite rates) and/or mortality; and 4) the study was conducted in Africa. Of 1,069 potentially relevant publications identified, only six studies, carried out over 18 years, were identified as meeting inclusion criteria. Heterogeneity of the evaluations, including variability in study design, precluded meta-analysis. All trials evaluated presumptive treatment with chloroquine and were conducted in rural areas, and most were done in settings with seasonal malaria transmission. Conclusions regarding the impact of HMM on morbidity and mortality endpoints were mixed. Two studies showed no health impact, while another showed a decrease in malaria prevalence and incidence, but no impact on mortality. One study in Burkina Faso suggested that HMM decreased the proportion of severe malaria cases, while another study from the same country showed a decrease in the risk of progression to severe malaria. Of the four studies with mortality endpoints only one from Ethiopia showed a positive impact, with a reduction in the under-5 mortality rate of 40.6% (95% CI 29.2 - 50.6). Currently the evidence base for HMM in Africa, particularly regarding use of ACTs, is narrow and priorities for further research are discussed. To optimize treatment and maximize health benefits, drug regimens and delivery strategies in HMM programmes may need to be tailored to local conditions. Additional research could help guide programme development, policy decision-making, and implementation.

Charge-transfer dynamics and nonlocal dielectric permittivity tuned with metamaterial structures as solvent analogues

NASA Astrophysics Data System (ADS)

Lee, Kwang Jin; Xiao, Yiming; Woo, Jae Heun; Kim, Eunsun; Kreher, David; Attias, André-Jean; Mathevet, Fabrice; Ribierre, Jean-Charles; Wu, Jeong Weon; André, Pascal

2017-07-01

Charge transfer (CT) is a fundamental and ubiquitous mechanism in biology, physics and chemistry. Here, we evidence that CT dynamics can be altered by multi-layered hyperbolic metamaterial (HMM) substrates. Taking triphenylene:perylene diimide dyad supramolecular self-assemblies as a model system, we reveal longer-lived CT states in the presence of HMM structures, with both charge separation and recombination characteristic times increased by factors of 2.4 and 1.7--that is, relative variations of 140 and 73%, respectively. To rationalize these experimental results in terms of driving force, we successfully introduce image dipole interactions in Marcus theory. The non-local effect herein demonstrated is directly linked to the number of metal-dielectric pairs, can be formalized in the dielectric permittivity, and is presented as a solid analogue to local solvent polarity effects. This model and extra PH3T:PC60BM results show the generality of this non-local phenomenon and that a wide range of kinetic tailoring opportunities can arise from substrate engineering. This work paves the way toward the design of artificial substrates to control CT dynamics of interest for applications in optoelectronics and chemistry.

Mining protein loops using a structural alphabet and statistical exceptionality

PubMed Central

2010-01-01

Background Protein loops encompass 50% of protein residues in available three-dimensional structures. These regions are often involved in protein functions, e.g. binding site, catalytic pocket... However, the description of protein loops with conventional tools is an uneasy task. Regular secondary structures, helices and strands, have been widely studied whereas loops, because they are highly variable in terms of sequence and structure, are difficult to analyze. Due to data sparsity, long loops have rarely been systematically studied. Results We developed a simple and accurate method that allows the description and analysis of the structures of short and long loops using structural motifs without restriction on loop length. This method is based on the structural alphabet HMM-SA. HMM-SA allows the simplification of a three-dimensional protein structure into a one-dimensional string of states, where each state is a four-residue prototype fragment, called structural letter. The difficult task of the structural grouping of huge data sets is thus easily accomplished by handling structural letter strings as in conventional protein sequence analysis. We systematically extracted all seven-residue fragments in a bank of 93000 protein loops and grouped them according to the structural-letter sequence, named structural word. This approach permits a systematic analysis of loops of all sizes since we consider the structural motifs of seven residues rather than complete loops. We focused the analysis on highly recurrent words of loops (observed more than 30 times). Our study reveals that 73% of loop-lengths are covered by only 3310 highly recurrent structural words out of 28274 observed words). These structural words have low structural variability (mean RMSd of 0.85 Å). As expected, half of these motifs display a flanking-region preference but interestingly, two thirds are shared by short (less than 12 residues) and long loops. Moreover, half of recurrent motifs exhibit a significant level of amino-acid conservation with at least four significant positions and 87% of long loops contain at least one such word. We complement our analysis with the detection of statistically over-represented patterns of structural letters as in conventional DNA sequence analysis. About 30% (930) of structural words are over-represented, and cover about 40% of loop lengths. Interestingly, these words exhibit lower structural variability and higher sequential specificity, suggesting structural or functional constraints. Conclusions We developed a method to systematically decompose and study protein loops using recurrent structural motifs. This method is based on the structural alphabet HMM-SA and not on structural alignment and geometrical parameters. We extracted meaningful structural motifs that are found in both short and long loops. To our knowledge, it is the first time that pattern mining helps to increase the signal-to-noise ratio in protein loops. This finding helps to better describe protein loops and might permit to decrease the complexity of long-loop analysis. Detailed results are available at http://www.mti.univ-paris-diderot.fr/publication/supplementary/2009/ACCLoop/. PMID:20132552

Mining protein loops using a structural alphabet and statistical exceptionality.

PubMed

Regad, Leslie; Martin, Juliette; Nuel, Gregory; Camproux, Anne-Claude

2010-02-04

Protein loops encompass 50% of protein residues in available three-dimensional structures. These regions are often involved in protein functions, e.g. binding site, catalytic pocket... However, the description of protein loops with conventional tools is an uneasy task. Regular secondary structures, helices and strands, have been widely studied whereas loops, because they are highly variable in terms of sequence and structure, are difficult to analyze. Due to data sparsity, long loops have rarely been systematically studied. We developed a simple and accurate method that allows the description and analysis of the structures of short and long loops using structural motifs without restriction on loop length. This method is based on the structural alphabet HMM-SA. HMM-SA allows the simplification of a three-dimensional protein structure into a one-dimensional string of states, where each state is a four-residue prototype fragment, called structural letter. The difficult task of the structural grouping of huge data sets is thus easily accomplished by handling structural letter strings as in conventional protein sequence analysis. We systematically extracted all seven-residue fragments in a bank of 93000 protein loops and grouped them according to the structural-letter sequence, named structural word. This approach permits a systematic analysis of loops of all sizes since we consider the structural motifs of seven residues rather than complete loops. We focused the analysis on highly recurrent words of loops (observed more than 30 times). Our study reveals that 73% of loop-lengths are covered by only 3310 highly recurrent structural words out of 28274 observed words). These structural words have low structural variability (mean RMSd of 0.85 A). As expected, half of these motifs display a flanking-region preference but interestingly, two thirds are shared by short (less than 12 residues) and long loops. Moreover, half of recurrent motifs exhibit a significant level of amino-acid conservation with at least four significant positions and 87% of long loops contain at least one such word. We complement our analysis with the detection of statistically over-represented patterns of structural letters as in conventional DNA sequence analysis. About 30% (930) of structural words are over-represented, and cover about 40% of loop lengths. Interestingly, these words exhibit lower structural variability and higher sequential specificity, suggesting structural or functional constraints. We developed a method to systematically decompose and study protein loops using recurrent structural motifs. This method is based on the structural alphabet HMM-SA and not on structural alignment and geometrical parameters. We extracted meaningful structural motifs that are found in both short and long loops. To our knowledge, it is the first time that pattern mining helps to increase the signal-to-noise ratio in protein loops. This finding helps to better describe protein loops and might permit to decrease the complexity of long-loop analysis. Detailed results are available at http://www.mti.univ-paris-diderot.fr/publication/supplementary/2009/ACCLoop/.

Tissue multifractality and hidden Markov model based integrated framework for optimum precancer detection

NASA Astrophysics Data System (ADS)

Mukhopadhyay, Sabyasachi; Das, Nandan K.; Kurmi, Indrajit; Pradhan, Asima; Ghosh, Nirmalya; Panigrahi, Prasanta K.

2017-10-01

We report the application of a hidden Markov model (HMM) on multifractal tissue optical properties derived via the Born approximation-based inverse light scattering method for effective discrimination of precancerous human cervical tissue sites from the normal ones. Two global fractal parameters, generalized Hurst exponent and the corresponding singularity spectrum width, computed by multifractal detrended fluctuation analysis (MFDFA), are used here as potential biomarkers. We develop a methodology that makes use of these multifractal parameters by integrating with different statistical classifiers like the HMM and support vector machine (SVM). It is shown that the MFDFA-HMM integrated model achieves significantly better discrimination between normal and different grades of cancer as compared to the MFDFA-SVM integrated model.

HMMBinder: DNA-Binding Protein Prediction Using HMM Profile Based Features.

PubMed

Zaman, Rianon; Chowdhury, Shahana Yasmin; Rashid, Mahmood A; Sharma, Alok; Dehzangi, Abdollah; Shatabda, Swakkhar

2017-01-01

DNA-binding proteins often play important role in various processes within the cell. Over the last decade, a wide range of classification algorithms and feature extraction techniques have been used to solve this problem. In this paper, we propose a novel DNA-binding protein prediction method called HMMBinder. HMMBinder uses monogram and bigram features extracted from the HMM profiles of the protein sequences. To the best of our knowledge, this is the first application of HMM profile based features for the DNA-binding protein prediction problem. We applied Support Vector Machines (SVM) as a classification technique in HMMBinder. Our method was tested on standard benchmark datasets. We experimentally show that our method outperforms the state-of-the-art methods found in the literature.

Accelerating Information Retrieval from Profile Hidden Markov Model Databases.

PubMed

Tamimi, Ahmad; Ashhab, Yaqoub; Tamimi, Hashem

2016-01-01

Profile Hidden Markov Model (Profile-HMM) is an efficient statistical approach to represent protein families. Currently, several databases maintain valuable protein sequence information as profile-HMMs. There is an increasing interest to improve the efficiency of searching Profile-HMM databases to detect sequence-profile or profile-profile homology. However, most efforts to enhance searching efficiency have been focusing on improving the alignment algorithms. Although the performance of these algorithms is fairly acceptable, the growing size of these databases, as well as the increasing demand for using batch query searching approach, are strong motivations that call for further enhancement of information retrieval from profile-HMM databases. This work presents a heuristic method to accelerate the current profile-HMM homology searching approaches. The method works by cluster-based remodeling of the database to reduce the search space, rather than focusing on the alignment algorithms. Using different clustering techniques, 4284 TIGRFAMs profiles were clustered based on their similarities. A representative for each cluster was assigned. To enhance sensitivity, we proposed an extended step that allows overlapping among clusters. A validation benchmark of 6000 randomly selected protein sequences was used to query the clustered profiles. To evaluate the efficiency of our approach, speed and recall values were measured and compared with the sequential search approach. Using hierarchical, k-means, and connected component clustering techniques followed by the extended overlapping step, we obtained an average reduction in time of 41%, and an average recall of 96%. Our results demonstrate that representation of profile-HMMs using a clustering-based approach can significantly accelerate data retrieval from profile-HMM databases.

Consistency of VDJ Rearrangement and Substitution Parameters Enables Accurate B Cell Receptor Sequence Annotation.

PubMed

Ralph, Duncan K; Matsen, Frederick A

2016-01-01

VDJ rearrangement and somatic hypermutation work together to produce antibody-coding B cell receptor (BCR) sequences for a remarkable diversity of antigens. It is now possible to sequence these BCRs in high throughput; analysis of these sequences is bringing new insight into how antibodies develop, in particular for broadly-neutralizing antibodies against HIV and influenza. A fundamental step in such sequence analysis is to annotate each base as coming from a specific one of the V, D, or J genes, or from an N-addition (a.k.a. non-templated insertion). Previous work has used simple parametric distributions to model transitions from state to state in a hidden Markov model (HMM) of VDJ recombination, and assumed that mutations occur via the same process across sites. However, codon frame and other effects have been observed to violate these parametric assumptions for such coding sequences, suggesting that a non-parametric approach to modeling the recombination process could be useful. In our paper, we find that indeed large modern data sets suggest a model using parameter-rich per-allele categorical distributions for HMM transition probabilities and per-allele-per-position mutation probabilities, and that using such a model for inference leads to significantly improved results. We present an accurate and efficient BCR sequence annotation software package using a novel HMM "factorization" strategy. This package, called partis (https://github.com/psathyrella/partis/), is built on a new general-purpose HMM compiler that can perform efficient inference given a simple text description of an HMM.

Hidden Markov models for evolution and comparative genomics analysis.

PubMed

Bykova, Nadezda A; Favorov, Alexander V; Mironov, Andrey A

2013-01-01

The problem of reconstruction of ancestral states given a phylogeny and data from extant species arises in a wide range of biological studies. The continuous-time Markov model for the discrete states evolution is generally used for the reconstruction of ancestral states. We modify this model to account for a case when the states of the extant species are uncertain. This situation appears, for example, if the states for extant species are predicted by some program and thus are known only with some level of reliability; it is common for bioinformatics field. The main idea is formulation of the problem as a hidden Markov model on a tree (tree HMM, tHMM), where the basic continuous-time Markov model is expanded with the introduction of emission probabilities of observed data (e.g. prediction scores) for each underlying discrete state. Our tHMM decoding algorithm allows us to predict states at the ancestral nodes as well as to refine states at the leaves on the basis of quantitative comparative genomics. The test on the simulated data shows that the tHMM approach applied to the continuous variable reflecting the probabilities of the states (i.e. prediction score) appears to be more accurate then the reconstruction from the discrete states assignment defined by the best score threshold. We provide examples of applying our model to the evolutionary analysis of N-terminal signal peptides and transcription factor binding sites in bacteria. The program is freely available at http://bioinf.fbb.msu.ru/~nadya/tHMM and via web-service at http://bioinf.fbb.msu.ru/treehmmweb.

An HMM-based algorithm for evaluating rates of receptor–ligand binding kinetics from thermal fluctuation data

PubMed Central

Ju, Lining; Wang, Yijie Dylan; Hung, Ying; Wu, Chien-Fu Jeff; Zhu, Cheng

2013-01-01

Motivation: Abrupt reduction/resumption of thermal fluctuations of a force probe has been used to identify association/dissociation events of protein–ligand bonds. We show that off-rate of molecular dissociation can be estimated by the analysis of the bond lifetime, while the on-rate of molecular association can be estimated by the analysis of the waiting time between two neighboring bond events. However, the analysis relies heavily on subjective judgments and is time-consuming. To automate the process of mapping out bond events from thermal fluctuation data, we develop a hidden Markov model (HMM)-based method. Results: The HMM method represents the bond state by a hidden variable with two values: bound and unbound. The bond association/dissociation is visualized and pinpointed. We apply the method to analyze a key receptor–ligand interaction in the early stage of hemostasis and thrombosis: the von Willebrand factor (VWF) binding to platelet glycoprotein Ibα (GPIbα). The numbers of bond lifetime and waiting time events estimated by the HMM are much more than those estimated by a descriptive statistical method from the same set of raw data. The kinetic parameters estimated by the HMM are in excellent agreement with those by a descriptive statistical analysis, but have much smaller errors for both wild-type and two mutant VWF-A1 domains. Thus, the computerized analysis allows us to speed up the analysis and improve the quality of estimates of receptor–ligand binding kinetics. Contact: jeffwu@isye.gatech.edu or cheng.zhu@bme.gatech.edu PMID:23599504

Performance testing and results of the first Etec CORE-2564

NASA Astrophysics Data System (ADS)

Franks, C. Edward; Shikata, Asao; Baker, Catherine A.

1993-03-01

In order to be able to write 64 megabit DRAM reticles, to prepare to write 256 megabit DRAM reticles and in general to meet the current and next generation mask and reticle quality requirements, Hoya Micro Mask (HMM) installed in 1991 the first CORE-2564 Laser Reticle Writer from Etec Systems, Inc. The system was delivered as a CORE-2500XP and was subsequently upgraded to a 2564. The CORE (Custom Optical Reticle Engraver) system produces photomasks with an exposure strategy similar to that employed by an electron beam system, but it uses a laser beam to deliver the photoresist exposure energy. Since then the 2564 has been tested by Etec's standard Acceptance Test Procedure and by several supplementary HMM techniques to insure performance to all the Etec advertised specifications and certain additional HMM requirements that were more demanding and/or more thorough than the advertised specifications. The primary purpose of the HMM tests was to more closely duplicate mask usage. The performance aspects covered by the tests include registration accuracy and repeatability; linewidth accuracy, uniformity and linearity; stripe butting; stripe and scan linearity; edge quality; system cleanliness; minimum geometry resolution; minimum address size and plate loading accuracy and repeatability.

De novo identification of highly diverged protein repeats by probabilistic consistency.

PubMed

Biegert, A; Söding, J

2008-03-15

An estimated 25% of all eukaryotic proteins contain repeats, which underlines the importance of duplication for evolving new protein functions. Internal repeats often correspond to structural or functional units in proteins. Methods capable of identifying diverged repeated segments or domains at the sequence level can therefore assist in predicting domain structures, inferring hypotheses about function and mechanism, and investigating the evolution of proteins from smaller fragments. We present HHrepID, a method for the de novo identification of repeats in protein sequences. It is able to detect the sequence signature of structural repeats in many proteins that have not yet been known to possess internal sequence symmetry, such as outer membrane beta-barrels. HHrepID uses HMM-HMM comparison to exploit evolutionary information in the form of multiple sequence alignments of homologs. In contrast to a previous method, the new method (1) generates a multiple alignment of repeats; (2) utilizes the transitive nature of homology through a novel merging procedure with fully probabilistic treatment of alignments; (3) improves alignment quality through an algorithm that maximizes the expected accuracy; (4) is able to identify different kinds of repeats within complex architectures by a probabilistic domain boundary detection method and (5) improves sensitivity through a new approach to assess statistical significance. Server: http://toolkit.tuebingen.mpg.de/hhrepid; Executables: ftp://ftp.tuebingen.mpg.de/pub/protevo/HHrepID

Regulation of Motivation: Predicting Students' Homework Motivation Management at the Secondary School Level

ERIC Educational Resources Information Center

Xu, Jianzhong

2014-01-01

This study examines models of variables posited to predict students' homework motivation management (HMM), based on survey data from 866 8th graders (61 classes) and 745 11th graders (46 classes) in the south-eastern USA. Most of the variance in HMM occurred at the student level, with parent education as the only significant predictor at the class…

A state-based probabilistic model for tumor respiratory motion prediction

NASA Astrophysics Data System (ADS)

Kalet, Alan; Sandison, George; Wu, Huanmei; Schmitz, Ruth

2010-12-01

This work proposes a new probabilistic mathematical model for predicting tumor motion and position based on a finite state representation using the natural breathing states of exhale, inhale and end of exhale. Tumor motion was broken down into linear breathing states and sequences of states. Breathing state sequences and the observables representing those sequences were analyzed using a hidden Markov model (HMM) to predict the future sequences and new observables. Velocities and other parameters were clustered using a k-means clustering algorithm to associate each state with a set of observables such that a prediction of state also enables a prediction of tumor velocity. A time average model with predictions based on average past state lengths was also computed. State sequences which are known a priori to fit the data were fed into the HMM algorithm to set a theoretical limit of the predictive power of the model. The effectiveness of the presented probabilistic model has been evaluated for gated radiation therapy based on previously tracked tumor motion in four lung cancer patients. Positional prediction accuracy is compared with actual position in terms of the overall RMS errors. Various system delays, ranging from 33 to 1000 ms, were tested. Previous studies have shown duty cycles for latencies of 33 and 200 ms at around 90% and 80%, respectively, for linear, no prediction, Kalman filter and ANN methods as averaged over multiple patients. At 1000 ms, the previously reported duty cycles range from approximately 62% (ANN) down to 34% (no prediction). Average duty cycle for the HMM method was found to be 100% and 91 ± 3% for 33 and 200 ms latency and around 40% for 1000 ms latency in three out of four breathing motion traces. RMS errors were found to be lower than linear and no prediction methods at latencies of 1000 ms. The results show that for system latencies longer than 400 ms, the time average HMM prediction outperforms linear, no prediction, and the more general HMM-type predictive models. RMS errors for the time average model approach the theoretical limit of the HMM, and predicted state sequences are well correlated with sequences known to fit the data.

Analysis of swallowing sounds using hidden Markov models.

PubMed

Aboofazeli, Mohammad; Moussavi, Zahra

2008-04-01

In recent years, acoustical analysis of the swallowing mechanism has received considerable attention due to its diagnostic potentials. This paper presents a hidden Markov model (HMM) based method for the swallowing sound segmentation and classification. Swallowing sound signals of 15 healthy and 11 dysphagic subjects were studied. The signals were divided into sequences of 25 ms segments each of which were represented by seven features. The sequences of features were modeled by HMMs. Trained HMMs were used for segmentation of the swallowing sounds into three distinct phases, i.e., initial quiet period, initial discrete sounds (IDS) and bolus transit sounds (BTS). Among the seven features, accuracy of segmentation by the HMM based on multi-scale product of wavelet coefficients was higher than that of the other HMMs and the linear prediction coefficient (LPC)-based HMM showed the weakest performance. In addition, HMMs were used for classification of the swallowing sounds of healthy subjects and dysphagic patients. Classification accuracy of different HMM configurations was investigated. When we increased the number of states of the HMMs from 4 to 8, the classification error gradually decreased. In most cases, classification error for N=9 was higher than that of N=8. Among the seven features used, root mean square (RMS) and waveform fractal dimension (WFD) showed the best performance in the HMM-based classification of swallowing sounds. When the sequences of the features of IDS segment were modeled separately, the accuracy reached up to 85.5%. As a second stage classification, a screening algorithm was used which correctly classified all the subjects but one healthy subject when RMS was used as characteristic feature of the swallowing sounds and the number of states was set to N=8.

Conditional Density Estimation with HMM Based Support Vector Machines

NASA Astrophysics Data System (ADS)

Hu, Fasheng; Liu, Zhenqiu; Jia, Chunxin; Chen, Dechang

Conditional density estimation is very important in financial engineer, risk management, and other engineering computing problem. However, most regression models have a latent assumption that the probability density is a Gaussian distribution, which is not necessarily true in many real life applications. In this paper, we give a framework to estimate or predict the conditional density mixture dynamically. Through combining the Input-Output HMM with SVM regression together and building a SVM model in each state of the HMM, we can estimate a conditional density mixture instead of a single gaussian. With each SVM in each node, this model can be applied for not only regression but classifications as well. We applied this model to denoise the ECG data. The proposed method has the potential to apply to other time series such as stock market return predictions.

Algorithms for Hidden Markov Models Restricted to Occurrences of Regular Expressions

PubMed Central

Tataru, Paula; Sand, Andreas; Hobolth, Asger; Mailund, Thomas; Pedersen, Christian N. S.

2013-01-01

Hidden Markov Models (HMMs) are widely used probabilistic models, particularly for annotating sequential data with an underlying hidden structure. Patterns in the annotation are often more relevant to study than the hidden structure itself. A typical HMM analysis consists of annotating the observed data using a decoding algorithm and analyzing the annotation to study patterns of interest. For example, given an HMM modeling genes in DNA sequences, the focus is on occurrences of genes in the annotation. In this paper, we define a pattern through a regular expression and present a restriction of three classical algorithms to take the number of occurrences of the pattern in the hidden sequence into account. We present a new algorithm to compute the distribution of the number of pattern occurrences, and we extend the two most widely used existing decoding algorithms to employ information from this distribution. We show experimentally that the expectation of the distribution of the number of pattern occurrences gives a highly accurate estimate, while the typical procedure can be biased in the sense that the identified number of pattern occurrences does not correspond to the true number. We furthermore show that using this distribution in the decoding algorithms improves the predictive power of the model. PMID:24833225

Spotting handwritten words and REGEX using a two stage BLSTM-HMM architecture

NASA Astrophysics Data System (ADS)

Bideault, Gautier; Mioulet, Luc; Chatelain, Clément; Paquet, Thierry

2015-01-01

In this article, we propose a hybrid model for spotting words and regular expressions (REGEX) in handwritten documents. The model is made of the state-of-the-art BLSTM (Bidirectional Long Short Time Memory) neural network for recognizing and segmenting characters, coupled with a HMM to build line models able to spot the desired sequences. Experiments on the Rimes database show very promising results.

QRS complex detection based on continuous density hidden Markov models using univariate observations

NASA Astrophysics Data System (ADS)

Sotelo, S.; Arenas, W.; Altuve, M.

2018-04-01

In the electrocardiogram (ECG), the detection of QRS complexes is a fundamental step in the ECG signal processing chain since it allows the determination of other characteristics waves of the ECG and provides information about heart rate variability. In this work, an automatic QRS complex detector based on continuous density hidden Markov models (HMM) is proposed. HMM were trained using univariate observation sequences taken either from QRS complexes or their derivatives. The detection approach is based on the log-likelihood comparison of the observation sequence with a fixed threshold. A sliding window was used to obtain the observation sequence to be evaluated by the model. The threshold was optimized by receiver operating characteristic curves. Sensitivity (Sen), specificity (Spc) and F1 score were used to evaluate the detection performance. The approach was validated using ECG recordings from the MIT-BIH Arrhythmia database. A 6-fold cross-validation shows that the best detection performance was achieved with 2 states HMM trained with QRS complexes sequences (Sen = 0.668, Spc = 0.360 and F1 = 0.309). We concluded that these univariate sequences provide enough information to characterize the QRS complex dynamics from HMM. Future works are directed to the use of multivariate observations to increase the detection performance.

Uncovering the cognitive processes underlying mental rotation: an eye-movement study.

PubMed

Xue, Jiguo; Li, Chunyong; Quan, Cheng; Lu, Yiming; Yue, Jingwei; Zhang, Chenggang

2017-08-30

Mental rotation is an important paradigm for spatial ability. Mental-rotation tasks are assumed to involve five or three sequential cognitive-processing states, though this has not been demonstrated experimentally. Here, we investigated how processing states alternate during mental-rotation tasks. Inference was carried out using an advanced statistical modelling and data-driven approach - a discriminative hidden Markov model (dHMM) trained using eye-movement data obtained from an experiment consisting of two different strategies: (I) mentally rotate the right-side figure to be aligned with the left-side figure and (II) mentally rotate the left-side figure to be aligned with the right-side figure. Eye movements were found to contain the necessary information for determining the processing strategy, and the dHMM that best fit our data segmented the mental-rotation process into three hidden states, which we termed encoding and searching, comparison, and searching on one-side pair. Additionally, we applied three classification methods, logistic regression, support vector model and dHMM, of which dHMM predicted the strategies with the highest accuracy (76.8%). Our study did confirm that there are differences in processing states between these two of mental-rotation strategies, and were consistent with the previous suggestion that mental rotation is discrete process that is accomplished in a piecemeal fashion.

Optimization of a near-field thermophotovoltaic system operating at low temperature and large vacuum gap

NASA Astrophysics Data System (ADS)

Lim, Mikyung; Song, Jaeman; Kim, Jihoon; Lee, Seung S.; Lee, Ikjin; Lee, Bong Jae

2018-05-01

The present work successfully achieves a strong enhancement in performance of a near-field thermophotovoltaic (TPV) system operating at low temperature and large-vacuum-gap width by introducing a hyperbolic-metamaterial (HMM) emitter, multilayered graphene, and an Au-backside reflector. Design variables for the HMM emitter and the multilayered-graphene-covered TPV cell are optimized for maximizing the power output of the near-field TPV system with the genetic algorithm. The near-field TPV system with the optimized configuration results in 24.2 times of enhancement in power output compared with that of the system with a bulk emitter and a bare TPV cell. Through the analysis of the radiative heat transfer together with surface-plasmon-polariton (SPP) dispersion curves, it is found that coupling of SPPs generated from both the HMM emitter and the multilayered-graphene-covered TPV cell plays a key role in a substantial increase in the heat transfer even at a 200-nm vacuum gap. Further, the backside reflector at the bottom of the TPV cell significantly increases not only the conversion efficiency, but also the power output by generating additional polariton modes which can be readily coupled with the existing SPPs of the HMM emitter and the multilayered-graphene-covered TPV cell.

Hidden Markov Model and Support Vector Machine based decoding of finger movements using Electrocorticography

PubMed Central

Wissel, Tobias; Pfeiffer, Tim; Frysch, Robert; Knight, Robert T.; Chang, Edward F.; Hinrichs, Hermann; Rieger, Jochem W.; Rose, Georg

2013-01-01

Objective Support Vector Machines (SVM) have developed into a gold standard for accurate classification in Brain-Computer-Interfaces (BCI). The choice of the most appropriate classifier for a particular application depends on several characteristics in addition to decoding accuracy. Here we investigate the implementation of Hidden Markov Models (HMM)for online BCIs and discuss strategies to improve their performance. Approach We compare the SVM, serving as a reference, and HMMs for classifying discrete finger movements obtained from the Electrocorticograms of four subjects doing a finger tapping experiment. The classifier decisions are based on a subset of low-frequency time domain and high gamma oscillation features. Main results We show that decoding optimization between the two approaches is due to the way features are extracted and selected and less dependent on the classifier. An additional gain in HMM performance of up to 6% was obtained by introducing model constraints. Comparable accuracies of up to 90% were achieved with both SVM and HMM with the high gamma cortical response providing the most important decoding information for both techniques. Significance We discuss technical HMM characteristics and adaptations in the context of the presented data as well as for general BCI applications. Our findings suggest that HMMs and their characteristics are promising for efficient online brain-computer interfaces. PMID:24045504

Structural features based genome-wide characterization and prediction of nucleosome organization

PubMed Central

2012-01-01

Background Nucleosome distribution along chromatin dictates genomic DNA accessibility and thus profoundly influences gene expression. However, the underlying mechanism of nucleosome formation remains elusive. Here, taking a structural perspective, we systematically explored nucleosome formation potential of genomic sequences and the effect on chromatin organization and gene expression in S. cerevisiae. Results We analyzed twelve structural features related to flexibility, curvature and energy of DNA sequences. The results showed that some structural features such as DNA denaturation, DNA-bending stiffness, Stacking energy, Z-DNA, Propeller twist and free energy, were highly correlated with in vitro and in vivo nucleosome occupancy. Specifically, they can be classified into two classes, one positively and the other negatively correlated with nucleosome occupancy. These two kinds of structural features facilitated nucleosome binding in centromere regions and repressed nucleosome formation in the promoter regions of protein-coding genes to mediate transcriptional regulation. Based on these analyses, we integrated all twelve structural features in a model to predict more accurately nucleosome occupancy in vivo than the existing methods that mainly depend on sequence compositional features. Furthermore, we developed a novel approach, named DLaNe, that located nucleosomes by detecting peaks of structural profiles, and built a meta predictor to integrate information from different structural features. As a comparison, we also constructed a hidden Markov model (HMM) to locate nucleosomes based on the profiles of these structural features. The result showed that the meta DLaNe and HMM-based method performed better than the existing methods, demonstrating the power of these structural features in predicting nucleosome positions. Conclusions Our analysis revealed that DNA structures significantly contribute to nucleosome organization and influence chromatin structure and gene expression regulation. The results indicated that our proposed methods are effective in predicting nucleosome occupancy and positions and that these structural features are highly predictive of nucleosome organization. The implementation of our DLaNe method based on structural features is available online. PMID:22449207

ECG signal analysis through hidden Markov models.

PubMed

Andreão, Rodrigo V; Dorizzi, Bernadette; Boudy, Jérôme

2006-08-01

This paper presents an original hidden Markov model (HMM) approach for online beat segmentation and classification of electrocardiograms. The HMM framework has been visited because of its ability of beat detection, segmentation and classification, highly suitable to the electrocardiogram (ECG) problem. Our approach addresses a large panel of topics some of them never studied before in other HMM related works: waveforms modeling, multichannel beat segmentation and classification, and unsupervised adaptation to the patient's ECG. The performance was evaluated on the two-channel QT database in terms of waveform segmentation precision, beat detection and classification. Our waveform segmentation results compare favorably to other systems in the literature. We also obtained high beat detection performance with sensitivity of 99.79% and a positive predictivity of 99.96%, using a test set of 59 recordings. Moreover, premature ventricular contraction beats were detected using an original classification strategy. The results obtained validate our approach for real world application.

A proposed OB-fold with a protein-interaction surface in Candida albicans telomerase protein Est3

PubMed Central

Yu, Eun Young; Wang, Feng; Lei, Ming; Lue, Neal F

2008-01-01

Ever shorter telomeres 3 (Est3) is an essential telomerase regulatory subunit thought to be unique to budding yeasts. Here we use multiple sequence alignment and hidden Markov model–hidden Markov model (HMM-HMM) comparison to uncover potential similarities between Est3 and the mammalian telomeric protein Tpp1. Analysis of site-specific mutants of Candida albicans Est3 revealed functional distinctions between residues that are conserved between Est3 and Tpp1 and those that are unique to Est3. Although both types of residues are important for telomere maintenance in vivo, only the former contributes to telomerase activity in vitro and facilitates the association of Est3 with telomerase core components. Consistent with a function in protein-protein interaction, the residues common to Est3 and Tpp1 map to one face of an OB-fold model structure, away from the canonical nucleic acid binding surface. We propose that Est3 and the OB-fold domain of Tpp1 mediate a conserved function in telomerase regulation. PMID:19172753

SVM-dependent pairwise HMM: an application to protein pairwise alignments.

PubMed

Orlando, Gabriele; Raimondi, Daniele; Khan, Taushif; Lenaerts, Tom; Vranken, Wim F

2017-12-15

Methods able to provide reliable protein alignments are crucial for many bioinformatics applications. In the last years many different algorithms have been developed and various kinds of information, from sequence conservation to secondary structure, have been used to improve the alignment performances. This is especially relevant for proteins with highly divergent sequences. However, recent works suggest that different features may have different importance in diverse protein classes and it would be an advantage to have more customizable approaches, capable to deal with different alignment definitions. Here we present Rigapollo, a highly flexible pairwise alignment method based on a pairwise HMM-SVM that can use any type of information to build alignments. Rigapollo lets the user decide the optimal features to align their protein class of interest. It outperforms current state of the art methods on two well-known benchmark datasets when aligning highly divergent sequences. A Python implementation of the algorithm is available at http://ibsquare.be/rigapollo. wim.vranken@vub.be. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

SATCHMO-JS: a webserver for simultaneous protein multiple sequence alignment and phylogenetic tree construction.

PubMed

Hagopian, Raffi; Davidson, John R; Datta, Ruchira S; Samad, Bushra; Jarvis, Glen R; Sjölander, Kimmen

2010-07-01

We present the jump-start simultaneous alignment and tree construction using hidden Markov models (SATCHMO-JS) web server for simultaneous estimation of protein multiple sequence alignments (MSAs) and phylogenetic trees. The server takes as input a set of sequences in FASTA format, and outputs a phylogenetic tree and MSA; these can be viewed online or downloaded from the website. SATCHMO-JS is an extension of the SATCHMO algorithm, and employs a divide-and-conquer strategy to jump-start SATCHMO at a higher point in the phylogenetic tree, reducing the computational complexity of the progressive all-versus-all HMM-HMM scoring and alignment. Results on a benchmark dataset of 983 structurally aligned pairs from the PREFAB benchmark dataset show that SATCHMO-JS provides a statistically significant improvement in alignment accuracy over MUSCLE, Multiple Alignment using Fast Fourier Transform (MAFFT), ClustalW and the original SATCHMO algorithm. The SATCHMO-JS webserver is available at http://phylogenomics.berkeley.edu/satchmo-js. The datasets used in these experiments are available for download at http://phylogenomics.berkeley.edu/satchmo-js/supplementary/.

Optimizing Likelihood Models for Particle Trajectory Segmentation in Multi-State Systems.

PubMed

Young, Dylan Christopher; Scrimgeour, Jan

2018-06-19

Particle tracking offers significant insight into the molecular mechanics that govern the behav- ior of living cells. The analysis of molecular trajectories that transition between different motive states, such as diffusive, driven and tethered modes, is of considerable importance, with even single trajectories containing significant amounts of information about a molecule's environment and its interactions with cellular structures. Hidden Markov models (HMM) have been widely adopted to perform the segmentation of such complex tracks. In this paper, we show that extensive analysis of hidden Markov model outputs using data derived from multi-state Brownian dynamics simulations can be used both for the optimization of the likelihood models used to describe the states of the system and for characterization of the technique's failure mechanisms. This analysis was made pos- sible by the implementation of parallelized adaptive direct search algorithm on a Nvidia graphics processing unit. This approach provides critical information for the visualization of HMM failure and successful design of particle tracking experiments where trajectories contain multiple mobile states. © 2018 IOP Publishing Ltd.

Comparison of RF spectrum prediction methods for dynamic spectrum access

NASA Astrophysics Data System (ADS)

Kovarskiy, Jacob A.; Martone, Anthony F.; Gallagher, Kyle A.; Sherbondy, Kelly D.; Narayanan, Ram M.

2017-05-01

Dynamic spectrum access (DSA) refers to the adaptive utilization of today's busy electromagnetic spectrum. Cognitive radio/radar technologies require DSA to intelligently transmit and receive information in changing environments. Predicting radio frequency (RF) activity reduces sensing time and energy consumption for identifying usable spectrum. Typical spectrum prediction methods involve modeling spectral statistics with Hidden Markov Models (HMM) or various neural network structures. HMMs describe the time-varying state probabilities of Markov processes as a dynamic Bayesian network. Neural Networks model biological brain neuron connections to perform a wide range of complex and often non-linear computations. This work compares HMM, Multilayer Perceptron (MLP), and Recurrent Neural Network (RNN) algorithms and their ability to perform RF channel state prediction. Monte Carlo simulations on both measured and simulated spectrum data evaluate the performance of these algorithms. Generalizing spectrum occupancy as an alternating renewal process allows Poisson random variables to generate simulated data while energy detection determines the occupancy state of measured RF spectrum data for testing. The results suggest that neural networks achieve better prediction accuracy and prove more adaptable to changing spectral statistics than HMMs given sufficient training data.

Sleep extension is a feasible lifestyle intervention in free-living adults who are habitually short sleepers: a potential strategy for decreasing intake of free sugars? A randomized controlled pilot study

PubMed Central

Al Khatib, Haya K; Hall, Wendy L; Creedon, Alice; Ooi, Emily; Masri, Tala; McGowan, Laura; Harding, Scott V; Darzi, Julia; Pot, Gerda K

2018-01-01

ABSTRACT Background Evidence suggests that short sleep duration may be a newly identified modifiable risk factor for obesity, yet there is a paucity of studies to investigate this. Objective We assessed the feasibility of a personalized sleep extension protocol in adults aged 18–64 y who are habitually short sleepers (5 to <7 h), with sleep primarily measured by wrist actigraphy. In addition, we collected pilot data to assess the effects of extended sleep on dietary intake and quality measured by 7-d food diaries, resting and total energy expenditure, physical activity, and markers of cardiometabolic health. Design Forty-two normal-weight healthy participants who were habitually short sleepers completed this free-living, 4-wk, parallel-design randomized controlled trial. The sleep extension group (n = 21) received a behavioral consultation session targeting sleep hygiene. The control group (n = 21) maintained habitual short sleep. Results Rates of participation, attrition, and compliance were 100%, 6.5%, and 85.7%, respectively. The sleep extension group significantly increased time in bed [0:55 hours:minutes (h:mm); 95% CI: 0:37, 1:12 h:mm], sleep period (0:47 h:mm; 95% CI: 0:29, 1:05 h:mm), and sleep duration (0:21 h:mm; 95% CI: 0:06, 0:36 h:mm) compared with the control group. Sleep extension led to reduced intake of free sugars (–9.6 g; 95% CI: –16.0, –3.1 g) compared with control (0.7 g; 95% CI: –5.7, 7.2 g) (P = 0.042). A sensitivity analysis in plausible reporters showed that the sleep extension group reduced intakes of fat (percentage), carbohydrates (grams), and free sugars (grams) in comparison to the control group. There were no significant differences between groups in markers of energy balance or cardiometabolic health. Conclusions We showed the feasibility of extending sleep in adult short sleepers. Sleep extension led to reduced free sugar intakes and may be a viable strategy to facilitate limiting excessive consumption of free sugars in an obesity-promoting environment. This trial was registered at www.clinicaltrials.gov as NCT02787577. PMID:29381788

Mining adverse drug reactions from online healthcare forums using hidden Markov model.

PubMed

Sampathkumar, Hariprasad; Chen, Xue-wen; Luo, Bo

2014-10-23

Adverse Drug Reactions are one of the leading causes of injury or death among patients undergoing medical treatments. Not all Adverse Drug Reactions are identified before a drug is made available in the market. Current post-marketing drug surveillance methods, which are based purely on voluntary spontaneous reports, are unable to provide the early indications necessary to prevent the occurrence of such injuries or fatalities. The objective of this research is to extract reports of adverse drug side-effects from messages in online healthcare forums and use them as early indicators to assist in post-marketing drug surveillance. We treat the task of extracting adverse side-effects of drugs from healthcare forum messages as a sequence labeling problem and present a Hidden Markov Model(HMM) based Text Mining system that can be used to classify a message as containing drug side-effect information and then extract the adverse side-effect mentions from it. A manually annotated dataset from http://www.medications.com is used in the training and validation of the HMM based Text Mining system. A 10-fold cross-validation on the manually annotated dataset yielded on average an F-Score of 0.76 from the HMM Classifier, in comparison to 0.575 from the Baseline classifier. Without the Plain Text Filter component as a part of the Text Processing module, the F-Score of the HMM Classifier was reduced to 0.378 on average, while absence of the HTML Filter component was found to have no impact. Reducing the Drug names dictionary size by half, on average reduced the F-Score of the HMM Classifier to 0.359, while a similar reduction to the side-effects dictionary yielded an F-Score of 0.651 on average. Adverse side-effects mined from http://www.medications.com and http://www.steadyhealth.com were found to match the Adverse Drug Reactions on the Drug Package Labels of several drugs. In addition, some novel adverse side-effects, which can be potential Adverse Drug Reactions, were also identified. The results from the HMM based Text Miner are encouraging to pursue further enhancements to this approach. The mined novel side-effects can act as early indicators for health authorities to help focus their efforts in post-marketing drug surveillance.

One-Shot Learning of Human Activity With an MAP Adapted GMM and Simplex-HMM.

PubMed

Rodriguez, Mario; Orrite, Carlos; Medrano, Carlos; Makris, Dimitrios

2016-05-10

This paper presents a novel activity class representation using a single sequence for training. The contribution of this representation lays on the ability to train an one-shot learning recognition system, useful in new scenarios where capturing and labeling sequences is expensive or impractical. The method uses a universal background model of local descriptors obtained from source databases available on-line and adapts it to a new sequence in the target scenario through a maximum a posteriori adaptation. Each activity sample is encoded in a sequence of normalized bag of features and modeled by a new hidden Markov model formulation, where the expectation-maximization algorithm for training is modified to deal with observations consisting in vectors in a unit simplex. Extensive experiments in recognition have been performed using one-shot learning over the public datasets Weizmann, KTH, and IXMAS. These experiments demonstrate the discriminative properties of the representation and the validity of application in recognition systems, achieving state-of-the-art results.

Online Farsi digit recognition using their upper half structure

NASA Astrophysics Data System (ADS)

Ghods, Vahid; Sohrabi, Mohammad Karim

2015-03-01

In this paper, we investigated the efficiency of upper half Farsi numerical digit structure. In other words, half of data (upper half of the digit shapes) was exploited for the recognition of Farsi numerical digits. This method can be used for both offline and online recognition. Half of data is more effective in speed process, data transfer and in this application accuracy. Hidden Markov model (HMM) was used to classify online Farsi digits. Evaluation was performed by TMU dataset. This dataset contains more than 1200 samples of online handwritten Farsi digits. The proposed method yielded more accuracy in recognition rate.

The Evolution and Future of Marine Corps Medical Evacuation and Casualty Evacuation Operations

DTIC Science & Technology

2011-03-16

PERSON Marine Corps University I Comm~nd and Staff College 19b. TELEPONE NUMBER (Include area code) (703) 784-3330 (Admin Office) Standard Form...they appear in the report, e.g. 001; AFAPL304801 05. 6. AUTHOR(S). Enter name(s) of person (s) responsible for writing the report, performing the...commanders assigned-three squ~ drons (HMM- · 161, HMM -286, and HtviM-364) to a rotation to cover CASEY AC and MEDEY AC operations for all forc~s serving

Optical character recognition of handwritten Arabic using hidden Markov models

NASA Astrophysics Data System (ADS)

Aulama, Mohannad M.; Natsheh, Asem M.; Abandah, Gheith A.; Olama, Mohammed M.

2011-04-01

The problem of optical character recognition (OCR) of handwritten Arabic has not received a satisfactory solution yet. In this paper, an Arabic OCR algorithm is developed based on Hidden Markov Models (HMMs) combined with the Viterbi algorithm, which results in an improved and more robust recognition of characters at the sub-word level. Integrating the HMMs represents another step of the overall OCR trends being currently researched in the literature. The proposed approach exploits the structure of characters in the Arabic language in addition to their extracted features to achieve improved recognition rates. Useful statistical information of the Arabic language is initially extracted and then used to estimate the probabilistic parameters of the mathematical HMM. A new custom implementation of the HMM is developed in this study, where the transition matrix is built based on the collected large corpus, and the emission matrix is built based on the results obtained via the extracted character features. The recognition process is triggered using the Viterbi algorithm which employs the most probable sequence of sub-words. The model was implemented to recognize the sub-word unit of Arabic text raising the recognition rate from being linked to the worst recognition rate for any character to the overall structure of the Arabic language. Numerical results show that there is a potentially large recognition improvement by using the proposed algorithms.

Optical character recognition of handwritten Arabic using hidden Markov models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aulama, Mohannad M.; Natsheh, Asem M.; Abandah, Gheith A.

2011-01-01

The problem of optical character recognition (OCR) of handwritten Arabic has not received a satisfactory solution yet. In this paper, an Arabic OCR algorithm is developed based on Hidden Markov Models (HMMs) combined with the Viterbi algorithm, which results in an improved and more robust recognition of characters at the sub-word level. Integrating the HMMs represents another step of the overall OCR trends being currently researched in the literature. The proposed approach exploits the structure of characters in the Arabic language in addition to their extracted features to achieve improved recognition rates. Useful statistical information of the Arabic language ismore » initially extracted and then used to estimate the probabilistic parameters of the mathematical HMM. A new custom implementation of the HMM is developed in this study, where the transition matrix is built based on the collected large corpus, and the emission matrix is built based on the results obtained via the extracted character features. The recognition process is triggered using the Viterbi algorithm which employs the most probable sequence of sub-words. The model was implemented to recognize the sub-word unit of Arabic text raising the recognition rate from being linked to the worst recognition rate for any character to the overall structure of the Arabic language. Numerical results show that there is a potentially large recognition improvement by using the proposed algorithms.« less

Achieving pattern uniformity in plasmonic lithography by spatial frequency selection

NASA Astrophysics Data System (ADS)

Liang, Gaofeng; Chen, Xi; Zhao, Qing; Guo, L. Jay

2018-01-01

The effects of the surface roughness of thin films and defects on photomasks are investigated in two representative plasmonic lithography systems: thin silver film-based superlens and multilayer-based hyperbolic metamaterial (HMM). Superlens can replicate arbitrary patterns because of its broad evanescent wave passband, which also makes it inherently vulnerable to the roughness of the thin film and imperfections of the mask. On the other hand, the HMM system has spatial frequency filtering characteristics and its pattern formation is based on interference, producing uniform and stable periodic patterns. In this work, we show that the HMM system is more immune to such imperfections due to its function of spatial frequency selection. The analyses are further verified by an interference lithography system incorporating the photoresist layer as an optical waveguide to improve the aspect ratio of the pattern. It is concluded that a system capable of spatial frequency selection is a powerful method to produce deep-subwavelength periodic patterns with high degree of uniformity and fidelity.

Change in ploidy status from hyperdiploid to near-tetraploid in multiple myeloma associated with bortezomib/lenalidomide resistance.

PubMed

Pavlistova, Lenka; Zemanova, Zuzana; Sarova, Iveta; Lhotska, Halka; Berkova, Adela; Spicka, Ivan; Michalova, Kyra

2014-01-01

Ploidy is an important prognostic factor in the risk stratification of multiple myeloma (MM) patients. Patients with MM can be divided into two groups according to the modal number of chromosomes: nonhyperdiploid (NH-MM) and hyperdiploid (H-MM), which has a more favorable outcome. The two ploidy groups represent two different oncogenetic pathways determined at the premalignant stage. The ploidy subtype also persists during the course of the disease, even during progression after the therapy, with only very rare cases of ploidy conversion. The clinical significance of ploidy conversion and its relation to drug resistance have been previously discussed. Here, we describe a female MM patient with a rare change in her ploidy status from H-MM to NH-MM, detected by cytogenetic and molecular cytogenetic examinations of consecutive bone marrow aspirates. We hypothesize that ploidy conversion (from H-MM to NH-MM) is associated with disease progression and acquired resistance to bortezomib/lenalidomide therapy. Copyright © 2014 Elsevier Inc. All rights reserved.

Enhancing speech recognition using improved particle swarm optimization based hidden Markov model.

PubMed

Selvaraj, Lokesh; Ganesan, Balakrishnan

2014-01-01

Enhancing speech recognition is the primary intention of this work. In this paper a novel speech recognition method based on vector quantization and improved particle swarm optimization (IPSO) is suggested. The suggested methodology contains four stages, namely, (i) denoising, (ii) feature mining (iii), vector quantization, and (iv) IPSO based hidden Markov model (HMM) technique (IP-HMM). At first, the speech signals are denoised using median filter. Next, characteristics such as peak, pitch spectrum, Mel frequency Cepstral coefficients (MFCC), mean, standard deviation, and minimum and maximum of the signal are extorted from the denoised signal. Following that, to accomplish the training process, the extracted characteristics are given to genetic algorithm based codebook generation in vector quantization. The initial populations are created by selecting random code vectors from the training set for the codebooks for the genetic algorithm process and IP-HMM helps in doing the recognition. At this point the creativeness will be done in terms of one of the genetic operation crossovers. The proposed speech recognition technique offers 97.14% accuracy.

Automated Error Detection in Physiotherapy Training.

PubMed

Jovanović, Marko; Seiffarth, Johannes; Kutafina, Ekaterina; Jonas, Stephan M

2018-01-01

Manual skills teaching, such as physiotherapy education, requires immediate teacher feedback for the students during the learning process, which to date can only be performed by expert trainers. A machine-learning system trained only on correct performances to classify and score performed movements, to identify sources of errors in the movement and give feedback to the learner. We acquire IMU and sEMG sensor data from a commercial-grade wearable device and construct an HMM-based model for gesture classification, scoring and feedback giving. We evaluate the model on publicly available and self-generated data of an exemplary movement pattern executions. The model achieves an overall accuracy of 90.71% on the public dataset and 98.9% on our dataset. An AUC of 0.99 for the ROC of the scoring method could be achieved to discriminate between correct and untrained incorrect executions. The proposed system demonstrated its suitability for scoring and feedback in manual skills training.

HMM based automated wheelchair navigation using EOG traces in EEG

NASA Astrophysics Data System (ADS)

Aziz, Fayeem; Arof, Hamzah; Mokhtar, Norrima; Mubin, Marizan

2014-10-01

This paper presents a wheelchair navigation system based on a hidden Markov model (HMM), which we developed to assist those with restricted mobility. The semi-autonomous system is equipped with obstacle/collision avoidance sensors and it takes the electrooculography (EOG) signal traces from the user as commands to maneuver the wheelchair. The EOG traces originate from eyeball and eyelid movements and they are embedded in EEG signals collected from the scalp of the user at three different locations. Features extracted from the EOG traces are used to determine whether the eyes are open or closed, and whether the eyes are gazing to the right, center, or left. These features are utilized as inputs to a few support vector machine (SVM) classifiers, whose outputs are regarded as observations to an HMM. The HMM determines the state of the system and generates commands for navigating the wheelchair accordingly. The use of simple features and the implementation of a sliding window that captures important signatures in the EOG traces result in a fast execution time and high classification rates. The wheelchair is equipped with a proximity sensor and it can move forward and backward in three directions. The asynchronous system achieved an average classification rate of 98% when tested with online data while its average execution time was less than 1 s. It was also tested in a navigation experiment where all of the participants managed to complete the tasks successfully without collisions.

HMM based automated wheelchair navigation using EOG traces in EEG.

PubMed

Aziz, Fayeem; Arof, Hamzah; Mokhtar, Norrima; Mubin, Marizan

2014-10-01

This paper presents a wheelchair navigation system based on a hidden Markov model (HMM), which we developed to assist those with restricted mobility. The semi-autonomous system is equipped with obstacle/collision avoidance sensors and it takes the electrooculography (EOG) signal traces from the user as commands to maneuver the wheelchair. The EOG traces originate from eyeball and eyelid movements and they are embedded in EEG signals collected from the scalp of the user at three different locations. Features extracted from the EOG traces are used to determine whether the eyes are open or closed, and whether the eyes are gazing to the right, center, or left. These features are utilized as inputs to a few support vector machine (SVM) classifiers, whose outputs are regarded as observations to an HMM. The HMM determines the state of the system and generates commands for navigating the wheelchair accordingly. The use of simple features and the implementation of a sliding window that captures important signatures in the EOG traces result in a fast execution time and high classification rates. The wheelchair is equipped with a proximity sensor and it can move forward and backward in three directions. The asynchronous system achieved an average classification rate of 98% when tested with online data while its average execution time was less than 1 s. It was also tested in a navigation experiment where all of the participants managed to complete the tasks successfully without collisions.

Detecting Seismic Events Using a Supervised Hidden Markov Model

NASA Astrophysics Data System (ADS)

Burks, L.; Forrest, R.; Ray, J.; Young, C.

2017-12-01

We explore the use of supervised hidden Markov models (HMMs) to detect seismic events in streaming seismogram data. Current methods for seismic event detection include simple triggering algorithms, such as STA/LTA and the Z-statistic, which can lead to large numbers of false positives that must be investigated by an analyst. The hypothesis of this study is that more advanced detection methods, such as HMMs, may decreases false positives while maintaining accuracy similar to current methods. We train a binary HMM classifier using 2 weeks of 3-component waveform data from the International Monitoring System (IMS) that was carefully reviewed by an expert analyst to pick all seismic events. Using an ensemble of simple and discrete features, such as the triggering of STA/LTA, the HMM predicts the time at which transition occurs from noise to signal. Compared to the STA/LTA detection algorithm, the HMM detects more true events, but the false positive rate remains unacceptably high. Future work to potentially decrease the false positive rate may include using continuous features, a Gaussian HMM, and multi-class HMMs to distinguish between types of seismic waves (e.g., P-waves and S-waves). Acknowledgement: Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA-0003525.SAND No: SAND2017-8154 A

Impact of combining intermittent preventive treatment with home management of malaria in children less than 10 years in a rural area of Senegal: a cluster randomized trial.

PubMed

Tine, Roger C K; Faye, Babacar; Ndour, Cheikh T; Ndiaye, Jean L; Ndiaye, Magatte; Bassene, Charlemagne; Magnussen, Pascal; Bygbjerg, Ib C; Sylla, Khadim; Ndour, Jacques D; Gaye, Oumar

2011-12-13

Current malaria control strategies recommend (i) early case detection using rapid diagnostic tests (RDT) and treatment with artemisinin combination therapy (ACT), (ii) pre-referral rectal artesunate, (iii) intermittent preventive treatment and (iv) impregnated bed nets. However, these individual malaria control interventions provide only partial protection in most epidemiological situations. Therefore, there is a need to investigate the potential benefits of integrating several malaria interventions to reduce malaria prevalence and morbidity. A randomized controlled trial was carried out to assess the impact of combining seasonal intermittent preventive treatment in children (IPTc) with home-based management of malaria (HMM) by community health workers (CHWs) in Senegal. Eight CHWs in eight villages covered by the Bonconto health post, (South Eastern part of Senegal) were trained to diagnose malaria using RDT, provide prompt treatment with artemether-lumefantrine for uncomplicated malaria cases and pre-referral rectal artesunate for complicated malaria occurring in children under 10 years. Four CHWs were randomized to also administer monthly IPTc as single dose of sulphadoxine-pyrimethamine (SP) plus three doses of amodiaquine (AQ) in the malaria transmission season, October and November 2010. Primary end point was incidence of single episode of malaria attacks over 8 weeks of follow up. Secondary end points included prevalence of malaria parasitaemia, and prevalence of anaemia at the end of the transmission season. Primary analysis was by intention to treat. The study protocol was approved by the Senegalese National Ethical Committee (approval 0027/MSP/DS/CNRS, 18/03/2010). A total of 1,000 children were enrolled. The incidence of malaria episodes was 7.1/100 child months at risk [95% CI (3.7-13.7)] in communities with IPTc + HMM compared to 35.6/100 child months at risk [95% CI (26.7-47.4)] in communities with only HMM (aOR = 0.20; 95% CI 0.09-0.41; p = 0.04). At the end of the transmission season, malaria parasitaemia prevalence was lower in communities with IPTc + HMM (2.05% versus 4.6% p = 0.03). Adjusted for age groups, sex, Plasmodium falciparum carriage and prevalence of malnutrition, IPTc + HMM showed a significant protective effect against anaemia (aOR = 0.59; 95% CI 0.42-0.82; p = 0.02). Combining IPTc and HMM can provide significant additional benefit in preventing clinical episodes of malaria as well as anaemia among children in Senegal.

A High-Performance Neural Prosthesis Incorporating Discrete State Selection With Hidden Markov Models.

PubMed

Kao, Jonathan C; Nuyujukian, Paul; Ryu, Stephen I; Shenoy, Krishna V

2017-04-01

Communication neural prostheses aim to restore efficient communication to people with motor neurological injury or disease by decoding neural activity into control signals. These control signals are both analog (e.g., the velocity of a computer mouse) and discrete (e.g., clicking an icon with a computer mouse) in nature. Effective, high-performing, and intuitive-to-use communication prostheses should be capable of decoding both analog and discrete state variables seamlessly. However, to date, the highest-performing autonomous communication prostheses rely on precise analog decoding and typically do not incorporate high-performance discrete decoding. In this report, we incorporated a hidden Markov model (HMM) into an intracortical communication prosthesis to enable accurate and fast discrete state decoding in parallel with analog decoding. In closed-loop experiments with nonhuman primates implanted with multielectrode arrays, we demonstrate that incorporating an HMM into a neural prosthesis can increase state-of-the-art achieved bitrate by 13.9% and 4.2% in two monkeys ( ). We found that the transition model of the HMM is critical to achieving this performance increase. Further, we found that using an HMM resulted in the highest achieved peak performance we have ever observed for these monkeys, achieving peak bitrates of 6.5, 5.7, and 4.7 bps in Monkeys J, R, and L, respectively. Finally, we found that this neural prosthesis was robustly controllable for the duration of entire experimental sessions. These results demonstrate that high-performance discrete decoding can be beneficially combined with analog decoding to achieve new state-of-the-art levels of performance.

Smart Annotation of Cyclic Data Using Hierarchical Hidden Markov Models.

PubMed

Martindale, Christine F; Hoenig, Florian; Strohrmann, Christina; Eskofier, Bjoern M

2017-10-13

Cyclic signals are an intrinsic part of daily life, such as human motion and heart activity. The detailed analysis of them is important for clinical applications such as pathological gait analysis and for sports applications such as performance analysis. Labeled training data for algorithms that analyze these cyclic data come at a high annotation cost due to only limited annotations available under laboratory conditions or requiring manual segmentation of the data under less restricted conditions. This paper presents a smart annotation method that reduces this cost of labeling for sensor-based data, which is applicable to data collected outside of strict laboratory conditions. The method uses semi-supervised learning of sections of cyclic data with a known cycle number. A hierarchical hidden Markov model (hHMM) is used, achieving a mean absolute error of 0.041 ± 0.020 s relative to a manually-annotated reference. The resulting model was also used to simultaneously segment and classify continuous, 'in the wild' data, demonstrating the applicability of using hHMM, trained on limited data sections, to label a complete dataset. This technique achieved comparable results to its fully-supervised equivalent. Our semi-supervised method has the significant advantage of reduced annotation cost. Furthermore, it reduces the opportunity for human error in the labeling process normally required for training of segmentation algorithms. It also lowers the annotation cost of training a model capable of continuous monitoring of cycle characteristics such as those employed to analyze the progress of movement disorders or analysis of running technique.

Classification of a set of vectors using self-organizing map- and rule-based technique

NASA Astrophysics Data System (ADS)

Ae, Tadashi; Okaniwa, Kaishirou; Nosaka, Kenzaburou

2005-02-01

There exist various objects, such as pictures, music, texts, etc., around our environment. We have a view for these objects by looking, reading or listening. Our view is concerned with our behaviors deeply, and is very important to understand our behaviors. We have a view for an object, and decide the next action (data selection, etc.) with our view. Such a series of actions constructs a sequence. Therefore, we propose a method which acquires a view as a vector from several words for a view, and apply the vector to sequence generation. We focus on sequences of the data of which a user selects from a multimedia database containing pictures, music, movie, etc... These data cannot be stereotyped because user's view for them changes by each user. Therefore, we represent the structure of the multimedia database as the vector representing user's view and the stereotyped vector, and acquire sequences containing the structure as elements. Such a vector can be classified by SOM (Self-Organizing Map). Hidden Markov Model (HMM) is a method to generate sequences. Therefore, we use HMM of which a state corresponds to the representative vector of user's view, and acquire sequences containing the change of user's view. We call it Vector-state Markov Model (VMM). We introduce the rough set theory as a rule-base technique, which plays a role of classifying the sets of data such as the sets of "Tour".

Prediction of lipoprotein signal peptides in Gram-negative bacteria.

PubMed

Juncker, Agnieszka S; Willenbrock, Hanni; Von Heijne, Gunnar; Brunak, Søren; Nielsen, Henrik; Krogh, Anders

2003-08-01

A method to predict lipoprotein signal peptides in Gram-negative Eubacteria, LipoP, has been developed. The hidden Markov model (HMM) was able to distinguish between lipoproteins (SPaseII-cleaved proteins), SPaseI-cleaved proteins, cytoplasmic proteins, and transmembrane proteins. This predictor was able to predict 96.8% of the lipoproteins correctly with only 0.3% false positives in a set of SPaseI-cleaved, cytoplasmic, and transmembrane proteins. The results obtained were significantly better than those of previously developed methods. Even though Gram-positive lipoprotein signal peptides differ from Gram-negatives, the HMM was able to identify 92.9% of the lipoproteins included in a Gram-positive test set. A genome search was carried out for 12 Gram-negative genomes and one Gram-positive genome. The results for Escherichia coli K12 were compared with new experimental data, and the predictions by the HMM agree well with the experimentally verified lipoproteins. A neural network-based predictor was developed for comparison, and it gave very similar results. LipoP is available as a Web server at www.cbs.dtu.dk/services/LipoP/.

Prediction of lipoprotein signal peptides in Gram-negative bacteria

PubMed Central

Juncker, Agnieszka S.; Willenbrock, Hanni; von Heijne, Gunnar; Brunak, Søren; Nielsen, Henrik; Krogh, Anders

2003-01-01

A method to predict lipoprotein signal peptides in Gram-negative Eubacteria, LipoP, has been developed. The hidden Markov model (HMM) was able to distinguish between lipoproteins (SPaseII-cleaved proteins), SPaseI-cleaved proteins, cytoplasmic proteins, and transmembrane proteins. This predictor was able to predict 96.8% of the lipoproteins correctly with only 0.3% false positives in a set of SPaseI-cleaved, cytoplasmic, and transmembrane proteins. The results obtained were significantly better than those of previously developed methods. Even though Gram-positive lipoprotein signal peptides differ from Gram-negatives, the HMM was able to identify 92.9% of the lipoproteins included in a Gram-positive test set. A genome search was carried out for 12 Gram-negative genomes and one Gram-positive genome. The results for Escherichia coli K12 were compared with new experimental data, and the predictions by the HMM agree well with the experimentally verified lipoproteins. A neural network-based predictor was developed for comparison, and it gave very similar results. LipoP is available as a Web server at www.cbs.dtu.dk/services/LipoP/. PMID:12876315

Development of a Fault Monitoring Technique for Wind Turbines Using a Hidden Markov Model.

PubMed

Shin, Sung-Hwan; Kim, SangRyul; Seo, Yun-Ho

2018-06-02

Regular inspection for the maintenance of the wind turbines is difficult because of their remote locations. For this reason, condition monitoring systems (CMSs) are typically installed to monitor their health condition. The purpose of this study is to propose a fault detection algorithm for the mechanical parts of the wind turbine. To this end, long-term vibration data were collected over two years by a CMS installed on a 3 MW wind turbine. The vibration distribution at a specific rotating speed of main shaft is approximated by the Weibull distribution and its cumulative distribution function is utilized for determining the threshold levels that indicate impending failure of mechanical parts. A Hidden Markov model (HMM) is employed to propose the statistical fault detection algorithm in the time domain and the method whereby the input sequence for HMM is extracted is also introduced by considering the threshold levels and the correlation between the signals. Finally, it was demonstrated that the proposed HMM algorithm achieved a greater than 95% detection success rate by using the long-term signals.

Is Media Multitasking Good for Cybersecurity? Exploring the Relationship Between Media Multitasking and Everyday Cognitive Failures on Self-Reported Risky Cybersecurity Behaviors.

PubMed

Hadlington, Lee; Murphy, Karen

2018-03-01

The current study focused on how engaging in media multitasking (MMT) and the experience of everyday cognitive failures impact on the individual's engagement in risky cybersecurity behaviors (RCsB). In total, 144 participants (32 males, 112 females) completed an online survey. The age range for participants was 18 to 43 years (M = 20.63, SD = 4.04). Participants completed three scales which included an inventory of weekly MMT, a measure of everyday cognitive failures, and RCsB. There was a significant difference between heavy media multitaskers (HMM), average media multitaskers (AMM), and light media multitaskers (LMM) in terms of RCsB, with HMM demonstrating more frequent risky behaviors than LMM or AMM. The HMM group also reported more cognitive failures in everyday life than the LMM group. A regression analysis showed that everyday cognitive failures and MMT acted as significant predictors for RCsB. These results expand our current understanding of the relationship between human factors and cybersecurity behaviors, which are useful to inform the design of training and intervention packages to mitigate RCsB.

HIV-1 Vif promotes the formation of high molecular mass APOBEC3G complexes

PubMed Central

Goila-Gaur, Ritu; Khan, Mohammad A.; Miyagi, Eri; Kao, Sandra; Opi, Sandrine; Takeuchi, Hiroaki; Strebel, Klaus

2008-01-01

HIV-1 Vif inhibits the antiviral activity of APOBEC3G (APO3G) by inducing proteasomal degradation. Here, we studied the effects of Vif on APO3G in vitro. In this system, Vif did not cause APO3G degradation. Instead, Vif induced changes in APO3G that affected immunoprecipitation of the native protein. This effect required wt Vif and was reversed by heat-denaturation of APO3G. Sucrose gradient analysis demonstrated that wt Vif induced the gradual transition of APO3G translated in vitro or expressed in HeLa cells from a low molecular mass conformation to puromycin-sensitive high molecular mass (HMM) complexes. In the absence of Vif or the presence of biologically inactive Vif APO3G failed to form HMM complexes. Our results expose a novel function of Vif that promotes the assembly of APO3G into presumably packaging-incompetent HMM complexes and may explain how Vif can overcome the APO3G-imposed block to HIV replication under conditions of no or inefficient APO3G degradation. PMID:18023836

Human gait recognition by pyramid of HOG feature on silhouette images

NASA Astrophysics Data System (ADS)

Yang, Guang; Yin, Yafeng; Park, Jeanrok; Man, Hong

2013-03-01

As a uncommon biometric modality, human gait recognition has a great advantage of identify people at a distance without high resolution images. It has attracted much attention in recent years, especially in the fields of computer vision and remote sensing. In this paper, we propose a human gait recognition framework that consists of a reliable background subtraction method followed by the pyramid of Histogram of Gradient (pHOG) feature extraction on the silhouette image, and a Hidden Markov Model (HMM) based classifier. Through background subtraction, the silhouette of human gait in each frame is extracted and normalized from the raw video sequence. After removing the shadow and noise in each region of interest (ROI), pHOG feature is computed on the silhouettes images. Then the pHOG features of each gait class will be used to train a corresponding HMM. In the test stage, pHOG feature will be extracted from each test sequence and used to calculate the posterior probability toward each trained HMM model. Experimental results on the CASIA Gait Dataset B1 demonstrate that with our proposed method can achieve very competitive recognition rate.

Recognizing Whispered Speech Produced by an Individual with Surgically Reconstructed Larynx Using Articulatory Movement Data

PubMed Central

Cao, Beiming; Kim, Myungjong; Mau, Ted; Wang, Jun

2017-01-01

Individuals with larynx (vocal folds) impaired have problems in controlling their glottal vibration, producing whispered speech with extreme hoarseness. Standard automatic speech recognition using only acoustic cues is typically ineffective for whispered speech because the corresponding spectral characteristics are distorted. Articulatory cues such as the tongue and lip motion may help in recognizing whispered speech since articulatory motion patterns are generally not affected. In this paper, we investigated whispered speech recognition for patients with reconstructed larynx using articulatory movement data. A data set with both acoustic and articulatory motion data was collected from a patient with surgically reconstructed larynx using an electromagnetic articulograph. Two speech recognition systems, Gaussian mixture model-hidden Markov model (GMM-HMM) and deep neural network-HMM (DNN-HMM), were used in the experiments. Experimental results showed adding either tongue or lip motion data to acoustic features such as mel-frequency cepstral coefficient (MFCC) significantly reduced the phone error rates on both speech recognition systems. Adding both tongue and lip data achieved the best performance. PMID:29423453

Handwritten digits recognition using HMM and PSO based on storks

NASA Astrophysics Data System (ADS)

Yan, Liao; Jia, Zhenhong; Yang, Jie; Pang, Shaoning

2010-07-01

A new method for handwritten digits recognition based on hidden markov model (HMM) and particle swarm optimization (PSO) is proposed. This method defined 24 strokes with the sense of directional, to make up for the shortage that is sensitive in choice of stating point in traditional methods, but also reduce the ambiguity caused by shakes. Make use of excellent global convergence of PSO; improving the probability of finding the optimum and avoiding local infinitesimal obviously. Experimental results demonstrate that compared with the traditional methods, the proposed method can make most of the recognition rate of handwritten digits improved.

A New Algorithm for Identifying Cis-Regulatory Modules Based on Hidden Markov Model

PubMed Central

2017-01-01

The discovery of cis-regulatory modules (CRMs) is the key to understanding mechanisms of transcription regulation. Since CRMs have specific regulatory structures that are the basis for the regulation of gene expression, how to model the regulatory structure of CRMs has a considerable impact on the performance of CRM identification. The paper proposes a CRM discovery algorithm called ComSPS. ComSPS builds a regulatory structure model of CRMs based on HMM by exploring the rules of CRM transcriptional grammar that governs the internal motif site arrangement of CRMs. We test ComSPS on three benchmark datasets and compare it with five existing methods. Experimental results show that ComSPS performs better than them. PMID:28497059

Substrate specificity of low-molecular mass bacterial DD-peptidases.

PubMed

Nemmara, Venkatesh V; Dzhekieva, Liudmila; Sarkar, Kumar Subarno; Adediran, S A; Duez, Colette; Nicholas, Robert A; Pratt, R F

2011-11-22

The bacterial DD-peptidases or penicillin-binding proteins (PBPs) catalyze the formation and regulation of cross-links in peptidoglycan biosynthesis. They are classified into two groups, the high-molecular mass (HMM) and low-molecular mass (LMM) enzymes. The latter group, which is subdivided into classes A-C (LMMA, -B, and -C, respectively), is believed to catalyze DD-carboxypeptidase and endopeptidase reactions in vivo. To date, the specificity of their reactions with particular elements of peptidoglycan structure has not, in general, been defined. This paper describes the steady-state kinetics of hydrolysis of a series of specific peptidoglycan-mimetic peptides, representing various elements of stem peptide structure, catalyzed by a range of LMM PBPs (the LMMA enzymes, Escherichia coli PBP5, Neisseria gonorrhoeae PBP4, and Streptococcus pneumoniae PBP3, and the LMMC enzymes, the Actinomadura R39 dd-peptidase, Bacillus subtilis PBP4a, and N. gonorrhoeae PBP3). The R39 enzyme (LMMC), like the previously studied Streptomyces R61 DD-peptidase (LMMB), specifically and rapidly hydrolyzes stem peptide fragments with a free N-terminus. In accord with this result, the crystal structures of the R61 and R39 enzymes display a binding site specific to the stem peptide N-terminus. These are water-soluble enzymes, however, with no known specific function in vivo. On the other hand, soluble versions of the remaining enzymes of those noted above, all of which are likely to be membrane-bound and/or associated in vivo and have been assigned particular roles in cell wall biosynthesis and maintenance, show little or no specificity for peptides containing elements of peptidoglycan structure. Peptidoglycan-mimetic boronate transition-state analogues do inhibit these enzymes but display notable specificity only for the LMMC enzymes, where, unlike peptide substrates, they may be able to effectively induce a specific active site structure. The manner in which LMMA (and HMM) DD-peptidases achieve substrate specificity, both in vitro and in vivo, remains unknown. © 2011 American Chemical Society

Comparing Four Age Model Techniques using Nine Sediment Cores from the Iberian Margin

NASA Astrophysics Data System (ADS)

Lisiecki, L. E.; Jones, A. M.; Lawrence, C.

2017-12-01

Interpretations of paleoclimate records from ocean sediment cores rely on age models, which provide estimates of age as a function of core depth. Here we compare four methods used to generate age models for sediment cores for the past 140 kyr. The first method is based on radiocarbon dating using the Bayesian statistical software, Bacon [Blaauw and Christen, 2011]. The second method aligns benthic δ18O to a target core using the probabilistic alignment algorithm, HMM-Match, which also generates age uncertainty estimates [Lin et al., 2014]. The third and fourth methods are planktonic δ18O and sea surface temperature (SST) alignments to the same target core, using the alignment algorithm Match [Lisiecki and Lisiecki, 2002]. Unlike HMM-Match, Match requires parameter tuning and does not produce uncertainty estimates. The results of these four age model techniques are compared for nine high-resolution cores from the Iberian margin. The root mean square error between the individual age model results and each core's average estimated age is 1.4 kyr. Additionally, HMM-Match and Bacon age estimates agree to within uncertainty and have similar 95% confidence widths of 1-2 kyr for the highest resolution records. In one core, the planktonic and SST alignments did not fall within the 95% confidence intervals from HMM-Match. For this core, the surface proxy alignments likely produce more reliable results due to millennial-scale SST variability and the presence of several gaps in the benthic δ18O data. Similar studies of other oceanographic regions are needed to determine the spatial extents over which these climate proxies may be stratigraphically correlated.

Gaze patterns hold key to unlocking successful search strategies and increasing polyp detection rate in colonoscopy.

PubMed

Lami, Mariam; Singh, Harsimrat; Dilley, James H; Ashraf, Hajra; Edmondon, Matthew; Orihuela-Espina, Felipe; Hoare, Jonathan; Darzi, Ara; Sodergren, Mikael H

2018-02-07

The adenoma detection rate (ADR) is an important quality indicator in colonoscopy. The aim of this study was to evaluate the changes in visual gaze patterns (VGPs) with increasing polyp detection rate (PDR), a surrogate marker of ADR. 18 endoscopists participated in the study. VGPs were measured using eye-tracking technology during the withdrawal phase of colonoscopy. VGPs were characterized using two analyses - screen and anatomy. Eye-tracking parameters were used to characterize performance, which was further substantiated using hidden Markov model (HMM) analysis. Subjects with higher PDRs spent more time viewing the outer ring of the 3 × 3 grid for both analyses (screen-based: r = 0.56, P  = 0.02; anatomy: r = 0.62, P  < 0.01). Fixation distribution to the "bottom U" of the screen in screen-based analysis was positively correlated with PDR (r = 0.62, P  = 0.01). HMM demarcated the VGPs into three PDR groups. This study defined distinct VGPs that are associated with expert behavior. These data may allow introduction of visual gaze training within structured training programs, and have implications for adoption in higher-level assessment. © Georg Thieme Verlag KG Stuttgart · New York.

Incorporating advanced language models into the P300 speller using particle filtering

NASA Astrophysics Data System (ADS)

Speier, W.; Arnold, C. W.; Deshpande, A.; Knall, J.; Pouratian, N.

2015-08-01

Objective. The P300 speller is a common brain-computer interface (BCI) application designed to communicate language by detecting event related potentials in a subject’s electroencephalogram signal. Information about the structure of natural language can be valuable for BCI communication, but attempts to use this information have thus far been limited to rudimentary n-gram models. While more sophisticated language models are prevalent in natural language processing literature, current BCI analysis methods based on dynamic programming cannot handle their complexity. Approach. Sampling methods can overcome this complexity by estimating the posterior distribution without searching the entire state space of the model. In this study, we implement sequential importance resampling, a commonly used particle filtering (PF) algorithm, to integrate a probabilistic automaton language model. Main result. This method was first evaluated offline on a dataset of 15 healthy subjects, which showed significant increases in speed and accuracy when compared to standard classification methods as well as a recently published approach using a hidden Markov model (HMM). An online pilot study verified these results as the average speed and accuracy achieved using the PF method was significantly higher than that using the HMM method. Significance. These findings strongly support the integration of domain-specific knowledge into BCI classification to improve system performance.

Image segmentation using hidden Markov Gauss mixture models.

PubMed

Pyun, Kyungsuk; Lim, Johan; Won, Chee Sun; Gray, Robert M

2007-07-01

Image segmentation is an important tool in image processing and can serve as an efficient front end to sophisticated algorithms and thereby simplify subsequent processing. We develop a multiclass image segmentation method using hidden Markov Gauss mixture models (HMGMMs) and provide examples of segmentation of aerial images and textures. HMGMMs incorporate supervised learning, fitting the observation probability distribution given each class by a Gauss mixture estimated using vector quantization with a minimum discrimination information (MDI) distortion. We formulate the image segmentation problem using a maximum a posteriori criteria and find the hidden states that maximize the posterior density given the observation. We estimate both the hidden Markov parameter and hidden states using a stochastic expectation-maximization algorithm. Our results demonstrate that HMGMM provides better classification in terms of Bayes risk and spatial homogeneity of the classified objects than do several popular methods, including classification and regression trees, learning vector quantization, causal hidden Markov models (HMMs), and multiresolution HMMs. The computational load of HMGMM is similar to that of the causal HMM.

Hidden Markov models of biological primary sequence information.

PubMed Central

Baldi, P; Chauvin, Y; Hunkapiller, T; McClure, M A

1994-01-01

Hidden Markov model (HMM) techniques are used to model families of biological sequences. A smooth and convergent algorithm is introduced to iteratively adapt the transition and emission parameters of the models from the examples in a given family. The HMM approach is applied to three protein families: globins, immunoglobulins, and kinases. In all cases, the models derived capture the important statistical characteristics of the family and can be used for a number of tasks, including multiple alignments, motif detection, and classification. For K sequences of average length N, this approach yields an effective multiple-alignment algorithm which requires O(KN2) operations, linear in the number of sequences. PMID:8302831

Dual Sticky Hierarchical Dirichlet Process Hidden Markov Model and Its Application to Natural Language Description of Motions.

PubMed

Hu, Weiming; Tian, Guodong; Kang, Yongxin; Yuan, Chunfeng; Maybank, Stephen

2017-09-25

In this paper, a new nonparametric Bayesian model called the dual sticky hierarchical Dirichlet process hidden Markov model (HDP-HMM) is proposed for mining activities from a collection of time series data such as trajectories. All the time series data are clustered. Each cluster of time series data, corresponding to a motion pattern, is modeled by an HMM. Our model postulates a set of HMMs that share a common set of states (topics in an analogy with topic models for document processing), but have unique transition distributions. For the application to motion trajectory modeling, topics correspond to motion activities. The learnt topics are clustered into atomic activities which are assigned predicates. We propose a Bayesian inference method to decompose a given trajectory into a sequence of atomic activities. On combining the learnt sources and sinks, semantic motion regions, and the learnt sequence of atomic activities, the action represented by the trajectory can be described in natural language in as automatic a way as possible. The effectiveness of our dual sticky HDP-HMM is validated on several trajectory datasets. The effectiveness of the natural language descriptions for motions is demonstrated on the vehicle trajectories extracted from a traffic scene.

Improving a HMM-based off-line handwriting recognition system using MME-PSO optimization

NASA Astrophysics Data System (ADS)

Hamdani, Mahdi; El Abed, Haikal; Hamdani, Tarek M.; Märgner, Volker; Alimi, Adel M.

2011-01-01

One of the trivial steps in the development of a classifier is the design of its architecture. This paper presents a new algorithm, Multi Models Evolvement (MME) using Particle Swarm Optimization (PSO). This algorithm is a modified version of the basic PSO, which is used to the unsupervised design of Hidden Markov Model (HMM) based architectures. For instance, the proposed algorithm is applied to an Arabic handwriting recognizer based on discrete probability HMMs. After the optimization of their architectures, HMMs are trained with the Baum- Welch algorithm. The validation of the system is based on the IfN/ENIT database. The performance of the developed approach is compared to the participating systems at the 2005 competition organized on Arabic handwriting recognition on the International Conference on Document Analysis and Recognition (ICDAR). The final system is a combination between an optimized HMM with 6 other HMMs obtained by a simple variation of the number of states. An absolute improvement of 6% of word recognition rate with about 81% is presented. This improvement is achieved comparing to the basic system (ARAB-IfN). The proposed recognizer outperforms also most of the known state-of-the-art systems.

Is Media Multitasking Good for Cybersecurity? Exploring the Relationship Between Media Multitasking and Everyday Cognitive Failures on Self-Reported Risky Cybersecurity Behaviors

PubMed Central

Murphy, Karen

2018-01-01

Abstract The current study focused on how engaging in media multitasking (MMT) and the experience of everyday cognitive failures impact on the individual's engagement in risky cybersecurity behaviors (RCsB). In total, 144 participants (32 males, 112 females) completed an online survey. The age range for participants was 18 to 43 years (M = 20.63, SD = 4.04). Participants completed three scales which included an inventory of weekly MMT, a measure of everyday cognitive failures, and RCsB. There was a significant difference between heavy media multitaskers (HMM), average media multitaskers (AMM), and light media multitaskers (LMM) in terms of RCsB, with HMM demonstrating more frequent risky behaviors than LMM or AMM. The HMM group also reported more cognitive failures in everyday life than the LMM group. A regression analysis showed that everyday cognitive failures and MMT acted as significant predictors for RCsB. These results expand our current understanding of the relationship between human factors and cybersecurity behaviors, which are useful to inform the design of training and intervention packages to mitigate RCsB. PMID:29638157

Video event classification and image segmentation based on noncausal multidimensional hidden Markov models.

PubMed

Ma, Xiang; Schonfeld, Dan; Khokhar, Ashfaq A

2009-06-01

In this paper, we propose a novel solution to an arbitrary noncausal, multidimensional hidden Markov model (HMM) for image and video classification. First, we show that the noncausal model can be solved by splitting it into multiple causal HMMs and simultaneously solving each causal HMM using a fully synchronous distributed computing framework, therefore referred to as distributed HMMs. Next we present an approximate solution to the multiple causal HMMs that is based on an alternating updating scheme and assumes a realistic sequential computing framework. The parameters of the distributed causal HMMs are estimated by extending the classical 1-D training and classification algorithms to multiple dimensions. The proposed extension to arbitrary causal, multidimensional HMMs allows state transitions that are dependent on all causal neighbors. We, thus, extend three fundamental algorithms to multidimensional causal systems, i.e., 1) expectation-maximization (EM), 2) general forward-backward (GFB), and 3) Viterbi algorithms. In the simulations, we choose to limit ourselves to a noncausal 2-D model whose noncausality is along a single dimension, in order to significantly reduce the computational complexity. Simulation results demonstrate the superior performance, higher accuracy rate, and applicability of the proposed noncausal HMM framework to image and video classification.

Towards parameter-free classification of sound effects in movies

NASA Astrophysics Data System (ADS)

Chu, Selina; Narayanan, Shrikanth; Kuo, C.-C. J.

2005-08-01

The problem of identifying intense events via multimedia data mining in films is investigated in this work. Movies are mainly characterized by dialog, music, and sound effects. We begin our investigation with detecting interesting events through sound effects. Sound effects are neither speech nor music, but are closely associated with interesting events such as car chases and gun shots. In this work, we utilize low-level audio features including MFCC and energy to identify sound effects. It was shown in previous work that the Hidden Markov model (HMM) works well for speech/audio signals. However, this technique requires a careful choice in designing the model and choosing correct parameters. In this work, we introduce a framework that will avoid such necessity and works well with semi- and non-parametric learning algorithms.

Learning cellular sorting pathways using protein interactions and sequence motifs.

PubMed

Lin, Tien-Ho; Bar-Joseph, Ziv; Murphy, Robert F

2011-11-01

Proper subcellular localization is critical for proteins to perform their roles in cellular functions. Proteins are transported by different cellular sorting pathways, some of which take a protein through several intermediate locations until reaching its final destination. The pathway a protein is transported through is determined by carrier proteins that bind to specific sequence motifs. In this article, we present a new method that integrates protein interaction and sequence motif data to model how proteins are sorted through these sorting pathways. We use a hidden Markov model (HMM) to represent protein sorting pathways. The model is able to determine intermediate sorting states and to assign carrier proteins and motifs to the sorting pathways. In simulation studies, we show that the method can accurately recover an underlying sorting model. Using data for yeast, we show that our model leads to accurate prediction of subcellular localization. We also show that the pathways learned by our model recover many known sorting pathways and correctly assign proteins to the path they utilize. The learned model identified new pathways and their putative carriers and motifs and these may represent novel protein sorting mechanisms. Supplementary results and software implementation are available from http://murphylab.web.cmu.edu/software/2010_RECOMB_pathways/.

Post processing of optically recognized text via second order hidden Markov model

NASA Astrophysics Data System (ADS)

Poudel, Srijana

In this thesis, we describe a postprocessing system on Optical Character Recognition(OCR) generated text. Second Order Hidden Markov Model (HMM) approach is used to detect and correct the OCR related errors. The reason for choosing the 2nd order HMM is to keep track of the bigrams so that the model can represent the system more accurately. Based on experiments with training data of 159,733 characters and testing of 5,688 characters, the model was able to correct 43.38 % of the errors with a precision of 75.34 %. However, the precision value indicates that the model introduced some new errors, decreasing the correction percentage to 26.4%.

Tunable graphene-based hyperbolic metamaterial operating in SCLU telecom bands.

PubMed

Janaszek, Bartosz; Tyszka-Zawadzka, Anna; Szczepański, Paweł

2016-10-17

The tunability of graphene-based hyperbolic metamaterial structure operating in SCLU telecom bands is investigated. For the first time it has been shown that for the proper design of a graphene/dielectric multilayer stack, the HMM Type I, Epsilon-Near-Zero and Type II regimes are possible by changing the biasing potential. Numerical results reveal the effect of structure parameters such as the thickness of the dielectric layer as well as a number of graphene sheets in a unit cell (i.e., dielectric/graphene bilayer) on the tunability range and shape of the dispersion characteristics (i.e., Type I/ENZ/Type II) in SCLU telecom bands. This kind of materials could offer a technological platform for novel devices having various applications in optical communications technology.

A Robust Self-Alignment Method for Ship's Strapdown INS Under Mooring Conditions

PubMed Central

Sun, Feng; Lan, Haiyu; Yu, Chunyang; El-Sheimy, Naser; Zhou, Guangtao; Cao, Tong; Liu, Hang

2013-01-01

Strapdown inertial navigation systems (INS) need an alignment process to determine the initial attitude matrix between the body frame and the navigation frame. The conventional alignment process is to compute the initial attitude matrix using the gravity and Earth rotational rate measurements. However, under mooring conditions, the inertial measurement unit (IMU) employed in a ship's strapdown INS often suffers from both the intrinsic sensor noise components and the external disturbance components caused by the motions of the sea waves and wind waves, so a rapid and precise alignment of a ship's strapdown INS without any auxiliary information is hard to achieve. A robust solution is given in this paper to solve this problem. The inertial frame based alignment method is utilized to adapt the mooring condition, most of the periodical low-frequency external disturbance components could be removed by the mathematical integration and averaging characteristic of this method. A novel prefilter named hidden Markov model based Kalman filter (HMM-KF) is proposed to remove the relatively high-frequency error components. Different from the digital filters, the HMM-KF barely cause time-delay problem. The turntable, mooring and sea experiments favorably validate the rapidness and accuracy of the proposed self-alignment method and the good de-noising performance of HMM-KF. PMID:23799492

Using DEDICOM for completely unsupervised part-of-speech tagging.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chew, Peter A.; Bader, Brett William; Rozovskaya, Alla

A standard and widespread approach to part-of-speech tagging is based on Hidden Markov Models (HMMs). An alternative approach, pioneered by Schuetze (1993), induces parts of speech from scratch using singular value decomposition (SVD). We introduce DEDICOM as an alternative to SVD for part-of-speech induction. DEDICOM retains the advantages of SVD in that it is completely unsupervised: no prior knowledge is required to induce either the tagset or the associations of terms with tags. However, unlike SVD, it is also fully compatible with the HMM framework, in that it can be used to estimate emission- and transition-probability matrices which can thenmore » be used as the input for an HMM. We apply the DEDICOM method to the CONLL corpus (CONLL 2000) and compare the output of DEDICOM to the part-of-speech tags given in the corpus, and find that the correlation (almost 0.5) is quite high. Using DEDICOM, we also estimate part-of-speech ambiguity for each term, and find that these estimates correlate highly with part-of-speech ambiguity as measured in the original corpus (around 0.88). Finally, we show how the output of DEDICOM can be evaluated and compared against the more familiar output of supervised HMM-based tagging.« less

High-resolution definition of the Vibrio cholerae essential gene set with hidden Markov model–based analyses of transposon-insertion sequencing data

PubMed Central

Chao, Michael C.; Pritchard, Justin R.; Zhang, Yanjia J.; Rubin, Eric J.; Livny, Jonathan; Davis, Brigid M.; Waldor, Matthew K.

2013-01-01

The coupling of high-density transposon mutagenesis to high-throughput DNA sequencing (transposon-insertion sequencing) enables simultaneous and genome-wide assessment of the contributions of individual loci to bacterial growth and survival. We have refined analysis of transposon-insertion sequencing data by normalizing for the effect of DNA replication on sequencing output and using a hidden Markov model (HMM)-based filter to exploit heretofore unappreciated information inherent in all transposon-insertion sequencing data sets. The HMM can smooth variations in read abundance and thereby reduce the effects of read noise, as well as permit fine scale mapping that is independent of genomic annotation and enable classification of loci into several functional categories (e.g. essential, domain essential or ‘sick’). We generated a high-resolution map of genomic loci (encompassing both intra- and intergenic sequences) that are required or beneficial for in vitro growth of the cholera pathogen, Vibrio cholerae. This work uncovered new metabolic and physiologic requirements for V. cholerae survival, and by combining transposon-insertion sequencing and transcriptomic data sets, we also identified several novel noncoding RNA species that contribute to V. cholerae growth. Our findings suggest that HMM-based approaches will enhance extraction of biological meaning from transposon-insertion sequencing genomic data. PMID:23901011

A Novel Approach to ECG Classification Based upon Two-Layered HMMs in Body Sensor Networks

PubMed Central

Liang, Wei; Zhang, Yinlong; Tan, Jindong; Li, Yang

2014-01-01

This paper presents a novel approach to ECG signal filtering and classification. Unlike the traditional techniques which aim at collecting and processing the ECG signals with the patient being still, lying in bed in hospitals, our proposed algorithm is intentionally designed for monitoring and classifying the patient's ECG signals in the free-living environment. The patients are equipped with wearable ambulatory devices the whole day, which facilitates the real-time heart attack detection. In ECG preprocessing, an integral-coefficient-band-stop (ICBS) filter is applied, which omits time-consuming floating-point computations. In addition, two-layered Hidden Markov Models (HMMs) are applied to achieve ECG feature extraction and classification. The periodic ECG waveforms are segmented into ISO intervals, P subwave, QRS complex and T subwave respectively in the first HMM layer where expert-annotation assisted Baum-Welch algorithm is utilized in HMM modeling. Then the corresponding interval features are selected and applied to categorize the ECG into normal type or abnormal type (PVC, APC) in the second HMM layer. For verifying the effectiveness of our algorithm on abnormal signal detection, we have developed an ECG body sensor network (BSN) platform, whereby real-time ECG signals are collected, transmitted, displayed and the corresponding classification outcomes are deduced and shown on the BSN screen. PMID:24681668

Hierarchical structure for audio-video based semantic classification of sports video sequences

NASA Astrophysics Data System (ADS)

Kolekar, M. H.; Sengupta, S.

2005-07-01

A hierarchical structure for sports event classification based on audio and video content analysis is proposed in this paper. Compared to the event classifications in other games, those of cricket are very challenging and yet unexplored. We have successfully solved cricket video classification problem using a six level hierarchical structure. The first level performs event detection based on audio energy and Zero Crossing Rate (ZCR) of short-time audio signal. In the subsequent levels, we classify the events based on video features using a Hidden Markov Model implemented through Dynamic Programming (HMM-DP) using color or motion as a likelihood function. For some of the game-specific decisions, a rule-based classification is also performed. Our proposed hierarchical structure can easily be applied to any other sports. Our results are very promising and we have moved a step forward towards addressing semantic classification problems in general.

Daily Rainfall Simulation Using Climate Variables and Nonhomogeneous Hidden Markov Model

NASA Astrophysics Data System (ADS)

Jung, J.; Kim, H. S.; Joo, H. J.; Han, D.

2017-12-01

Markov chain is an easy method to handle when we compare it with other ones for the rainfall simulation. However, it also has limitations in reflecting seasonal variability of rainfall or change on rainfall patterns caused by climate change. This study applied a Nonhomogeneous Hidden Markov Model(NHMM) to consider these problems. The NHMM compared with a Hidden Markov Model(HMM) for the evaluation of a goodness of the model. First, we chose Gum river basin in Korea to apply the models and collected daily rainfall data from the stations. Also, the climate variables of geopotential height, temperature, zonal wind, and meridional wind date were collected from NCEP/NCAR reanalysis data to consider external factors affecting the rainfall event. We conducted a correlation analysis between rainfall and climate variables then developed a linear regression equation using the climate variables which have high correlation with rainfall. The monthly rainfall was obtained by the regression equation and it became input data of NHMM. Finally, the daily rainfall by NHMM was simulated and we evaluated the goodness of fit and prediction capability of NHMM by comparing with those of HMM. As a result of simulation by HMM, the correlation coefficient and root mean square error of daily/monthly rainfall were 0.2076 and 10.8243/131.1304mm each. In case of NHMM, the correlation coefficient and root mean square error of daily/monthly rainfall were 0.6652 and 10.5112/100.9865mm each. We could verify that the error of daily and monthly rainfall simulated by NHMM was improved by 2.89% and 22.99% compared with HMM. Therefore, it is expected that the results of the study could provide more accurate data for hydrologic analysis. Acknowledgements This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT & Future Planning(2017R1A2B3005695)

Is having similar eye movement patterns during face learning and recognition beneficial for recognition performance? Evidence from hidden Markov modeling.

PubMed

Chuk, Tim; Chan, Antoni B; Hsiao, Janet H

2017-12-01

The hidden Markov model (HMM)-based approach for eye movement analysis is able to reflect individual differences in both spatial and temporal aspects of eye movements. Here we used this approach to understand the relationship between eye movements during face learning and recognition, and its association with recognition performance. We discovered holistic (i.e., mainly looking at the face center) and analytic (i.e., specifically looking at the two eyes in addition to the face center) patterns during both learning and recognition. Although for both learning and recognition, participants who adopted analytic patterns had better recognition performance than those with holistic patterns, a significant positive correlation between the likelihood of participants' patterns being classified as analytic and their recognition performance was only observed during recognition. Significantly more participants adopted holistic patterns during learning than recognition. Interestingly, about 40% of the participants used different patterns between learning and recognition, and among them 90% switched their patterns from holistic at learning to analytic at recognition. In contrast to the scan path theory, which posits that eye movements during learning have to be recapitulated during recognition for the recognition to be successful, participants who used the same or different patterns during learning and recognition did not differ in recognition performance. The similarity between their learning and recognition eye movement patterns also did not correlate with their recognition performance. These findings suggested that perceptuomotor memory elicited by eye movement patterns during learning does not play an important role in recognition. In contrast, the retrieval of diagnostic information for recognition, such as the eyes for face recognition, is a better predictor for recognition performance. Copyright © 2017 Elsevier Ltd. All rights reserved.

Force Sensor Based Tool Condition Monitoring Using a Heterogeneous Ensemble Learning Model

PubMed Central

Wang, Guofeng; Yang, Yinwei; Li, Zhimeng

2014-01-01

Tool condition monitoring (TCM) plays an important role in improving machining efficiency and guaranteeing workpiece quality. In order to realize reliable recognition of the tool condition, a robust classifier needs to be constructed to depict the relationship between tool wear states and sensory information. However, because of the complexity of the machining process and the uncertainty of the tool wear evolution, it is hard for a single classifier to fit all the collected samples without sacrificing generalization ability. In this paper, heterogeneous ensemble learning is proposed to realize tool condition monitoring in which the support vector machine (SVM), hidden Markov model (HMM) and radius basis function (RBF) are selected as base classifiers and a stacking ensemble strategy is further used to reflect the relationship between the outputs of these base classifiers and tool wear states. Based on the heterogeneous ensemble learning classifier, an online monitoring system is constructed in which the harmonic features are extracted from force signals and a minimal redundancy and maximal relevance (mRMR) algorithm is utilized to select the most prominent features. To verify the effectiveness of the proposed method, a titanium alloy milling experiment was carried out and samples with different tool wear states were collected to build the proposed heterogeneous ensemble learning classifier. Moreover, the homogeneous ensemble learning model and majority voting strategy are also adopted to make a comparison. The analysis and comparison results show that the proposed heterogeneous ensemble learning classifier performs better in both classification accuracy and stability. PMID:25405514

Force sensor based tool condition monitoring using a heterogeneous ensemble learning model.

PubMed

Wang, Guofeng; Yang, Yinwei; Li, Zhimeng

2014-11-14

Tool condition monitoring (TCM) plays an important role in improving machining efficiency and guaranteeing workpiece quality. In order to realize reliable recognition of the tool condition, a robust classifier needs to be constructed to depict the relationship between tool wear states and sensory information. However, because of the complexity of the machining process and the uncertainty of the tool wear evolution, it is hard for a single classifier to fit all the collected samples without sacrificing generalization ability. In this paper, heterogeneous ensemble learning is proposed to realize tool condition monitoring in which the support vector machine (SVM), hidden Markov model (HMM) and radius basis function (RBF) are selected as base classifiers and a stacking ensemble strategy is further used to reflect the relationship between the outputs of these base classifiers and tool wear states. Based on the heterogeneous ensemble learning classifier, an online monitoring system is constructed in which the harmonic features are extracted from force signals and a minimal redundancy and maximal relevance (mRMR) algorithm is utilized to select the most prominent features. To verify the effectiveness of the proposed method, a titanium alloy milling experiment was carried out and samples with different tool wear states were collected to build the proposed heterogeneous ensemble learning classifier. Moreover, the homogeneous ensemble learning model and majority voting strategy are also adopted to make a comparison. The analysis and comparison results show that the proposed heterogeneous ensemble learning classifier performs better in both classification accuracy and stability.

MoRFPred-plus: Computational Identification of MoRFs in Protein Sequences using Physicochemical Properties and HMM profiles.

PubMed

Sharma, Ronesh; Bayarjargal, Maitsetseg; Tsunoda, Tatsuhiko; Patil, Ashwini; Sharma, Alok

2018-01-21

Intrinsically Disordered Proteins (IDPs) lack stable tertiary structure and they actively participate in performing various biological functions. These IDPs expose short binding regions called Molecular Recognition Features (MoRFs) that permit interaction with structured protein regions. Upon interaction they undergo a disorder-to-order transition as a result of which their functionality arises. Predicting these MoRFs in disordered protein sequences is a challenging task. In this study, we present MoRFpred-plus, an improved predictor over our previous proposed predictor to identify MoRFs in disordered protein sequences. Two separate independent propensity scores are computed via incorporating physicochemical properties and HMM profiles, these scores are combined to predict final MoRF propensity score for a given residue. The first score reflects the characteristics of a query residue to be part of MoRF region based on the composition and similarity of assumed MoRF and flank regions. The second score reflects the characteristics of a query residue to be part of MoRF region based on the properties of flanks associated around the given residue in the query protein sequence. The propensity scores are processed and common averaging is applied to generate the final prediction score of MoRFpred-plus. Performance of the proposed predictor is compared with available MoRF predictors, MoRFchibi, MoRFpred, and ANCHOR. Using previously collected training and test sets used to evaluate the mentioned predictors, the proposed predictor outperforms these predictors and generates lower false positive rate. In addition, MoRFpred-plus is a downloadable predictor, which makes it useful as it can be used as input to other computational tools. https://github.com/roneshsharma/MoRFpred-plus/wiki/MoRFpred-plus:-Download. Copyright © 2017 Elsevier Ltd. All rights reserved.

Context Analysis of Customer Requests using a Hybrid Adaptive Neuro Fuzzy Inference System and Hidden Markov Models in the Natural Language Call Routing Problem

NASA Astrophysics Data System (ADS)

Rustamov, Samir; Mustafayev, Elshan; Clements, Mark A.

2018-04-01

The context analysis of customer requests in a natural language call routing problem is investigated in the paper. One of the most significant problems in natural language call routing is a comprehension of client request. With the aim of finding a solution to this issue, the Hybrid HMM and ANFIS models become a subject to an examination. Combining different types of models (ANFIS and HMM) can prevent misunderstanding by the system for identification of user intention in dialogue system. Based on these models, the hybrid system may be employed in various language and call routing domains due to nonusage of lexical or syntactic analysis in classification process.

Recognition of degraded handwritten digits using dynamic Bayesian networks

NASA Astrophysics Data System (ADS)

Likforman-Sulem, Laurence; Sigelle, Marc

2007-01-01

We investigate in this paper the application of dynamic Bayesian networks (DBNs) to the recognition of handwritten digits. The main idea is to couple two separate HMMs into various architectures. First, a vertical HMM and a horizontal HMM are built observing the evolving streams of image columns and image rows respectively. Then, two coupled architectures are proposed to model interactions between these two streams and to capture the 2D nature of character images. Experiments performed on the MNIST handwritten digit database show that coupled architectures yield better recognition performances than non-coupled ones. Additional experiments conducted on artificially degraded (broken) characters demonstrate that coupled architectures better cope with such degradation than non coupled ones and than discriminative methods such as SVMs.

Detection of Splice Sites Using Support Vector Machine

NASA Astrophysics Data System (ADS)

Varadwaj, Pritish; Purohit, Neetesh; Arora, Bhumika

Automatic identification and annotation of exon and intron region of gene, from DNA sequences has been an important research area in field of computational biology. Several approaches viz. Hidden Markov Model (HMM), Artificial Intelligence (AI) based machine learning and Digital Signal Processing (DSP) techniques have extensively and independently been used by various researchers to cater this challenging task. In this work, we propose a Support Vector Machine based kernel learning approach for detection of splice sites (the exon-intron boundary) in a gene. Electron-Ion Interaction Potential (EIIP) values of nucleotides have been used for mapping character sequences to corresponding numeric sequences. Radial Basis Function (RBF) SVM kernel is trained using EIIP numeric sequences. Furthermore this was tested on test gene dataset for detection of splice site by window (of 12 residues) shifting. Optimum values of window size, various important parameters of SVM kernel have been optimized for a better accuracy. Receiver Operating Characteristic (ROC) curves have been utilized for displaying the sensitivity rate of the classifier and results showed 94.82% accuracy for splice site detection on test dataset.

Semantic Indexing of Multimedia Content Using Visual, Audio, and Text Cues

NASA Astrophysics Data System (ADS)

Adams, W. H.; Iyengar, Giridharan; Lin, Ching-Yung; Naphade, Milind Ramesh; Neti, Chalapathy; Nock, Harriet J.; Smith, John R.

2003-12-01

We present a learning-based approach to the semantic indexing of multimedia content using cues derived from audio, visual, and text features. We approach the problem by developing a set of statistical models for a predefined lexicon. Novel concepts are then mapped in terms of the concepts in the lexicon. To achieve robust detection of concepts, we exploit features from multiple modalities, namely, audio, video, and text. Concept representations are modeled using Gaussian mixture models (GMM), hidden Markov models (HMM), and support vector machines (SVM). Models such as Bayesian networks and SVMs are used in a late-fusion approach to model concepts that are not explicitly modeled in terms of features. Our experiments indicate promise in the proposed classification and fusion methodologies: our proposed fusion scheme achieves more than 10% relative improvement over the best unimodal concept detector.

Fitting hidden Markov models of protein domains to a target species: application to Plasmodium falciparum

PubMed Central

2012-01-01

Background Hidden Markov Models (HMMs) are a powerful tool for protein domain identification. The Pfam database notably provides a large collection of HMMs which are widely used for the annotation of proteins in new sequenced organisms. In Pfam, each domain family is represented by a curated multiple sequence alignment from which a profile HMM is built. In spite of their high specificity, HMMs may lack sensitivity when searching for domains in divergent organisms. This is particularly the case for species with a biased amino-acid composition, such as P. falciparum, the main causal agent of human malaria. In this context, fitting HMMs to the specificities of the target proteome can help identify additional domains. Results Using P. falciparum as an example, we compare approaches that have been proposed for this problem, and present two alternative methods. Because previous attempts strongly rely on known domain occurrences in the target species or its close relatives, they mainly improve the detection of domains which belong to already identified families. Our methods learn global correction rules that adjust amino-acid distributions associated with the match states of HMMs. These rules are applied to all match states of the whole HMM library, thus enabling the detection of domains from previously absent families. Additionally, we propose a procedure to estimate the proportion of false positives among the newly discovered domains. Starting with the Pfam standard library, we build several new libraries with the different HMM-fitting approaches. These libraries are first used to detect new domain occurrences with low E-values. Second, by applying the Co-Occurrence Domain Discovery (CODD) procedure we have recently proposed, the libraries are further used to identify likely occurrences among potential domains with higher E-values. Conclusion We show that the new approaches allow identification of several domain families previously absent in the P. falciparum proteome and the Apicomplexa phylum, and identify many domains that are not detected by previous approaches. In terms of the number of new discovered domains, the new approaches outperform the previous ones when no close species are available or when they are used to identify likely occurrences among potential domains with high E-values. All predictions on P. falciparum have been integrated into a dedicated website which pools all known/new annotations of protein domains and functions for this organism. A software implementing the two proposed approaches is available at the same address: http://www.lirmm.fr/∼terrapon/HMMfit/ PMID:22548871

Fitting hidden Markov models of protein domains to a target species: application to Plasmodium falciparum.

PubMed

Terrapon, Nicolas; Gascuel, Olivier; Maréchal, Eric; Bréhélin, Laurent

2012-05-01

Hidden Markov Models (HMMs) are a powerful tool for protein domain identification. The Pfam database notably provides a large collection of HMMs which are widely used for the annotation of proteins in new sequenced organisms. In Pfam, each domain family is represented by a curated multiple sequence alignment from which a profile HMM is built. In spite of their high specificity, HMMs may lack sensitivity when searching for domains in divergent organisms. This is particularly the case for species with a biased amino-acid composition, such as P. falciparum, the main causal agent of human malaria. In this context, fitting HMMs to the specificities of the target proteome can help identify additional domains. Using P. falciparum as an example, we compare approaches that have been proposed for this problem, and present two alternative methods. Because previous attempts strongly rely on known domain occurrences in the target species or its close relatives, they mainly improve the detection of domains which belong to already identified families. Our methods learn global correction rules that adjust amino-acid distributions associated with the match states of HMMs. These rules are applied to all match states of the whole HMM library, thus enabling the detection of domains from previously absent families. Additionally, we propose a procedure to estimate the proportion of false positives among the newly discovered domains. Starting with the Pfam standard library, we build several new libraries with the different HMM-fitting approaches. These libraries are first used to detect new domain occurrences with low E-values. Second, by applying the Co-Occurrence Domain Discovery (CODD) procedure we have recently proposed, the libraries are further used to identify likely occurrences among potential domains with higher E-values. We show that the new approaches allow identification of several domain families previously absent in the P. falciparum proteome and the Apicomplexa phylum, and identify many domains that are not detected by previous approaches. In terms of the number of new discovered domains, the new approaches outperform the previous ones when no close species are available or when they are used to identify likely occurrences among potential domains with high E-values. All predictions on P. falciparum have been integrated into a dedicated website which pools all known/new annotations of protein domains and functions for this organism. A software implementing the two proposed approaches is available at the same address: http://www.lirmm.fr/~terrapon/HMMfit/

Detecting seismic waves using a binary hidden Markov model classifier

NASA Astrophysics Data System (ADS)

Ray, J.; Lefantzi, S.; Brogan, R. A.; Forrest, R.; Hansen, C. W.; Young, C. J.

2016-12-01

We explore the use of Hidden Markov Models (HMM) to detect the arrival of seismic waves using data captured by a seismogram. HMMs define the state of a station as a binary variable based on whether the station is receiving a signal or not. HMMs are simple and fast, allowing them to monitor multiple datastreams arising from a large distributed network of seismographs. In this study we examine the efficacy of HMM-based detectors with respect to their false positive and negative rates as well as the accuracy of the signal onset time as compared to the value determined by an expert analyst. The study uses 3 component International Monitoring System (IMS) data from a carefully analyzed 2 week period from May, 2010, for which our analyst tried to identify every signal. Part of this interval is used for training the HMM to recognize the transition between state from noise to signal, while the other is used for evaluating the effectiveness of our new detection algorithm. We compare our results with the STA/LTA detection processing applied by the IDC to assess potential for operational use. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000.

Muxstep: an open-source C ++ multiplex HMM library for making inferences on multiple data types.

PubMed

Veličković, Petar; Liò, Pietro

2016-08-15

With the development of experimental methods and technology, we are able to reliably gain access to data in larger quantities, dimensions and types. This has great potential for the improvement of machine learning (as the learning algorithms have access to a larger space of information). However, conventional machine learning approaches used thus far on single-dimensional data inputs are unlikely to be expressive enough to accurately model the problem in higher dimensions; in fact, it should generally be most suitable to represent our underlying models as some form of complex networksng;nsio with nontrivial topological features. As the first step in establishing such a trend, we present MUXSTEP: , an open-source library utilising multiplex networks for the purposes of binary classification on multiple data types. The library is designed to be used out-of-the-box for developing models based on the multiplex network framework, as well as easily modifiable to suit problem modelling needs that may differ significantly from the default approach described. The full source code is available on GitHub: https://github.com/PetarV-/muxstep petar.velickovic@cl.cam.ac.uk Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Nonintrusive Load Monitoring Based on Advanced Deep Learning and Novel Signature.

PubMed

Kim, Jihyun; Le, Thi-Thu-Huong; Kim, Howon

2017-01-01

Monitoring electricity consumption in the home is an important way to help reduce energy usage. Nonintrusive Load Monitoring (NILM) is existing technique which helps us monitor electricity consumption effectively and costly. NILM is a promising approach to obtain estimates of the electrical power consumption of individual appliances from aggregate measurements of voltage and/or current in the distribution system. Among the previous studies, Hidden Markov Model (HMM) based models have been studied very much. However, increasing appliances, multistate of appliances, and similar power consumption of appliances are three big issues in NILM recently. In this paper, we address these problems through providing our contributions as follows. First, we proposed state-of-the-art energy disaggregation based on Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) model and additional advanced deep learning. Second, we proposed a novel signature to improve classification performance of the proposed model in multistate appliance case. We applied the proposed model on two datasets such as UK-DALE and REDD. Via our experimental results, we have confirmed that our model outperforms the advanced model. Thus, we show that our combination between advanced deep learning and novel signature can be a robust solution to overcome NILM's issues and improve the performance of load identification.

Learning Cellular Sorting Pathways Using Protein Interactions and Sequence Motifs

PubMed Central

Lin, Tien-Ho; Bar-Joseph, Ziv

2011-01-01

Abstract Proper subcellular localization is critical for proteins to perform their roles in cellular functions. Proteins are transported by different cellular sorting pathways, some of which take a protein through several intermediate locations until reaching its final destination. The pathway a protein is transported through is determined by carrier proteins that bind to specific sequence motifs. In this article, we present a new method that integrates protein interaction and sequence motif data to model how proteins are sorted through these sorting pathways. We use a hidden Markov model (HMM) to represent protein sorting pathways. The model is able to determine intermediate sorting states and to assign carrier proteins and motifs to the sorting pathways. In simulation studies, we show that the method can accurately recover an underlying sorting model. Using data for yeast, we show that our model leads to accurate prediction of subcellular localization. We also show that the pathways learned by our model recover many known sorting pathways and correctly assign proteins to the path they utilize. The learned model identified new pathways and their putative carriers and motifs and these may represent novel protein sorting mechanisms. Supplementary results and software implementation are available from http://murphylab.web.cmu.edu/software/2010_RECOMB_pathways/. PMID:21999284

Nonintrusive Load Monitoring Based on Advanced Deep Learning and Novel Signature

PubMed Central

Le, Thi-Thu-Huong; Kim, Howon

2017-01-01

Monitoring electricity consumption in the home is an important way to help reduce energy usage. Nonintrusive Load Monitoring (NILM) is existing technique which helps us monitor electricity consumption effectively and costly. NILM is a promising approach to obtain estimates of the electrical power consumption of individual appliances from aggregate measurements of voltage and/or current in the distribution system. Among the previous studies, Hidden Markov Model (HMM) based models have been studied very much. However, increasing appliances, multistate of appliances, and similar power consumption of appliances are three big issues in NILM recently. In this paper, we address these problems through providing our contributions as follows. First, we proposed state-of-the-art energy disaggregation based on Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) model and additional advanced deep learning. Second, we proposed a novel signature to improve classification performance of the proposed model in multistate appliance case. We applied the proposed model on two datasets such as UK-DALE and REDD. Via our experimental results, we have confirmed that our model outperforms the advanced model. Thus, we show that our combination between advanced deep learning and novel signature can be a robust solution to overcome NILM's issues and improve the performance of load identification. PMID:29118809

Evaluating High-Throughput Ab Initio Gene Finders to Discover Proteins Encoded in Eukaryotic Pathogen Genomes Missed by Laboratory Techniques

PubMed Central

Goodswen, Stephen J.; Kennedy, Paul J.; Ellis, John T.

2012-01-01

Next generation sequencing technology is advancing genome sequencing at an unprecedented level. By unravelling the code within a pathogen’s genome, every possible protein (prior to post-translational modifications) can theoretically be discovered, irrespective of life cycle stages and environmental stimuli. Now more than ever there is a great need for high-throughput ab initio gene finding. Ab initio gene finders use statistical models to predict genes and their exon-intron structures from the genome sequence alone. This paper evaluates whether existing ab initio gene finders can effectively predict genes to deduce proteins that have presently missed capture by laboratory techniques. An aim here is to identify possible patterns of prediction inaccuracies for gene finders as a whole irrespective of the target pathogen. All currently available ab initio gene finders are considered in the evaluation but only four fulfil high-throughput capability: AUGUSTUS, GeneMark_hmm, GlimmerHMM, and SNAP. These gene finders require training data specific to a target pathogen and consequently the evaluation results are inextricably linked to the availability and quality of the data. The pathogen, Toxoplasma gondii, is used to illustrate the evaluation methods. The results support current opinion that predicted exons by ab initio gene finders are inaccurate in the absence of experimental evidence. However, the results reveal some patterns of inaccuracy that are common to all gene finders and these inaccuracies may provide a focus area for future gene finder developers. PMID:23226328

Principle component analysis to separate deformation signals from multiple sources during a 2015 intrusive sequence at Kīlauea Volcano

NASA Astrophysics Data System (ADS)

Johanson, I. A.; Miklius, A.; Poland, M. P.

2016-12-01

A sequence of magmatic events in April-May 2015 at Kīlauea Volcano produced a complex deformation pattern that can be described by multiple deforming sources, active simultaneously. The 2015 intrusive sequence began with inflation in the volcano's summit caldera near Halema`uma`u (HMM) Crater, which continued over a few weeks, followed by rapid deflation of the HMM source and inflation of a source in the south caldera region during the next few days. In Kīlauea Volcano's summit area, multiple deformation centers are active at varying times, and all contribute to the overall pattern observed with GPS, tiltmeters, and InSAR. Isolating the contribution of different signals related to each source is a challenge and complicates the determination of optimal source geometry for the underlying magma bodies. We used principle component analysis of continuous GPS time series from the 2015 intrusion sequence to determine three basis vectors which together account for 83% of the variance in the data set. The three basis vectors are non-orthogonal and not strictly the principle components of the data set. In addition to separating deformation sources in the continuous GPS data, the basis vectors provide a means to scale the contribution of each source in a given interferogram. This provides an additional constraint in a joint model of GPS and InSAR data (COSMO-SkyMed and Sentinel-1A) to determine source geometry. The first basis vector corresponds with inflation in the south caldera region, an area long recognized as the location of a long-term storage reservoir. The second vector represents deformation of the HMM source, which is in the same location as a previously modeled shallow reservoir, however InSAR data suggest a more complicated source. Preliminary modeling of the deformation attributed to the third basis vector shows that it is consistent with inflation of a steeply dipping ellipsoid centered below Keanakāko`i crater, southeast of HMM. Keanakāko`i crater is the locus of a known, intermittently active deformation source, which was not previously recognized to have been active during the 2015 event.

Accelerometry-based classification of human activities using Markov modeling.

PubMed

Mannini, Andrea; Sabatini, Angelo Maria

2011-01-01

Accelerometers are a popular choice as body-motion sensors: the reason is partly in their capability of extracting information that is useful for automatically inferring the physical activity in which the human subject is involved, beside their role in feeding biomechanical parameters estimators. Automatic classification of human physical activities is highly attractive for pervasive computing systems, whereas contextual awareness may ease the human-machine interaction, and in biomedicine, whereas wearable sensor systems are proposed for long-term monitoring. This paper is concerned with the machine learning algorithms needed to perform the classification task. Hidden Markov Model (HMM) classifiers are studied by contrasting them with Gaussian Mixture Model (GMM) classifiers. HMMs incorporate the statistical information available on movement dynamics into the classification process, without discarding the time history of previous outcomes as GMMs do. An example of the benefits of the obtained statistical leverage is illustrated and discussed by analyzing two datasets of accelerometer time series.

Two-dimensional hidden semantic information model for target saliency detection and eyetracking identification

NASA Astrophysics Data System (ADS)

Wan, Weibing; Yuan, Lingfeng; Zhao, Qunfei; Fang, Tao

2018-01-01

Saliency detection has been applied to the target acquisition case. This paper proposes a two-dimensional hidden Markov model (2D-HMM) that exploits the hidden semantic information of an image to detect its salient regions. A spatial pyramid histogram of oriented gradient descriptors is used to extract features. After encoding the image by a learned dictionary, the 2D-Viterbi algorithm is applied to infer the saliency map. This model can predict fixation of the targets and further creates robust and effective depictions of the targets' change in posture and viewpoint. To validate the model with a human visual search mechanism, two eyetrack experiments are employed to train our model directly from eye movement data. The results show that our model achieves better performance than visual attention. Moreover, it indicates the plausibility of utilizing visual track data to identify targets.

Human activity recognition based on feature selection in smart home using back-propagation algorithm.

PubMed

Fang, Hongqing; He, Lei; Si, Hao; Liu, Peng; Xie, Xiaolei

2014-09-01

In this paper, Back-propagation(BP) algorithm has been used to train the feed forward neural network for human activity recognition in smart home environments, and inter-class distance method for feature selection of observed motion sensor events is discussed and tested. And then, the human activity recognition performances of neural network using BP algorithm have been evaluated and compared with other probabilistic algorithms: Naïve Bayes(NB) classifier and Hidden Markov Model(HMM). The results show that different feature datasets yield different activity recognition accuracy. The selection of unsuitable feature datasets increases the computational complexity and degrades the activity recognition accuracy. Furthermore, neural network using BP algorithm has relatively better human activity recognition performances than NB classifier and HMM. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Stylistic gait synthesis based on hidden Markov models

NASA Astrophysics Data System (ADS)

Tilmanne, Joëlle; Moinet, Alexis; Dutoit, Thierry

2012-12-01

In this work we present an expressive gait synthesis system based on hidden Markov models (HMMs), following and modifying a procedure originally developed for speaking style adaptation, in speech synthesis. A large database of neutral motion capture walk sequences was used to train an HMM of average walk. The model was then used for automatic adaptation to a particular style of walk using only a small amount of training data from the target style. The open source toolkit that we adapted for motion modeling also enabled us to take into account the dynamics of the data and to model accurately the duration of each HMM state. We also address the assessment issue and propose a procedure for qualitative user evaluation of the synthesized sequences. Our tests show that the style of these sequences can easily be recognized and look natural to the evaluators.

Hidden Markov model tracking of continuous gravitational waves from a binary neutron star with wandering spin. II. Binary orbital phase tracking

NASA Astrophysics Data System (ADS)

Suvorova, S.; Clearwater, P.; Melatos, A.; Sun, L.; Moran, W.; Evans, R. J.

2017-11-01

A hidden Markov model (HMM) scheme for tracking continuous-wave gravitational radiation from neutron stars in low-mass x-ray binaries (LMXBs) with wandering spin is extended by introducing a frequency-domain matched filter, called the J -statistic, which sums the signal power in orbital sidebands coherently. The J -statistic is similar but not identical to the binary-modulated F -statistic computed by demodulation or resampling. By injecting synthetic LMXB signals into Gaussian noise characteristic of the Advanced Laser Interferometer Gravitational-wave Observatory (Advanced LIGO), it is shown that the J -statistic HMM tracker detects signals with characteristic wave strain h0≥2 ×10-26 in 370 d of data from two interferometers, divided into 37 coherent blocks of equal length. When applied to data from Stage I of the Scorpius X-1 Mock Data Challenge organized by the LIGO Scientific Collaboration, the tracker detects all 50 closed injections (h0≥6.84 ×10-26), recovering the frequency with a root-mean-square accuracy of ≤1.95 ×10-5 Hz . Of the 50 injections, 43 (with h0≥1.09 ×10-25) are detected in a single, coherent 10 d block of data. The tracker employs an efficient, recursive HMM solver based on the Viterbi algorithm, which requires ˜105 CPU-hours for a typical broadband (0.5 kHz) LMXB search.

A general Bayesian framework for calibrating and evaluating stochastic models of annual multi-site hydrological data

NASA Astrophysics Data System (ADS)

Frost, Andrew J.; Thyer, Mark A.; Srikanthan, R.; Kuczera, George

2007-07-01

SummaryMulti-site simulation of hydrological data are required for drought risk assessment of large multi-reservoir water supply systems. In this paper, a general Bayesian framework is presented for the calibration and evaluation of multi-site hydrological data at annual timescales. Models included within this framework are the hidden Markov model (HMM) and the widely used lag-1 autoregressive (AR(1)) model. These models are extended by the inclusion of a Box-Cox transformation and a spatial correlation function in a multi-site setting. Parameter uncertainty is evaluated using Markov chain Monte Carlo techniques. Models are evaluated by their ability to reproduce a range of important extreme statistics and compared using Bayesian model selection techniques which evaluate model probabilities. The case study, using multi-site annual rainfall data situated within catchments which contribute to Sydney's main water supply, provided the following results: Firstly, in terms of model probabilities and diagnostics, the inclusion of the Box-Cox transformation was preferred. Secondly the AR(1) and HMM performed similarly, while some other proposed AR(1)/HMM models with regionally pooled parameters had greater posterior probability than these two models. The practical significance of parameter and model uncertainty was illustrated using a case study involving drought security analysis for urban water supply. It was shown that ignoring parameter uncertainty resulted in a significant overestimate of reservoir yield and an underestimation of system vulnerability to severe drought.

Hidden Markov models in automatic speech recognition

NASA Astrophysics Data System (ADS)

Wrzoskowicz, Adam

1993-11-01

This article describes a method for constructing an automatic speech recognition system based on hidden Markov models (HMMs). The author discusses the basic concepts of HMM theory and the application of these models to the analysis and recognition of speech signals. The author provides algorithms which make it possible to train the ASR system and recognize signals on the basis of distinct stochastic models of selected speech sound classes. The author describes the specific components of the system and the procedures used to model and recognize speech. The author discusses problems associated with the choice of optimal signal detection and parameterization characteristics and their effect on the performance of the system. The author presents different options for the choice of speech signal segments and their consequences for the ASR process. The author gives special attention to the use of lexical, syntactic, and semantic information for the purpose of improving the quality and efficiency of the system. The author also describes an ASR system developed by the Speech Acoustics Laboratory of the IBPT PAS. The author discusses the results of experiments on the effect of noise on the performance of the ASR system and describes methods of constructing HMM's designed to operate in a noisy environment. The author also describes a language for human-robot communications which was defined as a complex multilevel network from an HMM model of speech sounds geared towards Polish inflections. The author also added mandatory lexical and syntactic rules to the system for its communications vocabulary.

The cellular source for APOBEC3G's incorporation into HIV-1

PubMed Central

2011-01-01

Background Human APOBEC3G (hA3G) has been identified as a cellular inhibitor of HIV-1 infectivity. Viral incorporation of hA3G is an essential step for its antiviral activity. Although the mechanism underlying hA3G virion encapsidation has been investigated extensively, the cellular source of viral hA3G remains unclear. Results Previous studies have shown that hA3G forms low-molecular-mass (LMM) and high-molecular-mass (HMM) complexes. Our work herein provides evidence that the majority of newly-synthesized hA3G interacts with membrane lipid raft domains to form Lipid raft-associated hA3G (RA hA3G), which serve as the precursor of the mature HMM hA3G complex, while a minority of newly-synthesized hA3G remains in the cytoplasm as a soluble LMM form. The distribution of hA3G among the soluble LMM form, the RA LMM form and the mature forms of HMM is regulated by a mechanism involving the N-terminal part of the linker region and the C-terminus of hA3G. Mutagenesis studies reveal a direct correlation between the ability of hA3G to form the RA LMM complex and its viral incorporation. Conclusions Together these data suggest that the Lipid raft-associated LMM A3G complex functions as the cellular source of viral hA3G. PMID:21211018

Satellite measurements of SO2 emission and dispersion during the 2008-2009 eruption of Halema‘uma‘u, Kilauea

NASA Astrophysics Data System (ADS)

Carn, S. A.; Sutton, A. J.; Elias, T.; Patrick, M. R.; Owen, R. C.; Wu, S.

2009-12-01

Satellite remote sensing is providing unique constraints on sulfur dioxide (SO2) emissions associated with the ongoing eruption of Halema‘uma‘u (HMM), and daily observations of volcanic plume dispersion. We use synoptic SO2 measurements by the Ozone Monitoring Instrument (OMI) on NASA’s Aura satellite to chart the fluctuation in SO2 emissions and plume dispersion. Prior to the onset of degassing from HMM, OMI detected SO2 emissions from the east rift Pu‘u ‘O‘o vent; the average daily SO2 burden measured between Sept 6, 2004 and Feb 29, 2008 was 0.7 kilotons (kt) ±1 (1σ). The additional SO2 production from HMM caused total SO2 burdens in the composite Kilauea plume to increase notably in March-April 2008, and a daily average SO2 burden of ~4 kt ±4 (1σ) was measured by OMI between Mar 1, 2008 and Jul 31, 2009 (all burdens are preliminary and assume a SO2 plume altitude of 3 km). A total of ~2 Megatons of SO2 was measured by OMI in the Kilauea emissions between March 2008 and July 2009. The increased SO2 emissions provide an excellent opportunity to compare ground-based ultraviolet (UV) spectrometer and space-based UV OMI measurements of SO2 output, and test algorithms for derivation of emission rates from satellite data. Kilauea data analyzed to date show that trends in ground-based SO2 emission rates and OMI SO2 burdens are in qualitative agreement but differ in magnitude. Plume altitude is a critical factor in satellite SO2 retrievals, and interpretation of the Kilauea observations is complicated by the presence of two SO2 plumes (from HMM and Pu‘u ‘O‘o) within the OMI field-of-view. In order to constrain plume heights and SO2 lifetimes, we use plume simulations generated by the FLEXPART particle dispersion model and compare the model output with OMI SO2 observations. We validate the model-generated plume altitudes using vertical aerosol profiles derived from the CALIPSO space-borne lidar instrument. Gaussian plume models parameterized using visual observations of the HMM plume injection height further constrain near-source plume dispersion and downwind evolution. Refinement of SO2 altitude provides improved constraints on SO2 burdens in observed plumes. A more rigorous approach to deriving source emission strengths from satellite observations is an inverse modeling scheme incorporating measurements and models. Using Kilauea as a case study, we plan to develop such a scheme using OMI data, FLEXPART simulations and atmospheric chemistry and transport modeling using the GEOS-Chem model. Modeling of plume dispersion and chemistry will also provide estimates of SO2 and acid aerosol concentrations for potential use in air quality and health hazard assessments in Hawaii.

Passive Acoustic Leak Detection for Sodium Cooled Fast Reactors Using Hidden Markov Models

NASA Astrophysics Data System (ADS)

Marklund, A. Riber; Kishore, S.; Prakash, V.; Rajan, K. K.; Michel, F.

2016-06-01

Acoustic leak detection for steam generators of sodium fast reactors have been an active research topic since the early 1970s and several methods have been tested over the years. Inspired by its success in the field of automatic speech recognition, we here apply hidden Markov models (HMM) in combination with Gaussian mixture models (GMM) to the problem. To achieve this, we propose a new feature calculation scheme, based on the temporal evolution of the power spectral density (PSD) of the signal. Using acoustic signals recorded during steam/water injection experiments done at the Indira Gandhi Centre for Atomic Research (IGCAR), the proposed method is tested. We perform parametric studies on the HMM+GMM model size and demonstrate that the proposed method a) performs well without a priori knowledge of injection noise, b) can incorporate several noise models and c) has an output distribution that simplifies false alarm rate control.

Time-lapse microscopy and image processing for stem cell research: modeling cell migration

NASA Astrophysics Data System (ADS)

Gustavsson, Tomas; Althoff, Karin; Degerman, Johan; Olsson, Torsten; Thoreson, Ann-Catrin; Thorlin, Thorleif; Eriksson, Peter

2003-05-01

This paper presents hardware and software procedures for automated cell tracking and migration modeling. A time-lapse microscopy system equipped with a computer controllable motorized stage was developed. The performance of this stage was improved by incorporating software algorithms for stage motion displacement compensation and auto focus. The microscope is suitable for in-vitro stem cell studies and allows for multiple cell culture image sequence acquisition. This enables comparative studies concerning rate of cell splits, average cell motion velocity, cell motion as a function of cell sample density and many more. Several cell segmentation procedures are described as well as a cell tracking algorithm. Statistical methods for describing cell migration patterns are presented. In particular, the Hidden Markov Model (HMM) was investigated. Results indicate that if the cell motion can be described as a non-stationary stochastic process, then the HMM can adequately model aspects of its dynamic behavior.

Integrating hidden Markov model and PRAAT: a toolbox for robust automatic speech transcription

NASA Astrophysics Data System (ADS)

Kabir, A.; Barker, J.; Giurgiu, M.

2010-09-01

An automatic time-aligned phone transcription toolbox of English speech corpora has been developed. Especially the toolbox would be very useful to generate robust automatic transcription and able to produce phone level transcription using speaker independent models as well as speaker dependent models without manual intervention. The system is based on standard Hidden Markov Models (HMM) approach and it was successfully experimented over a large audiovisual speech corpus namely GRID corpus. One of the most powerful features of the toolbox is the increased flexibility in speech processing where the speech community would be able to import the automatic transcription generated by HMM Toolkit (HTK) into a popular transcription software, PRAAT, and vice-versa. The toolbox has been evaluated through statistical analysis on GRID data which shows that automatic transcription deviates by an average of 20 ms with respect to manual transcription.

Ultra-wideband microwave absorber by connecting multiple absorption bands of two different-sized hyperbolic metamaterial waveguide arrays.

PubMed

Yin, Xiang; Long, Chang; Li, Junhao; Zhu, Hua; Chen, Lin; Guan, Jianguo; Li, Xun

2015-10-19

Microwave absorbers have important applications in various areas including stealth, camouflage, and antenna. Here, we have designed an ultra-broadband light absorber by integrating two different-sized tapered hyperbolic metamaterial (HMM) waveguides, each of which has wide but different absorption bands due to broadband slow-light response, into a unit cell. Both the numerical and experimental results demonstrate that in such a design strategy, the low absorption bands between high absorption bands with a single-sized tapered HMM waveguide array can be effectively eliminated, resulting in a largely expanded absorption bandwidth ranging from 2.3 to 40 GHz. The presented ultra-broadband light absorber is also insensitive to polarization and robust against incident angle. Our results offer a further step in developing practical artificial electromagnetic absorbers, which will impact a broad range of applications at microwave frequencies.

Understanding eye movements in face recognition using hidden Markov models.

PubMed

Chuk, Tim; Chan, Antoni B; Hsiao, Janet H

2014-09-16

We use a hidden Markov model (HMM) based approach to analyze eye movement data in face recognition. HMMs are statistical models that are specialized in handling time-series data. We conducted a face recognition task with Asian participants, and model each participant's eye movement pattern with an HMM, which summarized the participant's scan paths in face recognition with both regions of interest and the transition probabilities among them. By clustering these HMMs, we showed that participants' eye movements could be categorized into holistic or analytic patterns, demonstrating significant individual differences even within the same culture. Participants with the analytic pattern had longer response times, but did not differ significantly in recognition accuracy from those with the holistic pattern. We also found that correct and wrong recognitions were associated with distinctive eye movement patterns; the difference between the two patterns lies in the transitions rather than locations of the fixations alone. © 2014 ARVO.

A Hybrid of Deep Network and Hidden Markov Model for MCI Identification with Resting-State fMRI.

PubMed

Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

2015-10-01

In this paper, we propose a novel method for modelling functional dynamics in resting-state fMRI (rs-fMRI) for Mild Cognitive Impairment (MCI) identification. Specifically, we devise a hybrid architecture by combining Deep Auto-Encoder (DAE) and Hidden Markov Model (HMM). The roles of DAE and HMM are, respectively, to discover hierarchical non-linear relations among features, by which we transform the original features into a lower dimension space, and to model dynamic characteristics inherent in rs-fMRI, i.e. , internal state changes. By building a generative model with HMMs for each class individually, we estimate the data likelihood of a test subject as MCI or normal healthy control, based on which we identify the clinical label. In our experiments, we achieved the maximal accuracy of 81.08% with the proposed method, outperforming state-of-the-art methods in the literature.

Dynamic and Contextual Information in HMM Modeling for Handwritten Word Recognition.

PubMed

Bianne-Bernard, Anne-Laure; Menasri, Farès; Al-Hajj Mohamad, Rami; Mokbel, Chafic; Kermorvant, Christopher; Likforman-Sulem, Laurence

2011-10-01

This study aims at building an efficient word recognition system resulting from the combination of three handwriting recognizers. The main component of this combined system is an HMM-based recognizer which considers dynamic and contextual information for a better modeling of writing units. For modeling the contextual units, a state-tying process based on decision tree clustering is introduced. Decision trees are built according to a set of expert-based questions on how characters are written. Questions are divided into global questions, yielding larger clusters, and precise questions, yielding smaller ones. Such clustering enables us to reduce the total number of models and Gaussians densities by 10. We then apply this modeling to the recognition of handwritten words. Experiments are conducted on three publicly available databases based on Latin or Arabic languages: Rimes, IAM, and OpenHart. The results obtained show that contextual information embedded with dynamic modeling significantly improves recognition.

A Hybrid of Deep Network and Hidden Markov Model for MCI Identification with Resting-State fMRI

PubMed Central

Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

2015-01-01

In this paper, we propose a novel method for modelling functional dynamics in resting-state fMRI (rs-fMRI) for Mild Cognitive Impairment (MCI) identification. Specifically, we devise a hybrid architecture by combining Deep Auto-Encoder (DAE) and Hidden Markov Model (HMM). The roles of DAE and HMM are, respectively, to discover hierarchical non-linear relations among features, by which we transform the original features into a lower dimension space, and to model dynamic characteristics inherent in rs-fMRI, i.e., internal state changes. By building a generative model with HMMs for each class individually, we estimate the data likelihood of a test subject as MCI or normal healthy control, based on which we identify the clinical label. In our experiments, we achieved the maximal accuracy of 81.08% with the proposed method, outperforming state-of-the-art methods in the literature. PMID:27054199

In vitro assays of molecular motors--impact of motor-surface interactions.

PubMed

Mansson, Alf; Balaz, Martina; Albet-Torres, Nuria; Rosengren, K Johan

2008-05-01

In many types of biophysical studies of both single molecules and ensembles of molecular motors the motors are adsorbed to artificial surfaces. Some of the most important assay systems of this type (in vitro motility assays and related single molecule techniques) will be briefly described together with an account of breakthroughs in the understanding of actomyosin function that have resulted from their use. A poorly characterized, but potentially important, entity in these studies is the mechanism of motor adsorption to surfaces and the effects of motor surface interactions on experimental results. A better understanding of these phenomena is also important for the development of commercially viable nanotechnological applications powered by molecular motors. Here, we will consider several aspects of motor surface interactions with a particular focus on heavy meromyosin (HMM) from skeletal muscle. These aspects will be related to heavy meromyosin structure and relevant parts of the vast literature on protein-surface interactions for non-motor proteins. An overview of methods for studying motor-surface interactions will also be given. The information is used as a basis for further development of a model for HMM-surface interactions and is discussed in relation to experiments where nanopatterning has been employed for in vitro reconstruction of actomyosin order. The challenges and potentials of this approach in biophysical studies, compared to the use of self-assembly of biological components into supramolecular protein aggregates (e.g. myosin filaments) will be considered. Finally, this review will consider the implications for further developments of motor-powered lab-on-a-chip devices.

Conserved structure and inferred evolutionary history of long terminal repeats (LTRs)

PubMed Central

2013-01-01

Background Long terminal repeats (LTRs, consisting of U3-R-U5 portions) are important elements of retroviruses and related retrotransposons. They are difficult to analyse due to their variability. The aim was to obtain a more comprehensive view of structure, diversity and phylogeny of LTRs than hitherto possible. Results Hidden Markov models (HMM) were created for 11 clades of LTRs belonging to Retroviridae (class III retroviruses), animal Metaviridae (Gypsy/Ty3) elements and plant Pseudoviridae (Copia/Ty1) elements, complementing our work with Orthoretrovirus HMMs. The great variation in LTR length of plant Metaviridae and the few divergent animal Pseudoviridae prevented building HMMs from both of these groups. Animal Metaviridae LTRs had the same conserved motifs as retroviral LTRs, confirming that the two groups are closely related. The conserved motifs were the short inverted repeats (SIRs), integrase recognition signals (5´TGTTRNR…YNYAACA 3´); the polyadenylation signal or AATAAA motif; a GT-rich stretch downstream of the polyadenylation signal; and a less conserved AT-rich stretch corresponding to the core promoter element, the TATA box. Plant Pseudoviridae LTRs differed slightly in having a conserved TATA-box, TATATA, but no conserved polyadenylation signal, plus a much shorter R region. The sensitivity of the HMMs for detection in genomic sequences was around 50% for most models, at a relatively high specificity, suitable for genome screening. The HMMs yielded consensus sequences, which were aligned by creating an HMM model (a ‘Superviterbi’ alignment). This yielded a phylogenetic tree that was compared with a Pol-based tree. Both LTR and Pol trees supported monophyly of retroviruses. In both, Pseudoviridae was ancestral to all other LTR retrotransposons. However, the LTR trees showed the chromovirus portion of Metaviridae clustering together with Pseudoviridae, dividing Metaviridae into two portions with distinct phylogeny. Conclusion The HMMs clearly demonstrated a unitary conserved structure of LTRs, supporting that they arose once during evolution. We attempted to follow the evolution of LTRs by tracing their functional foundations, that is, acquisition of RNAse H, a combined promoter/ polyadenylation site, integrase, hairpin priming and the primer binding site (PBS). Available information did not support a simple evolutionary chain of events. PMID:23369192

Hyperbolic metamaterial nanostructures to tune charge-transfer dynamics (Conference Presentation)

NASA Astrophysics Data System (ADS)

Lee, Kwang Jin; Xiao, Yiming; Woo, Jae Heun; Kim, Eun Sun; Kreher, David; Attias, André-Jean; Mathevet, Fabrice; Ribierre, Jean-Charles; Wu, Jeong Weon; André, Pascal

2016-09-01

Charge transfer (CT) is an essential phenomenon relevant to numerous fields including biology, physics and chemistry.1-5 Here, we demonstrate that multi-layered hyperbolic metamaterial (HMM) substrates alter organic semiconductor CT dynamics.6 With triphenylene:perylene diimide dyad supramolecular self-assemblies prepared on HMM substrates, we show that both charge separation (CS) and charge recombination (CR) characteristic times are increased by factors of 2.5 and 1.6, respectively, resulting in longer-lived CT states. We successfully rationalize the experimental data by extending Marcus theory framework with dipole image interactions tuning the driving force. The number of metal-dielectric pairs alters the HMM interfacial effective dielectric constant and becomes a solid analogue to solvent polarizability. Based on the experimental results and extended Marcus theory framework, we find that CS and CR processes are located in normal and inverted regions on Marcus parabola diagram, respectively. The model and further PH3T:PCBM data show that the phenomenon is general and that molecular and substrate engineering offer a wide range of kinetic tailoring opportunities. This work opens the path toward novel artificial substrates designed to control CT dynamics with potential applications in fields including optoelectronics, organic solar cells and chemistry. 1. Marcus, Rev. Mod. Phys., 1993, 65, 599. 2. Marcus, Phys. Chem. Chem. Phys., 2012, 14, 13729. 3. Lambert, et al., Nat. Phys., 2012, 9, 10. 4. C. Clavero, Nat. Photon., 2014, 8, 95. 5. A. Canaguier-Durand, et al., Angew. Chem. Int. Ed., 2013, 52, 10533. 6. K. J. Lee, et al., Submitted, 2015, arxiv.org/abs/1510.08574.

Supporting formal education to improve quality of health care provided by mothers of children with malaria in rural western Kenya.

PubMed

Kakai, Rose; Menya, Diana; Odero, Wilson

2009-08-30

Home management of malaria (HMM) has been shown to be an effective strategy for reducing childhood mortality from malaria. The direct and especially indirect costs of seeking health care from formal facilities may be substantial, providing a major barrier for many households. Further evaluations of HMM and community-based utilization of available options will help to optimize treatment strategies and maximize health benefits. The purpose of this study was to determine the effect of education, occupation, and family income on the choice of health care options for malaria. This was a cross-sectional, community-based study conducted between November 2007 and December 2007, using quantitative data collection methods. Mothers of children aged younger than five years were interviewed using a questionnaire to elicit responses on the mothers' level of education, occupation, income and malaria health care options. A total of 240 mothers of children aged younger than 5 years were interviewed between November and December, 2007. There was a direct relationship between formal education and occupation. The mean monthly family income was highest among those employed (KSh. 14,421) followed by businesswomen (KSh. 3,106) and farmers (KSh. 1,827) respectively (p<0.01). Those employed were more likely to take their ill children to a health facility (p = 0.05) or choose an antimalarial drug for home treatment. Supporting formal education may scale up the income of family health care providers and improve the quality of HMM among children living in rural communities.

Implementation of a health management mentoring program: year-1 evaluation of its impact on health system strengthening in Zambézia Province, Mozambique.

PubMed

Edwards, Laura J; Moisés, Abú; Nzaramba, Mathias; Cassimo, Aboobacar; Silva, Laura; Mauricio, Joaquim; Wester, C William; Vermund, Sten H; Moon, Troy D

2015-03-12

Avante Zambézia is an initiative of a Non-Governmental Organization (NGO), Friends in Global Health, LLC (FGH) and the Vanderbilt Institute for Global Health (VIGH) to provide technical assistance to the Mozambican Ministry of Health (MoH) in rural Zambézia Province. Avante Zambézia developed a district level Health Management Mentorship (HMM) program to strengthen health systems in ten of Zambézia's 17 districts. Our objective was to preliminarily analyze changes in four domains of health system capacity after the HMM's first year: accounting, Human Resources (HRs), Monitoring and Evaluation (M&E), and transportation management. Quantitative metrics were developed in each domain. During district visits for weeklong, on-site mentoring, the health management mentoring teams documented each indicator as a success ratio percentage. We analyzed data using linear regressions of each indicator's mean success ratio across all districts submitting a report over time. Of the four domains, district performance in the accounting domain was the strongest and most sustained. Linear regressions of mean monthly compliance for HR objectives indicated improvement in three of six mean success ratios. The M&E capacity domain showed the least overall improvement. The one indicator analyzed for transportation management suggested progress. Our outcome evaluation demonstrates improvement in health system performance during a HMM initiative. Evaluating which elements of our mentoring program are succeeding in strengthening district level health systems is vital in preparing to transition fiscal and managerial responsibility to local authorities. © 2015 by Kerman University of Medical Sciences.

Implicit emotion regulation in adolescent girls: An exploratory investigation of Hidden Markov Modeling and its neural correlates.

PubMed

Steele, James S; Bush, Keith; Stowe, Zachary N; James, George A; Smitherman, Sonet; Kilts, Clint D; Cisler, Josh

2018-01-01

Numerous data demonstrate that distracting emotional stimuli cause behavioral slowing (i.e. emotional conflict) and that behavior dynamically adapts to such distractors. However, the cognitive and neural mechanisms that mediate these behavioral findings are poorly understood. Several theoretical models have been developed that attempt to explain these phenomena, but these models have not been directly tested on human behavior nor compared. A potential tool to overcome this limitation is Hidden Markov Modeling (HMM), which is a computational approach to modeling indirectly observed systems. Here, we administered an emotional Stroop task to a sample of healthy adolescent girls (N = 24) during fMRI and used HMM to implement theoretical behavioral models. We then compared the model fits and tested for neural representations of the hidden states of the most supported model. We found that a modified variant of the model posited by Mathews et al. (1998) was most concordant with observed behavior and that brain activity was related to the model-based hidden states. Particularly, while the valences of the stimuli themselves were encoded primarily in the ventral visual cortex, the model-based detection of threatening targets was associated with increased activity in the bilateral anterior insula, while task effort (i.e. adaptation) was associated with reduction in the activity of these areas. These findings suggest that emotional target detection and adaptation are accomplished partly through increases and decreases, respectively, in the perceived immediate relevance of threatening cues and also demonstrate the efficacy of using HMM to apply theoretical models to human behavior.

Implicit emotion regulation in adolescent girls: An exploratory investigation of Hidden Markov Modeling and its neural correlates

PubMed Central

Bush, Keith; Stowe, Zachary N.; James, George A.; Smitherman, Sonet; Kilts, Clint D.; Cisler, Josh

2018-01-01

Numerous data demonstrate that distracting emotional stimuli cause behavioral slowing (i.e. emotional conflict) and that behavior dynamically adapts to such distractors. However, the cognitive and neural mechanisms that mediate these behavioral findings are poorly understood. Several theoretical models have been developed that attempt to explain these phenomena, but these models have not been directly tested on human behavior nor compared. A potential tool to overcome this limitation is Hidden Markov Modeling (HMM), which is a computational approach to modeling indirectly observed systems. Here, we administered an emotional Stroop task to a sample of healthy adolescent girls (N = 24) during fMRI and used HMM to implement theoretical behavioral models. We then compared the model fits and tested for neural representations of the hidden states of the most supported model. We found that a modified variant of the model posited by Mathews et al. (1998) was most concordant with observed behavior and that brain activity was related to the model-based hidden states. Particularly, while the valences of the stimuli themselves were encoded primarily in the ventral visual cortex, the model-based detection of threatening targets was associated with increased activity in the bilateral anterior insula, while task effort (i.e. adaptation) was associated with reduction in the activity of these areas. These findings suggest that emotional target detection and adaptation are accomplished partly through increases and decreases, respectively, in the perceived immediate relevance of threatening cues and also demonstrate the efficacy of using HMM to apply theoretical models to human behavior. PMID:29489856

BGK-MD, Version 1.0

DOE Office of Scientific and Technical Information (OSTI.GOV)

Haack, Jeffrey; Shohet, Gil

2016-12-02

The software implements a heterogeneous multiscale method (HMM), which involves solving a classical molecular dynamics (MD) problem and then computes the entropy production in order to compute the relaxation times towards equilibrium for use in a Bhatnagar-Gross-Krook (BGK) solver.

Cholera toxin B subunit pentamer reassembled from Escherichia coli inclusion bodies for use in vaccination.

PubMed

Tamaki, Yukihiro; Harakuni, Tetsuya; Yamaguchi, Rui; Miyata, Takeshi; Arakawa, Takeshi

2016-03-04

The cholera toxin B subunit (CTB) is secreted in its pentameric form from Escherichia coli if its leader peptide is replaced with one of E. coli origin. However, the secretion of the pentamer is generally severely impaired when the molecule is mutated or fused to a foreign peptide. Therefore, we attempted to regenerate pentameric CTB from the inclusion bodies (IBs) of E. coli. Stepwise dialysis of the IBs solubilized in guanidine hydrochloride predominantly generated soluble high-molecular-mass (HMM) aggregates and only a small fraction of pentamer. Three methods to reassemble homogeneous pentameric molecules were evaluated: (i) using a pentameric coiled-coil fusion partner, expecting it to function as an assembly core; (ii) optimizing the protein concentration during refolding; and (iii) eliminating contaminants before refolding. Coiled-coil fusion had some effect, but substantial amounts of HMM aggregates were still generated. Varying the protein concentration from 0.05 mg/mL to 5mg/mL had almost no effect. In contrast, eliminating the contaminants before refolding had a robust effect, and only the pentamer was regenerated, with no detectable HMM aggregates. Surprisingly, the protein concentration at refolding was up to 5mg/mL when the contaminants were removed, with no adverse effects on refolding. The regenerated pentamer was indistinguishable in its biochemical and immunological characteristics from CTB secreted from E. coli or choleragenoid from Vibrio cholerae. This study provides a simple but very efficient strategy for pentamerizing CTB with a highly homogeneous molecular conformation, with which it may be feasible to engineer CTB derivatives and CTB fusion antigens. Copyright © 2016 Elsevier Ltd. All rights reserved.

Qdot Labeled Actin Super Resolution Motility Assay Measures Low Duty Cycle Muscle Myosin Step-Size

PubMed Central

Wang, Yihua; Ajtai, Katalin; Burghardt, Thomas P.

2013-01-01

Myosin powers contraction in heart and skeletal muscle and is a leading target for mutations implicated in inheritable muscle diseases. During contraction, myosin transduces ATP free energy into the work of muscle shortening against resisting force. Muscle shortening involves relative sliding of myosin and actin filaments. Skeletal actin filaments were fluorescence labeled with a streptavidin conjugate quantum dot (Qdot) binding biotin-phalloidin on actin. Single Qdot’s were imaged in time with total internal reflection fluorescence microscopy then spatially localized to 1-3 nanometers using a super-resolution algorithm as they translated with actin over a surface coated with skeletal heavy meromyosin (sHMM) or full length β-cardiac myosin (MYH7). Average Qdot-actin velocity matches measurements with rhodamine-phalloidin labeled actin. The sHMM Qdot-actin velocity histogram contains low velocity events corresponding to actin translation in quantized steps of ~5 nm. The MYH7 velocity histogram has quantized steps at 3 and 8 nm in addition to 5 nm, and, larger compliance than sHMM depending on MYH7 surface concentration. Low duty cycle skeletal and cardiac myosin present challenges for a single molecule assay because actomyosin dissociates quickly and the freely moving element diffuses away. The in vitro motility assay has modestly more actomyosin interactions and methylcellulose inhibited diffusion to sustain the complex while preserving a subset of encounters that do not overlap in time on a single actin filament. A single myosin step is isolated in time and space then characterized using super-resolution. The approach provides quick, quantitative, and inexpensive step-size measurement for low duty cycle muscle myosin. PMID:23383646

Recognizing visual focus of attention from head pose in natural meetings.

PubMed

Ba, Sileye O; Odobez, Jean-Marc

2009-02-01

We address the problem of recognizing the visual focus of attention (VFOA) of meeting participants based on their head pose. To this end, the head pose observations are modeled using a Gaussian mixture model (GMM) or a hidden Markov model (HMM) whose hidden states correspond to the VFOA. The novelties of this paper are threefold. First, contrary to previous studies on the topic, in our setup, the potential VFOA of a person is not restricted to other participants only. It includes environmental targets as well (a table and a projection screen), which increases the complexity of the task, with more VFOA targets spread in the pan as well as tilt gaze space. Second, we propose a geometric model to set the GMM or HMM parameters by exploiting results from cognitive science on saccadic eye motion, which allows the prediction of the head pose given a gaze target. Third, an unsupervised parameter adaptation step not using any labeled data is proposed, which accounts for the specific gazing behavior of each participant. Using a publicly available corpus of eight meetings featuring four persons, we analyze the above methods by evaluating, through objective performance measures, the recognition of the VFOA from head pose information obtained either using a magnetic sensor device or a vision-based tracking system. The results clearly show that in such complex but realistic situations, the VFOA recognition performance is highly dependent on how well the visual targets are separated for a given meeting participant. In addition, the results show that the use of a geometric model with unsupervised adaptation achieves better results than the use of training data to set the HMM parameters.

The Roles of APOBEC3G Complexes in the Incorporation of APOBEC3G into HIV-1

PubMed Central

Zhang, Quan; Liu, Zhenlong; Jia, Pingping; Zhou, Jinming; Guo, Fei; You, Xuefu; Yu, Liyan; Zhao, Lixun; Jiang, Jiandong; Cen, Shan

2013-01-01

Background The incorporation of human APOBEC3G (hA3G) into HIV is required for exerting its antiviral activity, therefore the mechanism underlying hA3G virion encapsidation has been investigated extensively. hA3G was shown to form low-molecular-mass (LMM) and high-molecular-mass (HMM) complexes. The function of different forms of hA3G in its viral incorporation remains unclear. Methodology/Principal Findings In this study, we investigated the subcellular distribution and lipid raft association of hA3G using subcellular fractionation, membrane floatation assay and pulse-chase radiolabeling experiments respectively, and studied the correlation between the ability of hA3G to form the different complex and its viral incorporation. Our work herein provides evidence that the majority of newly-synthesized hA3G interacts with membrane lipid raft domains to form Lipid raft-associated hA3G (RA hA3G), which serve as the precursor of mature HMM hA3G complex, while a minority of newly-synthesized hA3G remains in the cytoplasm as a soluble LMM form. The distribution of hA3G among the soluble LMM form, the RA LMM form and the mature forms of HMM is regulated by a mechanism involving the N-terminal part of the linker region and the C-terminus of hA3G. Mutagenesis studies reveal a direct correlation between the ability of hA3G to form the RA LMM complex and its viral incorporation. Conclusions/Significance Together these data suggest that the Lipid raft-associated LMM A3G complex functions as the cellular source of viral hA3G. PMID:24098356

High-recall protein entity recognition using a dictionary

PubMed Central

Kou, Zhenzhen; Cohen, William W.; Murphy, Robert F.

2010-01-01

Protein name extraction is an important step in mining biological literature. We describe two new methods for this task: semiCRFs and dictionary HMMs. SemiCRFs are a recently-proposed extension to conditional random fields that enables more effective use of dictionary information as features. Dictionary HMMs are a technique in which a dictionary is converted to a large HMM that recognizes phrases from the dictionary, as well as variations of these phrases. Standard training methods for HMMs can be used to learn which variants should be recognized. We compared the performance of our new approaches to that of Maximum Entropy (Max-Ent) and normal CRFs on three datasets, and improvement was obtained for all four methods over the best published results for two of the datasets. CRFs and semiCRFs achieved the highest overall performance according to the widely-used F-measure, while the dictionary HMMs performed the best at finding entities that actually appear in the dictionary—the measure of most interest in our intended application. PMID:15961466

Human activities recognition by head movement using partial recurrent neural network

NASA Astrophysics Data System (ADS)

Tan, Henry C. C.; Jia, Kui; De Silva, Liyanage C.

2003-06-01

Traditionally, human activities recognition has been achieved mainly by the statistical pattern recognition methods or the Hidden Markov Model (HMM). In this paper, we propose a novel use of the connectionist approach for the recognition of ten simple human activities: walking, sitting down, getting up, squatting down and standing up, in both lateral and frontal views, in an office environment. By means of tracking the head movement of the subjects over consecutive frames from a database of different color image sequences, and incorporating the Elman model of the partial recurrent neural network (RNN) that learns the sequential patterns of relative change of the head location in the images, the proposed system is able to robustly classify all the ten activities performed by unseen subjects from both sexes, of different race and physique, with a recognition rate as high as 92.5%. This demonstrates the potential of employing partial RNN to recognize complex activities in the increasingly popular human-activities-based applications.

SnowyOwl: accurate prediction of fungal genes by using RNA-Seq and homology information to select among ab initio models

PubMed Central

2014-01-01

Background Locating the protein-coding genes in novel genomes is essential to understanding and exploiting the genomic information but it is still difficult to accurately predict all the genes. The recent availability of detailed information about transcript structure from high-throughput sequencing of messenger RNA (RNA-Seq) delineates many expressed genes and promises increased accuracy in gene prediction. Computational gene predictors have been intensively developed for and tested in well-studied animal genomes. Hundreds of fungal genomes are now or will soon be sequenced. The differences of fungal genomes from animal genomes and the phylogenetic sparsity of well-studied fungi call for gene-prediction tools tailored to them. Results SnowyOwl is a new gene prediction pipeline that uses RNA-Seq data to train and provide hints for the generation of Hidden Markov Model (HMM)-based gene predictions and to evaluate the resulting models. The pipeline has been developed and streamlined by comparing its predictions to manually curated gene models in three fungal genomes and validated against the high-quality gene annotation of Neurospora crassa; SnowyOwl predicted N. crassa genes with 83% sensitivity and 65% specificity. SnowyOwl gains sensitivity by repeatedly running the HMM gene predictor Augustus with varied input parameters and selectivity by choosing the models with best homology to known proteins and best agreement with the RNA-Seq data. Conclusions SnowyOwl efficiently uses RNA-Seq data to produce accurate gene models in both well-studied and novel fungal genomes. The source code for the SnowyOwl pipeline (in Python) and a web interface (in PHP) is freely available from http://sourceforge.net/projects/snowyowl/. PMID:24980894

Family-Based Benchmarking of Copy Number Variation Detection Software.

PubMed

Nutsua, Marcel Elie; Fischer, Annegret; Nebel, Almut; Hofmann, Sylvia; Schreiber, Stefan; Krawczak, Michael; Nothnagel, Michael

2015-01-01

The analysis of structural variants, in particular of copy-number variations (CNVs), has proven valuable in unraveling the genetic basis of human diseases. Hence, a large number of algorithms have been developed for the detection of CNVs in SNP array signal intensity data. Using the European and African HapMap trio data, we undertook a comparative evaluation of six commonly used CNV detection software tools, namely Affymetrix Power Tools (APT), QuantiSNP, PennCNV, GLAD, R-gada and VEGA, and assessed their level of pair-wise prediction concordance. The tool-specific CNV prediction accuracy was assessed in silico by way of intra-familial validation. Software tools differed greatly in terms of the number and length of the CNVs predicted as well as the number of markers included in a CNV. All software tools predicted substantially more deletions than duplications. Intra-familial validation revealed consistently low levels of prediction accuracy as measured by the proportion of validated CNVs (34-60%). Moreover, up to 20% of apparent family-based validations were found to be due to chance alone. Software using Hidden Markov models (HMM) showed a trend to predict fewer CNVs than segmentation-based algorithms albeit with greater validity. PennCNV yielded the highest prediction accuracy (60.9%). Finally, the pairwise concordance of CNV prediction was found to vary widely with the software tools involved. We recommend HMM-based software, in particular PennCNV, rather than segmentation-based algorithms when validity is the primary concern of CNV detection. QuantiSNP may be used as an additional tool to detect sets of CNVs not detectable by the other tools. Our study also reemphasizes the need for laboratory-based validation, such as qPCR, of CNVs predicted in silico.

zipHMMlib: a highly optimised HMM library exploiting repetitions in the input to speed up the forward algorithm.

PubMed

Sand, Andreas; Kristiansen, Martin; Pedersen, Christian N S; Mailund, Thomas

2013-11-22

Hidden Markov models are widely used for genome analysis as they combine ease of modelling with efficient analysis algorithms. Calculating the likelihood of a model using the forward algorithm has worst case time complexity linear in the length of the sequence and quadratic in the number of states in the model. For genome analysis, however, the length runs to millions or billions of observations, and when maximising the likelihood hundreds of evaluations are often needed. A time efficient forward algorithm is therefore a key ingredient in an efficient hidden Markov model library. We have built a software library for efficiently computing the likelihood of a hidden Markov model. The library exploits commonly occurring substrings in the input to reuse computations in the forward algorithm. In a pre-processing step our library identifies common substrings and builds a structure over the computations in the forward algorithm which can be reused. This analysis can be saved between uses of the library and is independent of concrete hidden Markov models so one preprocessing can be used to run a number of different models.Using this library, we achieve up to 78 times shorter wall-clock time for realistic whole-genome analyses with a real and reasonably complex hidden Markov model. In one particular case the analysis was performed in less than 8 minutes compared to 9.6 hours for the previously fastest library. We have implemented the preprocessing procedure and forward algorithm as a C++ library, zipHMM, with Python bindings for use in scripts. The library is available at http://birc.au.dk/software/ziphmm/.

Speech recognition for embedded automatic positioner for laparoscope

NASA Astrophysics Data System (ADS)

Chen, Xiaodong; Yin, Qingyun; Wang, Yi; Yu, Daoyin

2014-07-01

In this paper a novel speech recognition methodology based on Hidden Markov Model (HMM) is proposed for embedded Automatic Positioner for Laparoscope (APL), which includes a fixed point ARM processor as the core. The APL system is designed to assist the doctor in laparoscopic surgery, by implementing the specific doctor's vocal control to the laparoscope. Real-time respond to the voice commands asks for more efficient speech recognition algorithm for the APL. In order to reduce computation cost without significant loss in recognition accuracy, both arithmetic and algorithmic optimizations are applied in the method presented. First, depending on arithmetic optimizations most, a fixed point frontend for speech feature analysis is built according to the ARM processor's character. Then the fast likelihood computation algorithm is used to reduce computational complexity of the HMM-based recognition algorithm. The experimental results show that, the method shortens the recognition time within 0.5s, while the accuracy higher than 99%, demonstrating its ability to achieve real-time vocal control to the APL.

Wearable flex sensor system for multiple badminton player grip identification

NASA Astrophysics Data System (ADS)

Jacob, Alvin; Zakaria, Wan Nurshazwani Wan; Tomari, Mohd Razali Bin Md; Sek, Tee Kian; Suberi, Anis Azwani Muhd

2017-09-01

This paper focuses on the development of a wearable sensor system to identify the different types of badminton grip that is used by a player during training. Badminton movements and strokes are fast and dynamic, where most of the involved movement are difficult to identify with the naked eye. Also, the usage of high processing optometric motion capture system is expensive and causes computational burden. Therefore, this paper suggests the development of a sensorized glove using flex sensor to measure a badminton player's finger flexion angle. The proposed Hand Monitoring Module (HMM) is connected to a personal computer through Bluetooth to enable wireless data transmission. The usability and feasibility of the HMM to identify different grip types were examined through a series of experiments, where the system exhibited 70% detection ability for the five different grip type. The outcome plays a major role in training players to use the proper grips for a badminton stroke to achieve a more powerful and accurate stroke execution.

FOAM (Functional Ontology Assignments for Metagenomes): A Hidden Markov Model (HMM) database with environmental focus

DOE PAGES

Prestat, Emmanuel; David, Maude M.; Hultman, Jenni; ...

2014-09-26

A new functional gene database, FOAM (Functional Ontology Assignments for Metagenomes), was developed to screen environmental metagenomic sequence datasets. FOAM provides a new functional ontology dedicated to classify gene functions relevant to environmental microorganisms based on Hidden Markov Models (HMMs). Sets of aligned protein sequences (i.e. ‘profiles’) were tailored to a large group of target KEGG Orthologs (KOs) from which HMMs were trained. The alignments were checked and curated to make them specific to the targeted KO. Within this process, sequence profiles were enriched with the most abundant sequences available to maximize the yield of accurate classifier models. An associatedmore » functional ontology was built to describe the functional groups and hierarchy. FOAM allows the user to select the target search space before HMM-based comparison steps and to easily organize the results into different functional categories and subcategories. FOAM is publicly available at http://portal.nersc.gov/project/m1317/FOAM/.« less

Hidden Markov Model-Based CNV Detection Algorithms for Illumina Genotyping Microarrays.

PubMed

Seiser, Eric L; Innocenti, Federico

2014-01-01

Somatic alterations in DNA copy number have been well studied in numerous malignancies, yet the role of germline DNA copy number variation in cancer is still emerging. Genotyping microarrays generate allele-specific signal intensities to determine genotype, but may also be used to infer DNA copy number using additional computational approaches. Numerous tools have been developed to analyze Illumina genotype microarray data for copy number variant (CNV) discovery, although commonly utilized algorithms freely available to the public employ approaches based upon the use of hidden Markov models (HMMs). QuantiSNP, PennCNV, and GenoCN utilize HMMs with six copy number states but vary in how transition and emission probabilities are calculated. Performance of these CNV detection algorithms has been shown to be variable between both genotyping platforms and data sets, although HMM approaches generally outperform other current methods. Low sensitivity is prevalent with HMM-based algorithms, suggesting the need for continued improvement in CNV detection methodologies.

Application of finite elements heterogeneous multi-scale method to eddy currents non destructive testing of carbon composites material

NASA Astrophysics Data System (ADS)

Khebbab, Mohamed; Feliachi, Mouloud; El Hadi Latreche, Mohamed

2018-03-01

In this present paper, a simulation of eddy current non-destructive testing (EC NDT) on unidirectional carbon fiber reinforced polymer is performed; for this magneto-dynamic formulation in term of magnetic vector potential is solved using finite element heterogeneous multi-scale method (FE HMM). FE HMM has as goal to compute the homogenized solution without calculating the homogenized tensor explicitly, the solution is based only on the physical characteristic known in micro domain. This feature is well adapted to EC NDT to evaluate defect in carbon composite material in microscopic scale, where the defect detection is performed by coil impedance measurement; the measurement value is intimately linked to material characteristic in microscopic level. Based on this, our model can handle different defects such as: cracks, inclusion, internal electrical conductivity changes, heterogeneities, etc. The simulation results were compared with the solution obtained with homogenized material using mixture law, a good agreement was found.

Integrating Decision Tree and Hidden Markov Model (HMM) for Subtype Prediction of Human Influenza A Virus

NASA Astrophysics Data System (ADS)

Attaluri, Pavan K.; Chen, Zhengxin; Weerakoon, Aruna M.; Lu, Guoqing

Multiple criteria decision making (MCDM) has significant impact in bioinformatics. In the research reported here, we explore the integration of decision tree (DT) and Hidden Markov Model (HMM) for subtype prediction of human influenza A virus. Infection with influenza viruses continues to be an important public health problem. Viral strains of subtype H3N2 and H1N1 circulates in humans at least twice annually. The subtype detection depends mainly on the antigenic assay, which is time-consuming and not fully accurate. We have developed a Web system for accurate subtype detection of human influenza virus sequences. The preliminary experiment showed that this system is easy-to-use and powerful in identifying human influenza subtypes. Our next step is to examine the informative positions at the protein level and extend its current functionality to detect more subtypes. The web functions can be accessed at http://glee.ist.unomaha.edu/.

Passive acoustic leak detection for sodium cooled fast reactors using hidden Markov models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Riber Marklund, A.; Kishore, S.; Prakash, V.

2015-07-01

Acoustic leak detection for steam generators of sodium fast reactors have been an active research topic since the early 1970's and several methods have been tested over the years. Inspired by its success in the field of automatic speech recognition, we here apply hidden Markov models (HMM) in combination with Gaussian mixture models (GMM) to the problem. To achieve this, we propose a new feature calculation scheme, based on the temporal evolution of the power spectral density (PSD) of the signal. Using acoustic signals recorded during steam/water injection experiments done at the Indira Gandhi Centre for Atomic Research (IGCAR), themore » proposed method is tested. We perform parametric studies on the HMM+GMM model size and demonstrate that the proposed method a) performs well without a priori knowledge of injection noise, b) can incorporate several noise models and c) has an output distribution that simplifies false alarm rate control. (authors)« less

Cover song identification by sequence alignment algorithms

NASA Astrophysics Data System (ADS)

Wang, Chih-Li; Zhong, Qian; Wang, Szu-Ying; Roychowdhury, Vwani

2011-10-01

Content-based music analysis has drawn much attention due to the rapidly growing digital music market. This paper describes a method that can be used to effectively identify cover songs. A cover song is a song that preserves only the crucial melody of its reference song but different in some other acoustic properties. Hence, the beat/chroma-synchronous chromagram, which is insensitive to the variation of the timber or rhythm of songs but sensitive to the melody, is chosen. The key transposition is achieved by cyclically shifting the chromatic domain of the chromagram. By using the Hidden Markov Model (HMM) to obtain the time sequences of songs, the system is made even more robust. Similar structure or length between the cover songs and its reference are not necessary by the Smith-Waterman Alignment Algorithm.

Hideen Markov Models and Neural Networks for Fault Detection in Dynamic Systems

NASA Technical Reports Server (NTRS)

Smyth, Padhraic

1994-01-01

None given. (From conclusion): Neural networks plus Hidden Markov Models(HMM)can provide excellene detection and false alarm rate performance in fault detection applications. Modified models allow for novelty detection. Also covers some key contributions of neural network model, and application status.

Implementation of Home based management of malaria in children reduces the work load for peripheral health facilities in a rural district of Burkina Faso.

PubMed

Tiono, Alfred B; Kaboré, Youssouf; Traoré, Abdoulaye; Convelbo, Nathalie; Pagnoni, Franco; Sirima, Sodiomon B

2008-10-03

Home Management of Malaria (HMM) is one of the key strategies to reduce the burden of malaria for vulnerable population in endemic countries. It is based on the evidence that well-trained communities health workers can provide prompt and adequate care to patients close to their homes. The strategy has been shown to reduce malaria mortality and severe morbidity and has been adopted by the World Health Organization as a cornerstone of malaria control in Africa. However, the potential fall-out of this community-based strategy on the work burden at the peripheral health facilities level has never been investigated. A two-arm interventional study was conducted in a rural health district of Burkina Faso. The HMM strategy has been implemented in seven community clinics catchment's area (intervention arm). For the other seven community clinics in the control arm, no HMM intervention was implemented. In each of the study arms, presumptive treatment was provided for episodes of fevers/malaria (defined operationally as malaria). The study drug was artemether-lumefantrine, which was sold at a subsidized price by community health workers/Key opinion leaders at the community level and by the pharmacists at the health facility level. The outcome measured was the proportion of malaria cases among all health facility attendance (all causes diseases) in both arms throughout the high transmission season. A total of 7,621 children were enrolled in the intervention arm and 7,605 in the control arm. During the study period, the proportions of malaria cases among all health facility attendance (all causes diseases) were 21.0%, (445/2,111, 95% CI [19.3%-22.7%]) and 70.7% (2,595/3,671, 95% CI 68.5%-71.5%), respectively in the intervention and control arms (p < 0.0001). The relative risk ratio for a fever/malaria episode to be treated at the HF level was 30% (0.30 < RR < 0.32). The number of malaria episodes treated in the intervention arm was much higher than in the control arm (6,661 vs. 2,595), with malaria accounting for 87.4% of all disease episodes recorded in the intervention area and for 34.1% in the control area (P < 0.0001). Of all the malaria cases treated in the intervention arm, only 6.7% were treated at the health facility level. These findings suggest that implementation of HMM, by reducing the workload in health facilities, might contributes to an overall increase of the performance of the peripheral health facilities.

Remote monitoring of soldier safety through body posture identification using wearable sensor networks

NASA Astrophysics Data System (ADS)

Biswas, Subir; Quwaider, Muhannad

2008-04-01

The physical safety and well being of the soldiers in a battlefield is the highest priority of Incident Commanders. Currently, the ability to track and monitor soldiers rely on visual and verbal communication which can be somewhat limited in scenarios where the soldiers are deployed inside buildings and enclosed areas that are out of visual range of the commanders. Also, the need for being stealth can often prevent a battling soldier to send verbal clues to a commander about his or her physical well being. Sensor technologies can remotely provide various data about the soldiers including physiological monitoring and personal alert safety system functionality. This paper presents a networked sensing solution in which a body area wireless network of multi-modal sensors can monitor the body movement and other physiological parameters for statistical identification of a soldier's body posture, which can then be indicative of the physical conditions and safety alerts of the soldier in question. The specific concept is to leverage on-body proximity sensing and a Hidden Markov Model (HMM) based mechanism that can be applied for stochastic identification of human body postures using a wearable sensor network. The key idea is to collect relative proximity information between wireless sensors that are strategically placed over a subject's body to monitor the relative movements of the body segments, and then to process that using HMM in order to identify the subject's body postures. The key novelty of this approach is a departure from the traditional accelerometry based approaches in which the individual body segment movements, rather than their relative proximity, is used for activity monitoring and posture detection. Through experiments with body mounted sensors we demonstrate that while the accelerometry based approaches can be used for differentiating activity intensive postures such as walking and running, they are not very effective for identification and differentiation between low activity postures such as sitting and standing. We develop a wearable sensor network that monitors relative proximity using Radio Signal Strength indication (RSSI), and then construct a HMM system for posture identification in the presence of sensing errors. Controlled experiments using human subjects were carried out for evaluating the accuracy of the HMM identified postures compared to a naÃve threshold based mechanism, and its variations over different human subjects. A large spectrum of target human postures, including lie down, sit (straight and reclined), stand, walk, run, sprint and stair climbing, are used for validating the proposed system.

Insights into the structure and function of fungal β-mannosidases from glycoside hydrolase family 2 based on multiple crystal structures of the Trichoderma harzianum enzyme.

PubMed

Nascimento, Alessandro S; Muniz, Joao Renato C; Aparício, Ricardo; Golubev, Alexander M; Polikarpov, Igor

2014-09-01

Hemicellulose is an important part of the plant cell wall biomass, and is relevant to cellulosic ethanol technologies. β-Mannosidases are enzymes capable of cleaving nonreducing residues of β-d-mannose from β-d-mannosides and hemicellulose mannose-containing polysaccharides, such as mannans and galactomannans. β-Mannosidases are distributed between glycoside hydrolase (GH) families 1, 2, and 5, and only a handful of the enzymes have been structurally characterized to date. The only published X-ray structure of a GH family 2 mannosidase is that of the bacterial Bacteroides thetaiotaomicron enzyme. No structures of eukaryotic mannosidases of this family are currently available. To fill this gap, we set out to solve the structure of Trichoderma harzianum GH family 2 β-mannosidase and to refine it to 1.9-Å resolution. Structural comparisons of the T. harzianum GH2 β-mannosidase highlight similarities in its structural architecture with other members of GH family 2, reveal the molecular mechanism of β-mannoside binding and recognition, and shed light on its putative galactomannan-binding site. Coordinates and observed structure factor amplitudes have been deposited with the Protein Data Bank (4CVU and 4UOJ). The T. harzianum β-mannosidase 2A nucleotide sequence has GenBank accession number BankIt1712036 GeneMark.hmm KJ624918. © 2014 FEBS.

Feasibility and acceptability of home-based management of malaria strategy adapted to Sudan's conditions using artemisinin-based combination therapy and rapid diagnostic test.

PubMed

Elmardi, Khalid A; Malik, Elfatih M; Abdelgadir, Tarig; Ali, Salah H; Elsyed, Abdalla H; Mudather, Mahmoud A; Elhassan, Asma H; Adam, Ishag

2009-03-09

Malaria remains a major public health problem especially in sub-Saharan Africa. Despite the efforts exerted to provide effective anti-malarial drugs, still some communities suffer from getting access to these services due to many barriers. This research aimed to assess the feasibility and acceptability of home-based management of malaria (HMM) strategy using artemisinin-based combination therapy (ACT) for treatment and rapid diagnostic test (RDT) for diagnosis. This is a study conducted in 20 villages in Um Adara area, South Kordofan state, Sudan. Two-thirds (66%) of the study community were seeking treatment from heath facilities, which were more than 5 km far from their villages with marked inaccessibility during rainy season. Volunteers (one per village) were trained on using RDTs for diagnosis and artesunate plus sulphadoxine-pyrimethamine for treating malaria patients, as well as referral of severe and non-malaria cases. A system for supply and monitoring was established based on the rural health centre, which acted as a link between the volunteers and the health system. Advocacy for the policy was done through different tools. Volunteers worked on non-monetary incentives but only a consultation fee of One Sudanese Pound (equivalent to US$0.5).Pre- and post-intervention assessment was done using household survey, focus group discussion with the community leaders, structured interview with the volunteers, and records and reports analysis. The overall adherence of volunteers to the project protocol in treating and referring cases was accepted that was only one of the 20 volunteers did not comply with the study guidelines. Although the use of RDTs seemed to have improved the level of accuracy and trust in the diagnosis, 30% of volunteers did not rely on the negative RDT results when treating fever cases. Almost all (94.7%) the volunteers felt that they were satisfied with the spiritual outcome of their new tasks. As well, volunteers have initiated advocacy campaigns supported by their village health committees which were found to have a positive role to play in the project that proved their acceptability of the HMM design. The planned system for supply was found to be effective. The project was found to improve the accessibility to ACTs from 25% to 64.7% and the treatment seeking behaviour from 83.3% to 100% before- and after the HMM implementation respectively. The evaluation of the project identified the feasibility of the planned model in Sudan's condition. Moreover, the communities as well as the volunteers found to be satisfied with and supportive to the system and the outcome. The problem of treating other febrile cases when diagnosis is not malaria and other non-fever cases needs to be addressed as well.

HMMER web server: 2018 update.

PubMed

Potter, Simon C; Luciani, Aurélien; Eddy, Sean R; Park, Youngmi; Lopez, Rodrigo; Finn, Robert D

2018-06-14

The HMMER webserver [http://www.ebi.ac.uk/Tools/hmmer] is a free-to-use service which provides fast searches against widely used sequence databases and profile hidden Markov model (HMM) libraries using the HMMER software suite (http://hmmer.org). The results of a sequence search may be summarized in a number of ways, allowing users to view and filter the significant hits by domain architecture or taxonomy. For large scale usage, we provide an application programmatic interface (API) which has been expanded in scope, such that all result presentations are available via both HTML and API. Furthermore, we have refactored our JavaScript visualization library to provide standalone components for different result representations. These consume the aforementioned API and can be integrated into third-party websites. The range of databases that can be searched against has been expanded, adding four sequence datasets (12 in total) and one profile HMM library (6 in total). To help users explore the biological context of their results, and to discover new data resources, search results are now supplemented with cross references to other EMBL-EBI databases.

An online handwriting recognition system for Turkish

NASA Astrophysics Data System (ADS)

Vural, Esra; Erdogan, Hakan; Oflazer, Kemal; Yanikoglu, Berrin A.

2004-12-01

Despite recent developments in Tablet PC technology, there has not been any applications for recognizing handwritings in Turkish. In this paper, we present an online handwritten text recognition system for Turkish, developed using the Tablet PC interface. However, even though the system is developed for Turkish, the addressed issues are common to online handwriting recognition systems in general. Several dynamic features are extracted from the handwriting data for each recorded point and Hidden Markov Models (HMM) are used to train letter and word models. We experimented with using various features and HMM model topologies, and report on the effects of these experiments. We started with first and second derivatives of the x and y coordinates and relative change in the pen pressure as initial features. We found that using two more additional features, that is, number of neighboring points and relative heights of each point with respect to the base-line improve the recognition rate. In addition, extracting features within strokes and using a skipping state topology improve the system performance as well. The improved system performance is 94% in recognizing handwritten words from a 1000-word lexicon.

An online handwriting recognition system for Turkish

NASA Astrophysics Data System (ADS)

Vural, Esra; Erdogan, Hakan; Oflazer, Kemal; Yanikoglu, Berrin A.

2005-01-01

Despite recent developments in Tablet PC technology, there has not been any applications for recognizing handwritings in Turkish. In this paper, we present an online handwritten text recognition system for Turkish, developed using the Tablet PC interface. However, even though the system is developed for Turkish, the addressed issues are common to online handwriting recognition systems in general. Several dynamic features are extracted from the handwriting data for each recorded point and Hidden Markov Models (HMM) are used to train letter and word models. We experimented with using various features and HMM model topologies, and report on the effects of these experiments. We started with first and second derivatives of the x and y coordinates and relative change in the pen pressure as initial features. We found that using two more additional features, that is, number of neighboring points and relative heights of each point with respect to the base-line improve the recognition rate. In addition, extracting features within strokes and using a skipping state topology improve the system performance as well. The improved system performance is 94% in recognizing handwritten words from a 1000-word lexicon.

Vocal classification of vocalizations of a pair of Asian small-clawed otters to determine stress.

PubMed

Scheifele, Peter M; Johnson, Michael T; Fry, Michelle; Hamel, Benjamin; Laclede, Kathryn

2015-07-01

Asian Small-Clawed Otters (Aonyx cinerea) are a small, protected but threatened species living in freshwater. They are gregarious and live in monogamous pairs for their lifetimes, communicating via scent and acoustic vocalizations. This study utilized a hidden Markov model (HMM) to classify stress versus non-stress calls from a sibling pair under professional care. Vocalizations were expertly annotated by keepers into seven contextual categories. Four of these-aggression, separation anxiety, pain, and prefeeding-were identified as stressful contexts, and three of them-feeding, training, and play-were identified as non-stressful contexts. The vocalizations were segmented, manually categorized into broad vocal type call types, and analyzed to determine signal to noise ratios. From this information, vocalizations from the most common contextual categories were used to implement HMM-based automatic classification experiments, which included individual identification, stress vs non-stress, and individual context classification. Results indicate that both individual identity and stress vs non-stress were distinguishable, with accuracies above 90%, but that individual contexts within the stress category were not easily separable.

Detecting cell division of Pseudomonas aeruginosa bacteria from bright-field microscopy images with hidden conditional random fields.

PubMed

Ong, Lee-Ling S; Xinghua Zhang; Kundukad, Binu; Dauwels, Justin; Doyle, Patrick; Asada, H Harry

2016-08-01

An approach to automatically detect bacteria division with temporal models is presented. To understand how bacteria migrate and proliferate to form complex multicellular behaviours such as biofilms, it is desirable to track individual bacteria and detect cell division events. Unlike eukaryotic cells, prokaryotic cells such as bacteria lack distinctive features, causing bacteria division difficult to detect in a single image frame. Furthermore, bacteria may detach, migrate close to other bacteria and may orientate themselves at an angle to the horizontal plane. Our system trains a hidden conditional random field (HCRF) model from tracked and aligned bacteria division sequences. The HCRF model classifies a set of image frames as division or otherwise. The performance of our HCRF model is compared with a Hidden Markov Model (HMM). The results show that a HCRF classifier outperforms a HMM classifier. From 2D bright field microscopy data, it is a challenge to separate individual bacteria and associate observations to tracks. Automatic detection of sequences with bacteria division will improve tracking accuracy.

Hidden Markov model for dependent mark loss and survival estimation

USGS Publications Warehouse

Laake, Jeffrey L.; Johnson, Devin S.; Diefenbach, Duane R.; Ternent, Mark A.

2014-01-01

Mark-recapture estimators assume no loss of marks to provide unbiased estimates of population parameters. We describe a hidden Markov model (HMM) framework that integrates a mark loss model with a Cormack–Jolly–Seber model for survival estimation. Mark loss can be estimated with single-marked animals as long as a sub-sample of animals has a permanent mark. Double-marking provides an estimate of mark loss assuming independence but dependence can be modeled with a permanently marked sub-sample. We use a log-linear approach to include covariates for mark loss and dependence which is more flexible than existing published methods for integrated models. The HMM approach is demonstrated with a dataset of black bears (Ursus americanus) with two ear tags and a subset of which were permanently marked with tattoos. The data were analyzed with and without the tattoo. Dropping the tattoos resulted in estimates of survival that were reduced by 0.005–0.035 due to tag loss dependence that could not be modeled. We also analyzed the data with and without the tattoo using a single tag. By not using.

EvoCor: a platform for predicting functionally related genes using phylogenetic and expression profiles.

PubMed

Dittmar, W James; McIver, Lauren; Michalak, Pawel; Garner, Harold R; Valdez, Gregorio

2014-07-01

The wealth of publicly available gene expression and genomic data provides unique opportunities for computational inference to discover groups of genes that function to control specific cellular processes. Such genes are likely to have co-evolved and be expressed in the same tissues and cells. Unfortunately, the expertise and computational resources required to compare tens of genomes and gene expression data sets make this type of analysis difficult for the average end-user. Here, we describe the implementation of a web server that predicts genes involved in affecting specific cellular processes together with a gene of interest. We termed the server 'EvoCor', to denote that it detects functional relationships among genes through evolutionary analysis and gene expression correlation. This web server integrates profiles of sequence divergence derived by a Hidden Markov Model (HMM) and tissue-wide gene expression patterns to determine putative functional linkages between pairs of genes. This server is easy to use and freely available at http://pilot-hmm.vbi.vt.edu/. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Segment-based acoustic models for continuous speech recognition

NASA Astrophysics Data System (ADS)

Ostendorf, Mari; Rohlicek, J. R.

1993-07-01

This research aims to develop new and more accurate stochastic models for speaker-independent continuous speech recognition, by extending previous work in segment-based modeling and by introducing a new hierarchical approach to representing intra-utterance statistical dependencies. These techniques, which are more costly than traditional approaches because of the large search space associated with higher order models, are made feasible through rescoring a set of HMM-generated N-best sentence hypotheses. We expect these different modeling techniques to result in improved recognition performance over that achieved by current systems, which handle only frame-based observations and assume that these observations are independent given an underlying state sequence. In the fourth quarter of the project, we have completed the following: (1) ported our recognition system to the Wall Street Journal task, a standard task in the ARPA community; (2) developed an initial dependency-tree model of intra-utterance observation correlation; and (3) implemented baseline language model estimation software. Our initial results on the Wall Street Journal task are quite good and represent significantly improved performance over most HMM systems reporting on the Nov. 1992 5k vocabulary test set.

Exploring the behavior of Caenorhabditis Elegans by using a self-organizing map and hidden Markov model

NASA Astrophysics Data System (ADS)

Kang, Seung-Ho; Lee, Sang-Hee; Chon, Tae-Soo

2012-02-01

In recent decades, the behavior of Caenorhabditis elegans ( C. elegans) has been extensively studied to understand the respective roles of neural control and biomechanics. Thus far, however, only a few studies on the simulation modeling of C. elegans swimming behavior have been conducted because it is mathematically difficult to describe its complicated behavior. In this study, we built two hidden Markov models (HMMs), corresponding to the movements of C. elegans in a controlled environment with no chemical treatment and in a formaldehyde-treated environment (0.1 ppm), respectively. The movement was characterized by a series of shape patterns of the organism, taken every 0.25 s for 40 min. All shape patterns were quantified by branch length similarity (BLS) entropy and classified into seven patterns by using the self-organizing map (SOM) and the k-means clustering algorithm. The HMM coupled with the SOM was successful in accurately explaining the organism's behavior. In addition, we briefly discussed the possibility of using the HMM together with BLS entropy to develop bio-monitoring systems for real-time applications to determine water quality.

Role of high molecular mass organics in colour formation during biological treatment of pulp and paper wastewater.

PubMed

Milestone, C B; Stuthridge, T R; Fulthorpe, R R

2007-01-01

This paper forms part of series of biological treatment colour behaviour studies. Surveys across a range of mills have observed colour increases in aerated stabilisation basins of 20-45%. Much of the colour formation has been demonstrated to occur in high molecular mass effluent organic constituents (HMM) present in bleach plant effluents. Removing material greater than 3000 Da essentially eliminated the colour forming ability in both E and D stage wastewaters. We have also shown that pulp and paper sludges contain anaerobic bacteria capable of reducing humic like materials. Colour formation was correlated to the anoxic conditions and the availability of readily biodegradable organic constituents during the wastewater treatment process. Overall, these studies suggest that colour formation in pulp and paper biological treatment systems may be caused by anaerobic bacteria using HMM material from the bleaching effluents as an electron acceptor for growth. This leads to the reduction of the material, which in turn leads to non-reversible internal changes, such as intra-molecular polymerisation or formation of chromophoric functional groups.

Regime switching model for financial data: Empirical risk analysis

NASA Astrophysics Data System (ADS)

Salhi, Khaled; Deaconu, Madalina; Lejay, Antoine; Champagnat, Nicolas; Navet, Nicolas

2016-11-01

This paper constructs a regime switching model for the univariate Value-at-Risk estimation. Extreme value theory (EVT) and hidden Markov models (HMM) are combined to estimate a hybrid model that takes volatility clustering into account. In the first stage, HMM is used to classify data in crisis and steady periods, while in the second stage, EVT is applied to the previously classified data to rub out the delay between regime switching and their detection. This new model is applied to prices of numerous stocks exchanged on NYSE Euronext Paris over the period 2001-2011. We focus on daily returns for which calibration has to be done on a small dataset. The relative performance of the regime switching model is benchmarked against other well-known modeling techniques, such as stable, power laws and GARCH models. The empirical results show that the regime switching model increases predictive performance of financial forecasting according to the number of violations and tail-loss tests. This suggests that the regime switching model is a robust forecasting variant of power laws model while remaining practical to implement the VaR measurement.

Probabilistic Multi-Sensor Fusion Based Indoor Positioning System on a Mobile Device

PubMed Central

He, Xiang; Aloi, Daniel N.; Li, Jia

2015-01-01

Nowadays, smart mobile devices include more and more sensors on board, such as motion sensors (accelerometer, gyroscope, magnetometer), wireless signal strength indicators (WiFi, Bluetooth, Zigbee), and visual sensors (LiDAR, camera). People have developed various indoor positioning techniques based on these sensors. In this paper, the probabilistic fusion of multiple sensors is investigated in a hidden Markov model (HMM) framework for mobile-device user-positioning. We propose a graph structure to store the model constructed by multiple sensors during the offline training phase, and a multimodal particle filter to seamlessly fuse the information during the online tracking phase. Based on our algorithm, we develop an indoor positioning system on the iOS platform. The experiments carried out in a typical indoor environment have shown promising results for our proposed algorithm and system design. PMID:26694387

Probabilistic Multi-Sensor Fusion Based Indoor Positioning System on a Mobile Device.

PubMed

He, Xiang; Aloi, Daniel N; Li, Jia

2015-12-14

Nowadays, smart mobile devices include more and more sensors on board, such as motion sensors (accelerometer, gyroscope, magnetometer), wireless signal strength indicators (WiFi, Bluetooth, Zigbee), and visual sensors (LiDAR, camera). People have developed various indoor positioning techniques based on these sensors. In this paper, the probabilistic fusion of multiple sensors is investigated in a hidden Markov model (HMM) framework for mobile-device user-positioning. We propose a graph structure to store the model constructed by multiple sensors during the offline training phase, and a multimodal particle filter to seamlessly fuse the information during the online tracking phase. Based on our algorithm, we develop an indoor positioning system on the iOS platform. The experiments carried out in a typical indoor environment have shown promising results for our proposed algorithm and system design.

Directional RNA-seq reveals highly complex condition-dependent transcriptomes in E. coli K12 through accurate full-length transcripts assembling.

PubMed

Li, Shan; Dong, Xia; Su, Zhengchang

2013-07-30

Although prokaryotic gene transcription has been studied over decades, many aspects of the process remain poorly understood. Particularly, recent studies have revealed that transcriptomes in many prokaryotes are far more complex than previously thought. Genes in an operon are often alternatively and dynamically transcribed under different conditions, and a large portion of genes and intergenic regions have antisense RNA (asRNA) and non-coding RNA (ncRNA) transcripts, respectively. Ironically, similar studies have not been conducted in the model bacterium E coli K12, thus it is unknown whether or not the bacterium possesses similar complex transcriptomes. Furthermore, although RNA-seq becomes the major method for analyzing the complexity of prokaryotic transcriptome, it is still a challenging task to accurately assemble full length transcripts using short RNA-seq reads. To fill these gaps, we have profiled the transcriptomes of E. coli K12 under different culture conditions and growth phases using a highly specific directional RNA-seq technique that can capture various types of transcripts in the bacterial cells, combined with a highly accurate and robust algorithm and tool TruHMM (http://bioinfolab.uncc.edu/TruHmm_package/) for assembling full length transcripts. We found that 46.9 ~ 63.4% of expressed operons were utilized in their putative alternative forms, 72.23 ~ 89.54% genes had putative asRNA transcripts and 51.37 ~ 72.74% intergenic regions had putative ncRNA transcripts under different culture conditions and growth phases. As has been demonstrated in many other prokaryotes, E. coli K12 also has a highly complex and dynamic transcriptomes under different culture conditions and growth phases. Such complex and dynamic transcriptomes might play important roles in the physiology of the bacterium. TruHMM is a highly accurate and robust algorithm for assembling full-length transcripts in prokaryotes using directional RNA-seq short reads.

Directional RNA-seq reveals highly complex condition-dependent transcriptomes in E. coli K12 through accurate full-length transcripts assembling

PubMed Central

2013-01-01

Background Although prokaryotic gene transcription has been studied over decades, many aspects of the process remain poorly understood. Particularly, recent studies have revealed that transcriptomes in many prokaryotes are far more complex than previously thought. Genes in an operon are often alternatively and dynamically transcribed under different conditions, and a large portion of genes and intergenic regions have antisense RNA (asRNA) and non-coding RNA (ncRNA) transcripts, respectively. Ironically, similar studies have not been conducted in the model bacterium E coli K12, thus it is unknown whether or not the bacterium possesses similar complex transcriptomes. Furthermore, although RNA-seq becomes the major method for analyzing the complexity of prokaryotic transcriptome, it is still a challenging task to accurately assemble full length transcripts using short RNA-seq reads. Results To fill these gaps, we have profiled the transcriptomes of E. coli K12 under different culture conditions and growth phases using a highly specific directional RNA-seq technique that can capture various types of transcripts in the bacterial cells, combined with a highly accurate and robust algorithm and tool TruHMM (http://bioinfolab.uncc.edu/TruHmm_package/) for assembling full length transcripts. We found that 46.9 ~ 63.4% of expressed operons were utilized in their putative alternative forms, 72.23 ~ 89.54% genes had putative asRNA transcripts and 51.37 ~ 72.74% intergenic regions had putative ncRNA transcripts under different culture conditions and growth phases. Conclusions As has been demonstrated in many other prokaryotes, E. coli K12 also has a highly complex and dynamic transcriptomes under different culture conditions and growth phases. Such complex and dynamic transcriptomes might play important roles in the physiology of the bacterium. TruHMM is a highly accurate and robust algorithm for assembling full-length transcripts in prokaryotes using directional RNA-seq short reads. PMID:23899370

Characterizing and Differentiating Brain State Dynamics via Hidden Markov Models

PubMed Central

Ou, Jinli; Xie, Li; Jin, Changfeng; Li, Xiang; Zhu, Dajiang; Jiang, Rongxin; Chen, Yaowu

2014-01-01

Functional connectivity measured from resting state fMRI (R-fMRI) data has been widely used to examine the brain’s functional activities and has been recently used to characterize and differentiate brain conditions. However, the dynamical transition patterns of the brain’s functional states have been less explored. In this work, we propose a novel computational framework to quantitatively characterize the brain state dynamics via hidden Markov models (HMMs) learned from the observations of temporally dynamic functional connectomics, denoted as functional connectome states. The framework has been applied to the R-fMRI dataset including 44 post-traumatic stress disorder (PTSD) patients and 51 normal control (NC) subjects. Experimental results show that both PTSD and NC brains were undergoing remarkable changes in resting state and mainly transiting amongst a few brain states. Interestingly, further prediction with the best-matched HMM demonstrates that PTSD would enter into, but could not disengage from, a negative mood state. Importantly, 84 % of PTSD patients and 86 % of NC subjects are successfully classified via multiple HMMs using majority voting. PMID:25331991

Autoregressive-moving-average hidden Markov model for vision-based fall prediction-An application for walker robot.

PubMed

Taghvaei, Sajjad; Jahanandish, Mohammad Hasan; Kosuge, Kazuhiro

2017-01-01

Population aging of the societies requires providing the elderly with safe and dependable assistive technologies in daily life activities. Improving the fall detection algorithms can play a major role in achieving this goal. This article proposes a real-time fall prediction algorithm based on the acquired visual data of a user with walking assistive system from a depth sensor. In the lack of a coupled dynamic model of the human and the assistive walker a hybrid "system identification-machine learning" approach is used. An autoregressive-moving-average (ARMA) model is fitted on the time-series walking data to forecast the upcoming states, and a hidden Markov model (HMM) based classifier is built on the top of the ARMA model to predict falling in the upcoming time frames. The performance of the algorithm is evaluated through experiments with four subjects including an experienced physiotherapist while using a walker robot in five different falling scenarios; namely, fall forward, fall down, fall back, fall left, and fall right. The algorithm successfully predicts the fall with a rate of 84.72%.

Foreign Language Analysis and Recognition (FLARe)

DTIC Science & Technology

2016-10-08

10 7 Chinese CER ...Rates ( CERs ) were obtained with each feature set: (1) 19.2%, (2) 17.3%, and (3) 15.3%. Based on these results, a GMM-HMM speech recognition system...These systems were evaluated on the HUB4 and HKUST test partitions. Table 7 shows the CER obtained on each test set. Whereas including the HKUST data

Diffusion-Tensor Imaging Findings and Cognitive Function Following Hospitalized Mixed-Mechanism Mild Traumatic Brain Injury: A Systematic Review and Meta-Analysis.

PubMed

Oehr, Lucy; Anderson, Jacqueline

2017-11-01

To undertake a systematic review and meta-analysis of the relationship between microstructural damage and cognitive function after hospitalized mixed-mechanism (HMM) mild traumatic brain injury (mTBI). PsycInfo, EMBASE, and MEDLINE were used to find relevant empirical articles published between January 2002 and January 2016. Studies that examined the specific relationship between diffusion tensor imaging (DTI) and cognitive test performance were included. The final sample comprised previously medically and psychiatrically healthy adults with HMM mTBI. Specific data were extracted including mTBI definitional criteria, descriptive statistics, outcome measures, and specific results of associations between DTI metrics and cognitive test performance. Of the 248 original articles retrieved and reviewed, 8 studies met all inclusion criteria and were included in the meta-analysis. The meta-analysis revealed statistically significant associations between reduced white matter integrity and poor performance on measures of attention (fractional anisotropy [FA]: d=.413, P<.001; mean diffusivity [MD]: d=-.407, P=.001), memory (FA: d=.347, P<.001; MD: d=-.568, P<.001), and executive function (FA: d=.246, P<.05), which persisted beyond 1 month postinjury. The findings from the meta-analysis provide clear support for an association between in vivo markers of underlying neuropathology and cognitive function after mTBI. Furthermore, these results demonstrate clearly for the first time that in vivo markers of structural neuropathology are associated with cognitive dysfunction within the domains of attention, memory, and executive function. These findings provide an avenue for future research to examine the causal relationship between mTBI-related neuropathology and cognitive dysfunction. Furthermore, they have important implications for clinical management of patients with mTBI because they provide a more comprehensive understanding of factors that are associated with cognitive dysfunction after mTBI. Copyright © 2017 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Monitoring Farmland Loss Caused by Urbanization in Beijing from Modis Time Series Using Hierarchical Hidden Markov Model

NASA Astrophysics Data System (ADS)

Yuan, Y.; Meng, Y.; Chen, Y. X.; Jiang, C.; Yue, A. Z.

2018-04-01

In this study, we proposed a method to map urban encroachment onto farmland using satellite image time series (SITS) based on the hierarchical hidden Markov model (HHMM). In this method, the farmland change process is decomposed into three hierarchical levels, i.e., the land cover level, the vegetation phenology level, and the SITS level. Then a three-level HHMM is constructed to model the multi-level semantic structure of farmland change process. Once the HHMM is established, a change from farmland to built-up could be detected by inferring the underlying state sequence that is most likely to generate the input time series. The performance of the method is evaluated on MODIS time series in Beijing. Results on both simulated and real datasets demonstrate that our method improves the change detection accuracy compared with the HMM-based method.

Effects of emotion on different phoneme classes

NASA Astrophysics Data System (ADS)

Lee, Chul Min; Yildirim, Serdar; Bulut, Murtaza; Busso, Carlos; Kazemzadeh, Abe; Lee, Sungbok; Narayanan, Shrikanth

2004-10-01

This study investigates the effects of emotion on different phoneme classes using short-term spectral features. In the research on emotion in speech, most studies have focused on prosodic features of speech. In this study, based on the hypothesis that different emotions have varying effects on the properties of the different speech sounds, we investigate the usefulness of phoneme-class level acoustic modeling for automatic emotion classification. Hidden Markov models (HMM) based on short-term spectral features for five broad phonetic classes are used for this purpose using data obtained from recordings of two actresses. Each speaker produces 211 sentences with four different emotions (neutral, sad, angry, happy). Using the speech material we trained and compared the performances of two sets of HMM classifiers: a generic set of ``emotional speech'' HMMs (one for each emotion) and a set of broad phonetic-class based HMMs (vowel, glide, nasal, stop, fricative) for each emotion type considered. Comparison of classification results indicates that different phoneme classes were affected differently by emotional change and that the vowel sounds are the most important indicator of emotions in speech. Detailed results and their implications on the underlying speech articulation will be discussed.

Effect of high-frequency spectral components in computer recognition of dysarthric speech based on a Mel-cepstral stochastic model.

PubMed

Polur, Prasad D; Miller, Gerald E

2005-01-01

Computer speech recognition of individuals with dysarthria, such as cerebral palsy patients, requires a robust technique that can handle conditions of very high variability and limited training data. In this study, a hidden Markov model (HMM) was constructed and conditions investigated that would provide improved performance for a dysarthric speech (isolated word) recognition system intended to act as an assistive/control tool. In particular, we investigated the effect of high-frequency spectral components on the recognition rate of the system to determine if they contributed useful additional information to the system. A small-size vocabulary spoken by three cerebral palsy subjects was chosen. Mel-frequency cepstral coefficients extracted with the use of 15 ms frames served as training input to an ergodic HMM setup. Subsequent results demonstrated that no significant useful information was available to the system for enhancing its ability to discriminate dysarthric speech above 5.5 kHz in the current set of dysarthric data. The level of variability in input dysarthric speech patterns limits the reliability of the system. However, its application as a rehabilitation/control tool to assist dysarthric motor-impaired individuals such as cerebral palsy subjects holds sufficient promise.

Behavior analysis for elderly care using a network of low-resolution visual sensors

NASA Astrophysics Data System (ADS)

Eldib, Mohamed; Deboeverie, Francis; Philips, Wilfried; Aghajan, Hamid

2016-07-01

Recent advancements in visual sensor technologies have made behavior analysis practical for in-home monitoring systems. The current in-home monitoring systems face several challenges: (1) visual sensor calibration is a difficult task and not practical in real-life because of the need for recalibration when the visual sensors are moved accidentally by a caregiver or the senior citizen, (2) privacy concerns, and (3) the high hardware installation cost. We propose to use a network of cheap low-resolution visual sensors (30×30 pixels) for long-term behavior analysis. The behavior analysis starts by visual feature selection based on foreground/background detection to track the motion level in each visual sensor. Then a hidden Markov model (HMM) is used to estimate the user's locations without calibration. Finally, an activity discovery approach is proposed using spatial and temporal contexts. We performed experiments on 10 months of real-life data. We show that the HMM approach outperforms the k-nearest neighbor classifier against ground truth for 30 days. Our framework is able to discover 13 activities of daily livings (ADL parameters). More specifically, we analyze mobility patterns and some of the key ADL parameters to detect increasing or decreasing health conditions.

Spider Neurotoxins, Short Linear Cationic Peptides and Venom Protein Classification Improved by an Automated Competition between Exhaustive Profile HMM Classifiers

PubMed Central

Koua, Dominique; Kuhn-Nentwig, Lucia

2017-01-01

Spider venoms are rich cocktails of bioactive peptides, proteins, and enzymes that are being intensively investigated over the years. In order to provide a better comprehension of that richness, we propose a three-level family classification system for spider venom components. This classification is supported by an exhaustive set of 219 new profile hidden Markov models (HMMs) able to attribute a given peptide to its precise peptide type, family, and group. The proposed classification has the advantages of being totally independent from variable spider taxonomic names and can easily evolve. In addition to the new classifiers, we introduce and demonstrate the efficiency of hmmcompete, a new standalone tool that monitors HMM-based family classification and, after post-processing the result, reports the best classifier when multiple models produce significant scores towards given peptide queries. The combined used of hmmcompete and the new spider venom component-specific classifiers demonstrated 96% sensitivity to properly classify all known spider toxins from the UniProtKB database. These tools are timely regarding the important classification needs caused by the increasing number of peptides and proteins generated by transcriptomic projects. PMID:28786958

COACH: profile-profile alignment of protein families using hidden Markov models.

PubMed

Edgar, Robert C; Sjölander, Kimmen

2004-05-22

Alignments of two multiple-sequence alignments, or statistical models of such alignments (profiles), have important applications in computational biology. The increased amount of information in a profile versus a single sequence can lead to more accurate alignments and more sensitive homolog detection in database searches. Several profile-profile alignment methods have been proposed and have been shown to improve sensitivity and alignment quality compared with sequence-sequence methods (such as BLAST) and profile-sequence methods (e.g. PSI-BLAST). Here we present a new approach to profile-profile alignment we call Comparison of Alignments by Constructing Hidden Markov Models (HMMs) (COACH). COACH aligns two multiple sequence alignments by constructing a profile HMM from one alignment and aligning the other to that HMM. We compare the alignment accuracy of COACH with two recently published methods: Yona and Levitt's prof_sim and Sadreyev and Grishin's COMPASS. On two sets of reference alignments selected from the FSSP database, we find that COACH is able, on average, to produce alignments giving the best coverage or the fewest errors, depending on the chosen parameter settings. COACH is freely available from www.drive5.com/lobster

Capturing the state transitions of seizure-like events using Hidden Markov models.

PubMed

Guirgis, Mirna; Serletis, Demitre; Carlen, Peter L; Bardakjian, Berj L

2011-01-01

The purpose of this study was to investigate the number of states present in the progression of a seizure-like event (SLE). Of particular interest is to determine if there are more than two clearly defined states, as this would suggest that there is a distinct state preceding an SLE. Whole-intact hippocampus from C57/BL mice was used to model epileptiform activity induced by the perfusion of a low Mg(2+)/high K(+) solution while extracellular field potentials were recorded from CA3 pyramidal neurons. Hidden Markov models (HMM) were used to model the state transitions of the recorded SLEs by incorporating various features of the Hilbert transform into the training algorithm; specifically, 2- and 3-state HMMs were explored. Although the 2-state model was able to distinguish between SLE and nonSLE behavior, it provided no improvements compared to visual inspection alone. However, the 3-state model was able to capture two distinct nonSLE states that visual inspection failed to discriminate. Moreover, by developing an HMM based system a priori knowledge of the state transitions was not required making this an ideal platform for seizure prediction algorithms.

Reductive evolution and the loss of PDC/PAS domains from the genus Staphylococcus

PubMed Central

2013-01-01

Background The Per-Arnt-Sim (PAS) domain represents a ubiquitous structural fold that is involved in bacterial sensing and adaptation systems, including several virulence related functions. Although PAS domains and the subclass of PhoQ-DcuS-CitA (PDC) domains have a common structure, there is limited amino acid sequence similarity. To gain greater insight into the evolution of PDC/PAS domains present in the bacterial kingdom and staphylococci in specific, the PDC/PAS domains from the genomic sequences of 48 bacteria, representing 5 phyla, were identified using the sensitive search method based on HMM-to-HMM comparisons (HHblits). Results A total of 1,007 PAS domains and 686 PDC domains distributed over 1,174 proteins were identified. For 28 Gram-positive bacteria, the distribution, organization, and molecular evolution of PDC/PAS domains were analyzed in greater detail, with a special emphasis on the genus Staphylococcus. Compared to other bacteria the staphylococci have relatively fewer proteins (6–9) containing PDC/PAS domains. As a general rule, the staphylococcal genomes examined in this study contain a core group of seven PDC/PAS domain-containing proteins consisting of WalK, SrrB, PhoR, ArlS, HssS, NreB, and GdpP. The exceptions to this rule are: 1) S. saprophyticus lacks the core NreB protein; 2) S. carnosus has two additional PAS domain containing proteins; 3) S. epidermidis, S. aureus, and S. pseudintermedius have an additional protein with two PDC domains that is predicted to code for a sensor histidine kinase; 4) S. lugdunensis has an additional PDC containing protein predicted to be a sensor histidine kinase. Conclusions This comprehensive analysis demonstrates that variation in PDC/PAS domains among bacteria has limited correlations to the genome size or pathogenicity; however, our analysis established that bacteria having a motile phase in their life cycle have significantly more PDC/PAS-containing proteins. In addition, our analysis revealed a tremendous amount of variation in the number of PDC/PAS-containing proteins within genera. This variation extended to the Staphylococcus genus, which had between 6 and 9 PDC/PAS proteins and some of these appear to be previously undescribed signaling proteins. This latter point is important because most staphylococcal proteins that contain PDC/PAS domains regulate virulence factor synthesis or antibiotic resistance. PMID:23902280

Reductive evolution and the loss of PDC/PAS domains from the genus Staphylococcus.

PubMed

Shah, Neethu; Gaupp, Rosmarie; Moriyama, Hideaki; Eskridge, Kent M; Moriyama, Etsuko N; Somerville, Greg A

2013-07-31

The Per-Arnt-Sim (PAS) domain represents a ubiquitous structural fold that is involved in bacterial sensing and adaptation systems, including several virulence related functions. Although PAS domains and the subclass of PhoQ-DcuS-CitA (PDC) domains have a common structure, there is limited amino acid sequence similarity. To gain greater insight into the evolution of PDC/PAS domains present in the bacterial kingdom and staphylococci in specific, the PDC/PAS domains from the genomic sequences of 48 bacteria, representing 5 phyla, were identified using the sensitive search method based on HMM-to-HMM comparisons (HHblits). A total of 1,007 PAS domains and 686 PDC domains distributed over 1,174 proteins were identified. For 28 Gram-positive bacteria, the distribution, organization, and molecular evolution of PDC/PAS domains were analyzed in greater detail, with a special emphasis on the genus Staphylococcus. Compared to other bacteria the staphylococci have relatively fewer proteins (6-9) containing PDC/PAS domains. As a general rule, the staphylococcal genomes examined in this study contain a core group of seven PDC/PAS domain-containing proteins consisting of WalK, SrrB, PhoR, ArlS, HssS, NreB, and GdpP. The exceptions to this rule are: 1) S. saprophyticus lacks the core NreB protein; 2) S. carnosus has two additional PAS domain containing proteins; 3) S. epidermidis, S. aureus, and S. pseudintermedius have an additional protein with two PDC domains that is predicted to code for a sensor histidine kinase; 4) S. lugdunensis has an additional PDC containing protein predicted to be a sensor histidine kinase. This comprehensive analysis demonstrates that variation in PDC/PAS domains among bacteria has limited correlations to the genome size or pathogenicity; however, our analysis established that bacteria having a motile phase in their life cycle have significantly more PDC/PAS-containing proteins. In addition, our analysis revealed a tremendous amount of variation in the number of PDC/PAS-containing proteins within genera. This variation extended to the Staphylococcus genus, which had between 6 and 9 PDC/PAS proteins and some of these appear to be previously undescribed signaling proteins. This latter point is important because most staphylococcal proteins that contain PDC/PAS domains regulate virulence factor synthesis or antibiotic resistance.

Engineering Geobacillus thermodenitrificans to introduce cellulolytic activity; expression of native and heterologous cellulase genes.

PubMed

Daas, Martinus J A; Nijsse, Bart; van de Weijer, Antonius H P; Groenendaal, Bart W A J; Janssen, Fons; van der Oost, John; van Kranenburg, Richard

2018-06-27

Consolidated bioprocessing (CBP) is a cost-effective approach for the conversion of lignocellulosic biomass to biofuels and biochemicals. The enzymatic conversion of cellulose to glucose requires the synergistic action of three types of enzymes: exoglucanases, endoglucanases and β-glucosidases. The thermophilic, hemicellulolytic Geobacillus thermodenitrificans T12 was shown to harbor desired features for CBP, although it lacks the desired endo and exoglucanases required for the conversion of cellulose. Here, we report the expression of both endoglucanase and exoglucanase encoding genes by G. thermodenitrificans T12, in an initial attempt to express cellulolytic enzymes that complement the enzymatic machinery of this strain. A metagenome screen was performed on 73 G. thermodenitrificans strains using HMM profiles of all known CAZy families that contain endo and/or exoglucanases. Two putative endoglucanases, GE39 and GE40, belonging to glucoside hydrolase family 5 (GH5) were isolated and expressed in both E. coli and G. thermodenitrificans T12. Structure modeling of GE39 revealed a folding similar to a GH5 exo-1,3-β-glucanase from S. cerevisiae. However, we determined GE39 to be a β-xylosidase having pronounced activity towards p-nitrophenyl-β-D-xylopyranoside. Structure modelling of GE40 revealed its protein architecture to be similar to a GH5 endoglucanase from B. halodurans, and its endoglucanase activity was confirmed by enzymatic activity against 2-hydroxyethylcellulose, carboxymethylcellulose and barley β-glucan. Additionally, we introduced expression constructs into T12 containing Geobacillus sp. 70PC53 endoglucanase gene celA and both endoglucanase genes (M1 and M2) from Geobacillus sp. WSUCF1. Finally, we introduced expression constructs into T12 containing the C. thermocellum exoglucanases celK and celS genes and the endoglucanase celC gene. We identified a novel G. thermodenitrificans β-xylosidase (GE39) and a novel endoglucanase (GE40) using a metagenome screen based on multiple HMM profiles. We successfully expressed both genes in E. coli and functionally expressed the GE40 endoglucanase in G. thermodenitrificans T12. Additionally, the heterologous production of active CelK, a C. thermocellum derived exoglucanase, and CelA, a Geobacillus derived endoglucanase, was demonstrated with strain T12. The native hemicellulolytic activity and the heterologous cellulolytic activity described in this research provide a good basis for the further development of G. thermodenitrificans T12 as a host for consolidated bioprocessing.

Motif discovery with data mining in 3D protein structure databases: discovery, validation and prediction of the U-shape zinc binding ("Huf-Zinc") motif.

PubMed

Maurer-Stroh, Sebastian; Gao, He; Han, Hao; Baeten, Lies; Schymkowitz, Joost; Rousseau, Frederic; Zhang, Louxin; Eisenhaber, Frank

2013-02-01

Data mining in protein databases, derivatives from more fundamental protein 3D structure and sequence databases, has considerable unearthed potential for the discovery of sequence motif--structural motif--function relationships as the finding of the U-shape (Huf-Zinc) motif, originally a small student's project, exemplifies. The metal ion zinc is critically involved in universal biological processes, ranging from protein-DNA complexes and transcription regulation to enzymatic catalysis and metabolic pathways. Proteins have evolved a series of motifs to specifically recognize and bind zinc ions. Many of these, so called zinc fingers, are structurally independent globular domains with discontinuous binding motifs made up of residues mostly far apart in sequence. Through a systematic approach starting from the BRIX structure fragment database, we discovered that there exists another predictable subset of zinc-binding motifs that not only have a conserved continuous sequence pattern but also share a characteristic local conformation, despite being included in totally different overall folds. While this does not allow general prediction of all Zn binding motifs, a HMM-based web server, Huf-Zinc, is available for prediction of these novel, as well as conventional, zinc finger motifs in protein sequences. The Huf-Zinc webserver can be freely accessed through this URL (http://mendel.bii.a-star.edu.sg/METHODS/hufzinc/).

On the Tradeoff Between Altruism and Selfishness in MANET Trust Management

DTIC Science & Technology

2016-04-07

to discourage selfish behaviors, using a hidden Markov model (HMM) to quanti - tatively measure the trustworthiness of nodes. Adams et al. [18...based reliability metric to predict trust-based system survivability. Section 4 analyzes numerical results obtained through the evaluation of our SPN...concepts in MANETs, trust man- agement for MANETs should consider the following design features: trust metrics must be customizable, evaluation of

Feasibility Study for Hotel/Motel Career Program for Harper College. Volume XIX, No. 1.

ERIC Educational Resources Information Center

Lucas, John A.; And Others

In spring 1990, a study was conducted at William Rainey Harper College (WRHC) to determine the feasibility of adding a career program in Hotel/Motel Management (HMM) to the current Food Service Program. Surveys were sent to 53 hotels and motels in the WRHC service area to determine employment demands that would affect the hiring of graduates of…

Effectiveness of artemether-lumefantrine provided by community health workers in under-five children with uncomplicated malaria in rural Tanzania: an open label prospective study.

PubMed

Ngasala, Billy E; Malmberg, Maja; Carlsson, Anja M; Ferreira, Pedro E; Petzold, Max G; Blessborn, Daniel; Bergqvist, Yngve; Gil, José P; Premji, Zul; Mårtensson, Andreas

2011-03-16

Home-management of malaria (HMM) strategy improves early access of anti-malarial medicines to high-risk groups in remote areas of sub-Saharan Africa. However, limited data are available on the effectiveness of using artemisinin-based combination therapy (ACT) within the HMM strategy. The aim of this study was to assess the effectiveness of artemether-lumefantrine (AL), presently the most favoured ACT in Africa, in under-five children with uncomplicated Plasmodium falciparum malaria in Tanzania, when provided by community health workers (CHWs) and administered unsupervised by parents or guardians at home. An open label, single arm prospective study was conducted in two rural villages with high malaria transmission in Kibaha District, Tanzania. Children presenting to CHWs with uncomplicated fever and a positive rapid malaria diagnostic test (RDT) were provisionally enrolled and provided AL for unsupervised treatment at home. Patients with microscopy confirmed P. falciparum parasitaemia were definitely enrolled and reviewed weekly by the CHWs during 42 days. Primary outcome measure was PCR corrected parasitological cure rate by day 42, as estimated by Kaplan-Meier survival analysis. This trial is registered with ClinicalTrials.gov, number NCT00454961. A total of 244 febrile children were enrolled between March-August 2007. Two patients were lost to follow up on day 14, and one patient withdrew consent on day 21. Some 141/241 (58.5%) patients had recurrent infection during follow-up, of whom 14 had recrudescence. The PCR corrected cure rate by day 42 was 93.0% (95% CI 88.3%-95.9%). The median lumefantrine concentration was statistically significantly lower in patients with recrudescence (97 ng/mL [IQR 0-234]; n = 10) compared with reinfections (205 ng/mL [114-390]; n = 92), or no parasite reappearance (217 [121-374] ng/mL; n = 70; p ≤ 0.046). Provision of AL by CHWs for unsupervised malaria treatment at home was highly effective, which provides evidence base for scaling-up implementation of HMM with AL in Tanzania.

Fast and accurate imputation of summary statistics enhances evidence of functional enrichment

PubMed Central

Pasaniuc, Bogdan; Zaitlen, Noah; Shi, Huwenbo; Bhatia, Gaurav; Gusev, Alexander; Pickrell, Joseph; Hirschhorn, Joel; Strachan, David P.; Patterson, Nick; Price, Alkes L.

2014-01-01

Motivation: Imputation using external reference panels (e.g. 1000 Genomes) is a widely used approach for increasing power in genome-wide association studies and meta-analysis. Existing hidden Markov models (HMM)-based imputation approaches require individual-level genotypes. Here, we develop a new method for Gaussian imputation from summary association statistics, a type of data that is becoming widely available. Results: In simulations using 1000 Genomes (1000G) data, this method recovers 84% (54%) of the effective sample size for common (>5%) and low-frequency (1–5%) variants [increasing to 87% (60%) when summary linkage disequilibrium information is available from target samples] versus the gold standard of 89% (67%) for HMM-based imputation, which cannot be applied to summary statistics. Our approach accounts for the limited sample size of the reference panel, a crucial step to eliminate false-positive associations, and it is computationally very fast. As an empirical demonstration, we apply our method to seven case–control phenotypes from the Wellcome Trust Case Control Consortium (WTCCC) data and a study of height in the British 1958 birth cohort (1958BC). Gaussian imputation from summary statistics recovers 95% (105%) of the effective sample size (as quantified by the ratio of χ2 association statistics) compared with HMM-based imputation from individual-level genotypes at the 227 (176) published single nucleotide polymorphisms (SNPs) in the WTCCC (1958BC height) data. In addition, for publicly available summary statistics from large meta-analyses of four lipid traits, we publicly release imputed summary statistics at 1000G SNPs, which could not have been obtained using previously published methods, and demonstrate their accuracy by masking subsets of the data. We show that 1000G imputation using our approach increases the magnitude and statistical evidence of enrichment at genic versus non-genic loci for these traits, as compared with an analysis without 1000G imputation. Thus, imputation of summary statistics will be a valuable tool in future functional enrichment analyses. Availability and implementation: Publicly available software package available at http://bogdan.bioinformatics.ucla.edu/software/. Contact: bpasaniuc@mednet.ucla.edu or aprice@hsph.harvard.edu Supplementary information: Supplementary materials are available at Bioinformatics online. PMID:24990607

Web of Objects Based Ambient Assisted Living Framework for Emergency Psychiatric State Prediction

PubMed Central

Alam, Md Golam Rabiul; Abedin, Sarder Fakhrul; Al Ameen, Moshaddique; Hong, Choong Seon

2016-01-01

Ambient assisted living can facilitate optimum health and wellness by aiding physical, mental and social well-being. In this paper, patients’ psychiatric symptoms are collected through lightweight biosensors and web-based psychiatric screening scales in a smart home environment and then analyzed through machine learning algorithms to provide ambient intelligence in a psychiatric emergency. The psychiatric states are modeled through a Hidden Markov Model (HMM), and the model parameters are estimated using a Viterbi path counting and scalable Stochastic Variational Inference (SVI)-based training algorithm. The most likely psychiatric state sequence of the corresponding observation sequence is determined, and an emergency psychiatric state is predicted through the proposed algorithm. Moreover, to enable personalized psychiatric emergency care, a service a web of objects-based framework is proposed for a smart-home environment. In this framework, the biosensor observations and the psychiatric rating scales are objectified and virtualized in the web space. Then, the web of objects of sensor observations and psychiatric rating scores are used to assess the dweller’s mental health status and to predict an emergency psychiatric state. The proposed psychiatric state prediction algorithm reported 83.03 percent prediction accuracy in an empirical performance study. PMID:27608023

Short text sentiment classification based on feature extension and ensemble classifier

NASA Astrophysics Data System (ADS)

Liu, Yang; Zhu, Xie

2018-05-01

With the rapid development of Internet social media, excavating the emotional tendencies of the short text information from the Internet, the acquisition of useful information has attracted the attention of researchers. At present, the commonly used can be attributed to the rule-based classification and statistical machine learning classification methods. Although micro-blog sentiment analysis has made good progress, there still exist some shortcomings such as not highly accurate enough and strong dependence from sentiment classification effect. Aiming at the characteristics of Chinese short texts, such as less information, sparse features, and diverse expressions, this paper considers expanding the original text by mining related semantic information from the reviews, forwarding and other related information. First, this paper uses Word2vec to compute word similarity to extend the feature words. And then uses an ensemble classifier composed of SVM, KNN and HMM to analyze the emotion of the short text of micro-blog. The experimental results show that the proposed method can make good use of the comment forwarding information to extend the original features. Compared with the traditional method, the accuracy, recall and F1 value obtained by this method have been improved.

Sarment: Python modules for HMM analysis and partitioning of sequences.

PubMed

Guéguen, Laurent

2005-08-15

Sarment is a package of Python modules for easy building and manipulation of sequence segmentations. It provides efficient implementation of usual algorithms for hidden Markov Model computation, as well as for maximal predictive partitioning. Owing to its very large variety of criteria for computing segmentations, Sarment can handle many kinds of models. Because of object-oriented programming, the results of the segmentation are very easy tomanipulate.

Reactions of Free Radicals with Nitro-Compounds and Nitrates

DTIC Science & Technology

1981-03-31

PAGE(I/hmm a•Ia ntatemd the fragment derived from the nitrates but not from the nitro-compounds could undergo exothermic rearrangement. Product analyses...compounds could undergo exothermic rearrangement. Product analyses and computer modelling were undertaken, these provided a clear explanation of why the...Nitrate 14 Reaction of Oxygen Atoms with Nitromethane 16 Reaction of Oxygen Atoms with Nitroethane 17 Products from Nitrocompounds 18 Effect of Carbon

A method of hidden Markov model optimization for use with geophysical data sets

NASA Technical Reports Server (NTRS)

Granat, R. A.

2003-01-01

Geophysics research has been faced with a growing need for automated techniques with which to process large quantities of data. A successful tool must meet a number of requirements: it should be consistent, require minimal parameter tuning, and produce scientifically meaningful results in reasonable time. We introduce a hidden Markov model (HMM)-based method for analysis of geophysical data sets that attempts to address these issues.

Three Dimensional Object Recognition Using a Complex Autoregressive Model

DTIC Science & Technology

1993-12-01

3.4.2 Template Matching Algorithm ...................... 3-16 3.4.3 K-Nearest-Neighbor ( KNN ) Techniques ................. 3-25 3.4.4 Hidden Markov Model...Neighbor ( KNN ) Test Results ...................... 4-13 4.2.1 Single-Look 1-NN Testing .......................... 4-14 4.2.2 Multiple-Look 1-NN Testing...4-15 4.2.3 Discussion of KNN Test Results ...................... 4-15 4.3 Hidden Markov Model (HMM) Test Results

Query-seeded iterative sequence similarity searching improves selectivity 5–20-fold

PubMed Central

Li, Weizhong; Lopez, Rodrigo

2017-01-01

Abstract Iterative similarity search programs, like psiblast, jackhmmer, and psisearch, are much more sensitive than pairwise similarity search methods like blast and ssearch because they build a position specific scoring model (a PSSM or HMM) that captures the pattern of sequence conservation characteristic to a protein family. But models are subject to contamination; once an unrelated sequence has been added to the model, homologs of the unrelated sequence will also produce high scores, and the model can diverge from the original protein family. Examination of alignment errors during psiblast PSSM contamination suggested a simple strategy for dramatically reducing PSSM contamination. psiblast PSSMs are built from the query-based multiple sequence alignment (MSA) implied by the pairwise alignments between the query model (PSSM, HMM) and the subject sequences in the library. When the original query sequence residues are inserted into gapped positions in the aligned subject sequence, the resulting PSSM rarely produces alignment over-extensions or alignments to unrelated sequences. This simple step, which tends to anchor the PSSM to the original query sequence and slightly increase target percent identity, can reduce the frequency of false-positive alignments more than 20-fold compared with psiblast and jackhmmer, with little loss in search sensitivity. PMID:27923999

Electrooculography-based continuous eye-writing recognition system for efficient assistive communication systems

PubMed Central

Shinozaki, Takahiro

2018-01-01

Human-computer interface systems whose input is based on eye movements can serve as a means of communication for patients with locked-in syndrome. Eye-writing is one such system; users can input characters by moving their eyes to follow the lines of the strokes corresponding to characters. Although this input method makes it easy for patients to get started because of their familiarity with handwriting, existing eye-writing systems suffer from slow input rates because they require a pause between input characters to simplify the automatic recognition process. In this paper, we propose a continuous eye-writing recognition system that achieves a rapid input rate because it accepts characters eye-written continuously, with no pauses. For recognition purposes, the proposed system first detects eye movements using electrooculography (EOG), and then a hidden Markov model (HMM) is applied to model the EOG signals and recognize the eye-written characters. Additionally, this paper investigates an EOG adaptation that uses a deep neural network (DNN)-based HMM. Experiments with six participants showed an average input speed of 27.9 character/min using Japanese Katakana as the input target characters. A Katakana character-recognition error rate of only 5.0% was achieved using 13.8 minutes of adaptation data. PMID:29425248

Hidden Markov models reveal complexity in the diving behaviour of short-finned pilot whales

PubMed Central

Quick, Nicola J.; Isojunno, Saana; Sadykova, Dina; Bowers, Matthew; Nowacek, Douglas P.; Read, Andrew J.

2017-01-01

Diving behaviour of short-finned pilot whales is often described by two states; deep foraging and shallow, non-foraging dives. However, this simple classification system ignores much of the variation that occurs during subsurface periods. We used multi-state hidden Markov models (HMM) to characterize states of diving behaviour and the transitions between states in short-finned pilot whales. We used three parameters (number of buzzes, maximum dive depth and duration) measured in 259 dives by digital acoustic recording tags (DTAGs) deployed on 20 individual whales off Cape Hatteras, North Carolina, USA. The HMM identified a four-state model as the best descriptor of diving behaviour. The state-dependent distributions for the diving parameters showed variation between states, indicative of different diving behaviours. Transition probabilities were considerably higher for state persistence than state switching, indicating that dive types occurred in bouts. Our results indicate that subsurface behaviour in short-finned pilot whales is more complex than a simple dichotomy of deep and shallow diving states, and labelling all subsurface behaviour as deep dives or shallow dives discounts a significant amount of important variation. We discuss potential drivers of these patterns, including variation in foraging success, prey availability and selection, bathymetry, physiological constraints and socially mediated behaviour. PMID:28361954

Histogram equalization with Bayesian estimation for noise robust speech recognition.

PubMed

Suh, Youngjoo; Kim, Hoirin

2018-02-01

The histogram equalization approach is an efficient feature normalization technique for noise robust automatic speech recognition. However, it suffers from performance degradation when some fundamental conditions are not satisfied in the test environment. To remedy these limitations of the original histogram equalization methods, class-based histogram equalization approach has been proposed. Although this approach showed substantial performance improvement under noise environments, it still suffers from performance degradation due to the overfitting problem when test data are insufficient. To address this issue, the proposed histogram equalization technique employs the Bayesian estimation method in the test cumulative distribution function estimation. It was reported in a previous study conducted on the Aurora-4 task that the proposed approach provided substantial performance gains in speech recognition systems based on the acoustic modeling of the Gaussian mixture model-hidden Markov model. In this work, the proposed approach was examined in speech recognition systems with deep neural network-hidden Markov model (DNN-HMM), the current mainstream speech recognition approach where it also showed meaningful performance improvement over the conventional maximum likelihood estimation-based method. The fusion of the proposed features with the mel-frequency cepstral coefficients provided additional performance gains in DNN-HMM systems, which otherwise suffer from performance degradation in the clean test condition.

N-substituted methyl maleamates as larvicidal compounds against Aedes aegypti (Diptera: Culicidae).

PubMed

Harburguer, Laura; Gonzalez, Paula V; Gonzalez Audino, Paola; Zerba, Eduardo; Masuh, Héctor

2018-02-01

Severe human arboviral diseases can be transmitted by the mosquito Aedes aegypti (L.), including dengue, chikungunya, zika, and yellow fever. The use of larvicides in containers that can result as potential breeding places and cannot be eliminated is the main alternative in control programs. However, their continuous and widespread use caused an increase in insecticide-resistant populations of this mosquito. The aim of this study was to evaluate the effect of three N-substituted methyl maleamates as larvicides on Ae. aegypti, the N-propyl methyl maleamate (PMM), N-butyl methyl maleamate (BMM), and N-hexyl methyl maleamate (HMM). These compounds could have a different mode of action from those larvicides known so far. We evaluated the larva mortality after 1 and 24 h of exposure and we found that mortality was fast and occurs within the first 60 min. HMM was slightly more effective with LC 50 values of 0.7 and 0.3 ppm for 1 and 24 h of exposure and LC 95 of 11 and 3 ppm. Our results demonstrate that N-substituted methyl maleamates have insecticidal properties for the control of Ae. aegypti larvae. These compounds could become useful alternatives to traditional larvicides after studying their insecticidal mechanism as well as their toxicity towards non target organisms.

Estimation of the biphasic property in a female's menstrual cycle from cutaneous temperature measured during sleep.

PubMed

Chen, Wenxi; Kitazawa, Masumi; Togawa, Tatsuo

2009-09-01

This paper proposes a method to estimate a woman's menstrual cycle based on the hidden Markov model (HMM). A tiny device was developed that attaches around the abdominal region to measure cutaneous temperature at 10-min intervals during sleep. The measured temperature data were encoded as a two-dimensional image (QR code, i.e., quick response code) and displayed in the LCD window of the device. A mobile phone captured the QR code image, decoded the information and transmitted the data to a database server. The collected data were analyzed by three steps to estimate the biphasic temperature property in a menstrual cycle. The key step was an HMM-based step between preprocessing and postprocessing. A discrete Markov model, with two hidden phases, was assumed to represent higher- and lower-temperature phases during a menstrual cycle. The proposed method was verified by the data collected from 30 female participants, aged from 14 to 46, over six consecutive months. By comparing the estimated results with individual records from the participants, 71.6% of 190 menstrual cycles were correctly estimated. The sensitivity and positive predictability were 91.8 and 96.6%, respectively. This objective evaluation provides a promising approach for managing premenstrual syndrome and birth control.

HMM-based lexicon-driven and lexicon-free word recognition for online handwritten Indic scripts.

PubMed

Bharath, A; Madhvanath, Sriganesh

2012-04-01

Research for recognizing online handwritten words in Indic scripts is at its early stages when compared to Latin and Oriental scripts. In this paper, we address this problem specifically for two major Indic scripts--Devanagari and Tamil. In contrast to previous approaches, the techniques we propose are largely data driven and script independent. We propose two different techniques for word recognition based on Hidden Markov Models (HMM): lexicon driven and lexicon free. The lexicon-driven technique models each word in the lexicon as a sequence of symbol HMMs according to a standard symbol writing order derived from the phonetic representation. The lexicon-free technique uses a novel Bag-of-Symbols representation of the handwritten word that is independent of symbol order and allows rapid pruning of the lexicon. On handwritten Devanagari word samples featuring both standard and nonstandard symbol writing orders, a combination of lexicon-driven and lexicon-free recognizers significantly outperforms either of them used in isolation. In contrast, most Tamil word samples feature the standard symbol order, and the lexicon-driven recognizer outperforms the lexicon free one as well as their combination. The best recognition accuracies obtained for 20,000 word lexicons are 87.13 percent for Devanagari when the two recognizers are combined, and 91.8 percent for Tamil using the lexicon-driven technique.

UUCD: a family-based database of ubiquitin and ubiquitin-like conjugation.

PubMed

Gao, Tianshun; Liu, Zexian; Wang, Yongbo; Cheng, Han; Yang, Qing; Guo, Anyuan; Ren, Jian; Xue, Yu

2013-01-01

In this work, we developed a family-based database of UUCD (http://uucd.biocuckoo.org) for ubiquitin and ubiquitin-like conjugation, which is one of the most important post-translational modifications responsible for regulating a variety of cellular processes, through a similar E1 (ubiquitin-activating enzyme)-E2 (ubiquitin-conjugating enzyme)-E3 (ubiquitin-protein ligase) enzyme thioester cascade. Although extensive experimental efforts have been taken, an integrative data resource is still not available. From the scientific literature, 26 E1s, 105 E2s, 1003 E3s and 148 deubiquitination enzymes (DUBs) were collected and classified into 1, 3, 19 and 7 families, respectively. To computationally characterize potential enzymes in eukaryotes, we constructed 1, 1, 15 and 6 hidden Markov model (HMM) profiles for E1s, E2s, E3s and DUBs at the family level, separately. Moreover, the ortholog searches were conducted for E3 and DUB families without HMM profiles. Then the UUCD database was developed with 738 E1s, 2937 E2s, 46 631 E3s and 6647 DUBs of 70 eukaryotic species. The detailed annotations and classifications were also provided. The online service of UUCD was implemented in PHP + MySQL + JavaScript + Perl.

Spectral identification of sperm whales from Littoral Acoustic Demonstration Center passive acoustic recordings

NASA Astrophysics Data System (ADS)

Sidorovskaia, Natalia A.; Richard, Blake; Ioup, George E.; Ioup, Juliette W.

2005-09-01

The Littoral Acoustic Demonstration Center (LADC) made a series of passive broadband acoustic recordings in the Gulf of Mexico and Ligurian Sea to study noise and marine mammal phonations. The collected data contain a large amount of various types of sperm whale phonations, such as isolated clicks and communication codas. It was previously reported that the spectrograms of the extracted clicks and codas contain well-defined null patterns that seem to be unique for individuals. The null pattern is formed due to individual features of the sound production organs of an animal. These observations motivated the present studies of adapting human speech identification techniques for deep-diving marine mammal phonations. A three-state trained hidden Markov model (HMM) was used with the phonation spectra of sperm whales. The HHM-algorithm gave 75% accuracy in identifying individuals when it had been initially tested for the acoustic data set correlated with visual observations of sperm whales. A comparison of the identification accuracy based on null-pattern similarity analysis and the HMM-algorithm is presented. The results can establish the foundation for developing an acoustic identification database for sperm whales and possibly other deep-diving marine mammals that would be difficult to observe visually. [Research supported by ONR.

Inference of Functionally-Relevant N-acetyltransferase Residues Based on Statistical Correlations.

PubMed

Neuwald, Andrew F; Altschul, Stephen F

2016-12-01

Over evolutionary time, members of a superfamily of homologous proteins sharing a common structural core diverge into subgroups filling various functional niches. At the sequence level, such divergence appears as correlations that arise from residue patterns distinct to each subgroup. Such a superfamily may be viewed as a population of sequences corresponding to a complex, high-dimensional probability distribution. Here we model this distribution as hierarchical interrelated hidden Markov models (hiHMMs), which describe these sequence correlations implicitly. By characterizing such correlations one may hope to obtain information regarding functionally-relevant properties that have thus far evaded detection. To do so, we infer a hiHMM distribution from sequence data using Bayes' theorem and Markov chain Monte Carlo (MCMC) sampling, which is widely recognized as the most effective approach for characterizing a complex, high dimensional distribution. Other routines then map correlated residue patterns to available structures with a view to hypothesis generation. When applied to N-acetyltransferases, this reveals sequence and structural features indicative of functionally important, yet generally unknown biochemical properties. Even for sets of proteins for which nothing is known beyond unannotated sequences and structures, this can lead to helpful insights. We describe, for example, a putative coenzyme-A-induced-fit substrate binding mechanism mediated by arginine residue switching between salt bridge and π-π stacking interactions. A suite of programs implementing this approach is available (psed.igs.umaryland.edu).

HMM and Auction-based Formulations of ISR Coordination Mechanisms for the Expeditionary Strike Group Missions

DTIC Science & Technology

2009-06-01

CA 93943, United States kemple@nps.edu * To whom correspondence should be addressed: krishna@engr.uconn.edu 1 Report Documentation Page Form...ApprovedOMB No. 0704-0188 Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for...burden, to Washington Headquarters Services, Directorate for Information Operations and Reports , 1215 Jefferson Davis Highway, Suite 1204, Arlington

A Multidisciplinary Approach to Health Monitoring and Materials Damage Prognosis for Metallic Aerospace Systems

DTIC Science & Technology

2013-03-01

framework of orientation distribution functions and crack-induced texture o Quantify effects of temperature on damage behavior and damage monitoring...measurement model was obtained from hidden Markov modeling (HMM) of joint time-frequency (TF) features extracted from the PZT sensor signals using the...considered PZT sensor signals recorded from a bolted aluminum plate. About only 20% of the samples of a signal were first randomly selected as

Context-Sensitive Markov Models for Peptide Scoring and Identification from Tandem Mass Spectrometry

PubMed Central

Grover, Himanshu; Wallstrom, Garrick; Wu, Christine C.

2013-01-01

Abstract Peptide and protein identification via tandem mass spectrometry (MS/MS) lies at the heart of proteomic characterization of biological samples. Several algorithms are able to search, score, and assign peptides to large MS/MS datasets. Most popular methods, however, underutilize the intensity information available in the tandem mass spectrum due to the complex nature of the peptide fragmentation process, thus contributing to loss of potential identifications. We present a novel probabilistic scoring algorithm called Context-Sensitive Peptide Identification (CSPI) based on highly flexible Input-Output Hidden Markov Models (IO-HMM) that capture the influence of peptide physicochemical properties on their observed MS/MS spectra. We use several local and global properties of peptides and their fragment ions from literature. Comparison with two popular algorithms, Crux (re-implementation of SEQUEST) and X!Tandem, on multiple datasets of varying complexity, shows that peptide identification scores from our models are able to achieve greater discrimination between true and false peptides, identifying up to ∼25% more peptides at a False Discovery Rate (FDR) of 1%. We evaluated two alternative normalization schemes for fragment ion-intensities, a global rank-based and a local window-based. Our results indicate the importance of appropriate normalization methods for learning superior models. Further, combining our scores with Crux using a state-of-the-art procedure, Percolator, we demonstrate the utility of using scoring features from intensity-based models, identifying ∼4-8 % additional identifications over Percolator at 1% FDR. IO-HMMs offer a scalable and flexible framework with several modeling choices to learn complex patterns embedded in MS/MS data. PMID:23289783

Improved orthologous databases to ease protozoan targets inference.

PubMed

Kotowski, Nelson; Jardim, Rodrigo; Dávila, Alberto M R

2015-09-29

Homology inference helps on identifying similarities, as well as differences among organisms, which provides a better insight on how closely related one might be to another. In addition, comparative genomics pipelines are widely adopted tools designed using different bioinformatics applications and algorithms. In this article, we propose a methodology to build improved orthologous databases with the potential to aid on protozoan target identification, one of the many tasks which benefit from comparative genomics tools. Our analyses are based on OrthoSearch, a comparative genomics pipeline originally designed to infer orthologs through protein-profile comparison, supported by an HMM, reciprocal best hits based approach. Our methodology allows OrthoSearch to confront two orthologous databases and to generate an improved new one. Such can be later used to infer potential protozoan targets through a similarity analysis against the human genome. The protein sequences of Cryptosporidium hominis, Entamoeba histolytica and Leishmania infantum genomes were comparatively analyzed against three orthologous databases: (i) EggNOG KOG, (ii) ProtozoaDB and (iii) Kegg Orthology (KO). That allowed us to create two new orthologous databases, "KO + EggNOG KOG" and "KO + EggNOG KOG + ProtozoaDB", with 16,938 and 27,701 orthologous groups, respectively. Such new orthologous databases were used for a regular OrthoSearch run. By confronting "KO + EggNOG KOG" and "KO + EggNOG KOG + ProtozoaDB" databases and protozoan species we were able to detect the following total of orthologous groups and coverage (relation between the inferred orthologous groups and the species total number of proteins): Cryptosporidium hominis: 1,821 (11 %) and 3,254 (12 %); Entamoeba histolytica: 2,245 (13 %) and 5,305 (19 %); Leishmania infantum: 2,702 (16 %) and 4,760 (17 %). Using our HMM-based methodology and the largest created orthologous database, it was possible to infer 13 orthologous groups which represent potential protozoan targets; these were found because of our distant homology approach. We also provide the number of species-specific, pair-to-pair and core groups from such analyses, depicted in Venn diagrams. The orthologous databases generated by our HMM-based methodology provide a broader dataset, with larger amounts of orthologous groups when compared to the original databases used as input. Those may be used for several homology inference analyses, annotation tasks and protozoan targets identification.

An effective approach for annotation of protein families with low sequence similarity and conserved motifs: identifying GDSL hydrolases across the plant kingdom.

PubMed

Vujaklija, Ivan; Bielen, Ana; Paradžik, Tina; Biđin, Siniša; Goldstein, Pavle; Vujaklija, Dušica

2016-02-18

The massive accumulation of protein sequences arising from the rapid development of high-throughput sequencing, coupled with automatic annotation, results in high levels of incorrect annotations. In this study, we describe an approach to decrease annotation errors of protein families characterized by low overall sequence similarity. The GDSL lipolytic family comprises proteins with multifunctional properties and high potential for pharmaceutical and industrial applications. The number of proteins assigned to this family has increased rapidly over the last few years. In particular, the natural abundance of GDSL enzymes reported recently in plants indicates that they could be a good source of novel GDSL enzymes. We noticed that a significant proportion of annotated sequences lack specific GDSL motif(s) or catalytic residue(s). Here, we applied motif-based sequence analyses to identify enzymes possessing conserved GDSL motifs in selected proteomes across the plant kingdom. Motif-based HMM scanning (Viterbi decoding-VD and posterior decoding-PD) and the here described PD/VD protocol were successfully applied on 12 selected plant proteomes to identify sequences with GDSL motifs. A significant number of identified GDSL sequences were novel. Moreover, our scanning approach successfully detected protein sequences lacking at least one of the essential motifs (171/820) annotated by Pfam profile search (PfamA) as GDSL. Based on these analyses we provide a curated list of GDSL enzymes from the selected plants. CLANS clustering and phylogenetic analysis helped us to gain a better insight into the evolutionary relationship of all identified GDSL sequences. Three novel GDSL subfamilies as well as unreported variations in GDSL motifs were discovered in this study. In addition, analyses of selected proteomes showed a remarkable expansion of GDSL enzymes in the lycophyte, Selaginella moellendorffii. Finally, we provide a general motif-HMM scanner which is easily accessible through the graphical user interface ( http://compbio.math.hr/ ). Our results show that scanning with a carefully parameterized motif-HMM is an effective approach for annotation of protein families with low sequence similarity and conserved motifs. The results of this study expand current knowledge and provide new insights into the evolution of the large GDSL-lipase family in land plants.

Fast and accurate imputation of summary statistics enhances evidence of functional enrichment.

PubMed

Pasaniuc, Bogdan; Zaitlen, Noah; Shi, Huwenbo; Bhatia, Gaurav; Gusev, Alexander; Pickrell, Joseph; Hirschhorn, Joel; Strachan, David P; Patterson, Nick; Price, Alkes L

2014-10-15

Imputation using external reference panels (e.g. 1000 Genomes) is a widely used approach for increasing power in genome-wide association studies and meta-analysis. Existing hidden Markov models (HMM)-based imputation approaches require individual-level genotypes. Here, we develop a new method for Gaussian imputation from summary association statistics, a type of data that is becoming widely available. In simulations using 1000 Genomes (1000G) data, this method recovers 84% (54%) of the effective sample size for common (>5%) and low-frequency (1-5%) variants [increasing to 87% (60%) when summary linkage disequilibrium information is available from target samples] versus the gold standard of 89% (67%) for HMM-based imputation, which cannot be applied to summary statistics. Our approach accounts for the limited sample size of the reference panel, a crucial step to eliminate false-positive associations, and it is computationally very fast. As an empirical demonstration, we apply our method to seven case-control phenotypes from the Wellcome Trust Case Control Consortium (WTCCC) data and a study of height in the British 1958 birth cohort (1958BC). Gaussian imputation from summary statistics recovers 95% (105%) of the effective sample size (as quantified by the ratio of [Formula: see text] association statistics) compared with HMM-based imputation from individual-level genotypes at the 227 (176) published single nucleotide polymorphisms (SNPs) in the WTCCC (1958BC height) data. In addition, for publicly available summary statistics from large meta-analyses of four lipid traits, we publicly release imputed summary statistics at 1000G SNPs, which could not have been obtained using previously published methods, and demonstrate their accuracy by masking subsets of the data. We show that 1000G imputation using our approach increases the magnitude and statistical evidence of enrichment at genic versus non-genic loci for these traits, as compared with an analysis without 1000G imputation. Thus, imputation of summary statistics will be a valuable tool in future functional enrichment analyses. Publicly available software package available at http://bogdan.bioinformatics.ucla.edu/software/. bpasaniuc@mednet.ucla.edu or aprice@hsph.harvard.edu Supplementary materials are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

HMM Sequential Hypothesis Tests for Intrusion Detection in MANETs Extended Abstract

DTIC Science & Technology

2003-01-01

securing the routing protocols of mobile ad hoc wireless net- works has been done in prevention. Intrusion detection systems play a complimentary...TERMS 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF PAGES 10 19a. NAME OF RESPONSIBLE PERSON a. REPORT unclassified...hops of A would be unable to communicate with B and vice versa [1]. 1.2 The role of intrusion detection in security In order to provide reliable

miRvestigator: web application to identify miRNAs responsible for co-regulated gene expression patterns discovered through transcriptome profiling.

PubMed

Plaisier, Christopher L; Bare, J Christopher; Baliga, Nitin S

2011-07-01

Transcriptome profiling studies have produced staggering numbers of gene co-expression signatures for a variety of biological systems. A significant fraction of these signatures will be partially or fully explained by miRNA-mediated targeted transcript degradation. miRvestigator takes as input lists of co-expressed genes from Caenorhabditis elegans, Drosophila melanogaster, G. gallus, Homo sapiens, Mus musculus or Rattus norvegicus and identifies the specific miRNAs that are likely to bind to 3' un-translated region (UTR) sequences to mediate the observed co-regulation. The novelty of our approach is the miRvestigator hidden Markov model (HMM) algorithm which systematically computes a similarity P-value for each unique miRNA seed sequence from the miRNA database miRBase to an overrepresented sequence motif identified within the 3'-UTR of the query genes. We have made this miRNA discovery tool accessible to the community by integrating our HMM algorithm with a proven algorithm for de novo discovery of miRNA seed sequences and wrapping these algorithms into a user-friendly interface. Additionally, the miRvestigator web server also produces a list of putative miRNA binding sites within 3'-UTRs of the query transcripts to facilitate the design of validation experiments. The miRvestigator is freely available at http://mirvestigator.systemsbiology.net.

Ascertainment-adjusted parameter estimation approach to improve robustness against misspecification of health monitoring methods

NASA Astrophysics Data System (ADS)

Juesas, P.; Ramasso, E.

2016-12-01

Condition monitoring aims at ensuring system safety which is a fundamental requirement for industrial applications and that has become an inescapable social demand. This objective is attained by instrumenting the system and developing data analytics methods such as statistical models able to turn data into relevant knowledge. One difficulty is to be able to correctly estimate the parameters of those methods based on time-series data. This paper suggests the use of the Weighted Distribution Theory together with the Expectation-Maximization algorithm to improve parameter estimation in statistical models with latent variables with an application to health monotonic under uncertainty. The improvement of estimates is made possible by incorporating uncertain and possibly noisy prior knowledge on latent variables in a sound manner. The latent variables are exploited to build a degradation model of dynamical system represented as a sequence of discrete states. Examples on Gaussian Mixture Models, Hidden Markov Models (HMM) with discrete and continuous outputs are presented on both simulated data and benchmarks using the turbofan engine datasets. A focus on the application of a discrete HMM to health monitoring under uncertainty allows to emphasize the interest of the proposed approach in presence of different operating conditions and fault modes. It is shown that the proposed model depicts high robustness in presence of noisy and uncertain prior.

HMM for hyperspectral spectrum representation and classification with endmember entropy vectors

NASA Astrophysics Data System (ADS)

Arabi, Samir Y. W.; Fernandes, David; Pizarro, Marco A.

2015-10-01

The Hyperspectral images due to its good spectral resolution are extensively used for classification, but its high number of bands requires a higher bandwidth in the transmission data, a higher data storage capability and a higher computational capability in processing systems. This work presents a new methodology for hyperspectral data classification that can work with a reduced number of spectral bands and achieve good results, comparable with processing methods that require all hyperspectral bands. The proposed method for hyperspectral spectra classification is based on the Hidden Markov Model (HMM) associated to each Endmember (EM) of a scene and the conditional probabilities of each EM belongs to each other EM. The EM conditional probability is transformed in EM vector entropy and those vectors are used as reference vectors for the classes in the scene. The conditional probability of a spectrum that will be classified is also transformed in a spectrum entropy vector, which is classified in a given class by the minimum ED (Euclidian Distance) among it and the EM entropy vectors. The methodology was tested with good results using AVIRIS spectra of a scene with 13 EM considering the full 209 bands and the reduced spectral bands of 128, 64 and 32. For the test area its show that can be used only 32 spectral bands instead of the original 209 bands, without significant loss in the classification process.

Smart Sensor-Based Motion Detection System for Hand Movement Training in Open Surgery.

PubMed

Sun, Xinyao; Byrns, Simon; Cheng, Irene; Zheng, Bin; Basu, Anup

2017-02-01

We introduce a smart sensor-based motion detection technique for objective measurement and assessment of surgical dexterity among users at different experience levels. The goal is to allow trainees to evaluate their performance based on a reference model shared through communication technology, e.g., the Internet, without the physical presence of an evaluating surgeon. While in the current implementation we used a Leap Motion Controller to obtain motion data for analysis, our technique can be applied to motion data captured by other smart sensors, e.g., OptiTrack. To differentiate motions captured from different participants, measurement and assessment in our approach are achieved using two strategies: (1) low level descriptive statistical analysis, and (2) Hidden Markov Model (HMM) classification. Based on our surgical knot tying task experiment, we can conclude that finger motions generated from users with different surgical dexterity, e.g., expert and novice performers, display differences in path length, number of movements and task completion time. In order to validate the discriminatory ability of HMM for classifying different movement patterns, a non-surgical task was included in our analysis. Experimental results demonstrate that our approach had 100 % accuracy in discriminating between expert and novice performances. Our proposed motion analysis technique applied to open surgical procedures is a promising step towards the development of objective computer-assisted assessment and training systems.

Structural, electronic, magnetic, half-metallic, mechanical, and thermodynamic properties of the quaternary Heusler compound FeCrRuSi: A first-principles study.

PubMed

Wang, Xiaotian; Khachai, Houari; Khenata, Rabah; Yuan, Hongkuan; Wang, Liying; Wang, Wenhong; Bouhemadou, Abdelmadjid; Hao, Liyu; Dai, Xuefang; Guo, Ruikang; Liu, Guodong; Cheng, Zhenxiang

2017-11-23

In this paper, we have investigated the structural, electronic, magnetic, half-metallic, mechanical, and thermodynamic properties of the equiatomic quaternary Heusler (EQH) compound FeCrRuSi using the density functional theory (DFT) and the quasi-harmonic Debye model. Our results reveal that FeCrRuSi is a half-metallic material (HMM) with a total magnetic moment of 2.0 μ B in agreement with the well-known Slater-Pauling rule M t  = Z t  - 24. Furthermore, the origin of the half-metallic band gap in FeCrRuSi is well studied through a schematic diagram of the possible d-d hybridization between Fe, Cr and Ru elements. The half-metallic behavior of FeCrRuSi can be maintained in a relatively wide range of variations of the lattice constant (5.5-5.8 Å) under uniform strain and the c/a ratio (0.96-1.05) under tetragonal distortion. The calculated phonon dispersion, cohesive and formation energies, and mechanical properties reveal that FeCrRuSi is stable with an EQH structure. Importantly, the compound of interest has been prepared and is found to exist in an EQH type structure with the presence of some B2 disorder. Moreover, the thermodynamic properties, such as the thermal expansion coefficient α, the heat capacity C V , the Grüneisen constant γ, and the Debye temperature Θ D are calculated.

Real-time human versus animal classification using pyro-electric sensor array and Hidden Markov Model

NASA Astrophysics Data System (ADS)

Hossen, Jakir; Jacobs, Eddie L.; Chari, Srikant

2014-03-01

In this paper, we propose a real-time human versus animal classification technique using a pyro-electric sensor array and Hidden Markov Model. The technique starts with the variational energy functional level set segmentation technique to separate the object from background. After segmentation, we convert the segmented object to a signal by considering column-wise pixel values and then finding the wavelet coefficients of the signal. HMMs are trained to statistically model the wavelet features of individuals through an expectation-maximization learning process. Human versus animal classifications are made by evaluating a set of new wavelet feature data against the trained HMMs using the maximum-likelihood criterion. Human and animal data acquired-using a pyro-electric sensor in different terrains are used for performance evaluation of the algorithms. Failures of the computationally effective SURF feature based approach that we develop in our previous research are because of distorted images produced when the object runs very fast or if the temperature difference between target and background is not sufficient to accurately profile the object. We show that wavelet based HMMs work well for handling some of the distorted profiles in the data set. Further, HMM achieves improved classification rate over the SURF algorithm with almost the same computational time.

A Study to Interpret the Biological Significance of Behavior Associated with 3S Experimental Sonar Exposures

DTIC Science & Technology

2015-09-30

playbacks Killer whale (O. orca) 10 4 8 1 2 LF pilot whale (G. melas ) 30 8 14 4 8 Sperm whale (P. Macrocephalus) 10 4 10 2 5 Humpback whale (M...exposure dataset of the long-finned pilot whale (Globicephala melas ). A hidden Markov model (HMM) approach was developed to quantify behavioral states...Experimental Exposures of Killer (Orcinus orca), Long-Finned Pilot (Globicephala melas ), and Sperm (Physeter macrocephalus) Whales to Naval Sonar. Aquat

The Discontinuous Galerkin Method for the Multiscale Modeling of Dynamics of Crystalline Solids

DTIC Science & Technology

2007-08-26

number. 1. REPORT DATE 26 AUG 2007 2 . REPORT TYPE 3. DATES COVERED 00-00-2007 to 00-00-2007 4. TITLE AND SUBTITLE The Discontinuous Galerkin...Dynamics method (MAAD) [ 2 ], the bridging scale method [47], the bridging domain methods [48], the heterogeneous multiscale method (HMM) [23, 36, 24], and...method consists of three components, 1. a macro solver for the continuum model, 2 . a micro solver to equilibrate the atomistic system locally to the appro

Extracting volatility signal using maximum a posteriori estimation

NASA Astrophysics Data System (ADS)

Neto, David

2016-11-01

This paper outlines a methodology to estimate a denoised volatility signal for foreign exchange rates using a hidden Markov model (HMM). For this purpose a maximum a posteriori (MAP) estimation is performed. A double exponential prior is used for the state variable (the log-volatility) in order to allow sharp jumps in realizations and then log-returns marginal distributions with heavy tails. We consider two routes to choose the regularization and we compare our MAP estimate to realized volatility measure for three exchange rates.

Identifying Dynamic Functional Connectivity Changes in Dementia with Lewy Bodies Based on Product Hidden Markov Models.

PubMed

Sourty, Marion; Thoraval, Laurent; Roquet, Daniel; Armspach, Jean-Paul; Foucher, Jack; Blanc, Frédéric

2016-01-01

Exploring time-varying connectivity networks in neurodegenerative disorders is a recent field of research in functional MRI. Dementia with Lewy bodies (DLB) represents 20% of the neurodegenerative forms of dementia. Fluctuations of cognition and vigilance are the key symptoms of DLB. To date, no dynamic functional connectivity (DFC) investigations of this disorder have been performed. In this paper, we refer to the concept of connectivity state as a piecewise stationary configuration of functional connectivity between brain networks. From this concept, we propose a new method for group-level as well as for subject-level studies to compare and characterize connectivity state changes between a set of resting-state networks (RSNs). Dynamic Bayesian networks, statistical and graph theory-based models, enable one to learn dependencies between interacting state-based processes. Product hidden Markov models (PHMM), an instance of dynamic Bayesian networks, are introduced here to capture both statistical and temporal aspects of DFC of a set of RSNs. This analysis was based on sliding-window cross-correlations between seven RSNs extracted from a group independent component analysis performed on 20 healthy elderly subjects and 16 patients with DLB. Statistical models of DFC differed in patients compared to healthy subjects for the occipito-parieto-frontal network, the medial occipital network and the right fronto-parietal network. In addition, pairwise comparisons of DFC of RSNs revealed a decrease of dependency between these two visual networks (occipito-parieto-frontal and medial occipital networks) and the right fronto-parietal control network. The analysis of DFC state changes thus pointed out networks related to the cognitive functions that are known to be impaired in DLB: visual processing as well as attentional and executive functions. Besides this context, product HMM applied to RSNs cross-correlations offers a promising new approach to investigate structural and temporal aspects of brain DFC.

Bayesian Estimation and Inference Using Stochastic Electronics

PubMed Central

Thakur, Chetan Singh; Afshar, Saeed; Wang, Runchun M.; Hamilton, Tara J.; Tapson, Jonathan; van Schaik, André

2016-01-01

In this paper, we present the implementation of two types of Bayesian inference problems to demonstrate the potential of building probabilistic algorithms in hardware using single set of building blocks with the ability to perform these computations in real time. The first implementation, referred to as the BEAST (Bayesian Estimation and Stochastic Tracker), demonstrates a simple problem where an observer uses an underlying Hidden Markov Model (HMM) to track a target in one dimension. In this implementation, sensors make noisy observations of the target position at discrete time steps. The tracker learns the transition model for target movement, and the observation model for the noisy sensors, and uses these to estimate the target position by solving the Bayesian recursive equation online. We show the tracking performance of the system and demonstrate how it can learn the observation model, the transition model, and the external distractor (noise) probability interfering with the observations. In the second implementation, referred to as the Bayesian INference in DAG (BIND), we show how inference can be performed in a Directed Acyclic Graph (DAG) using stochastic circuits. We show how these building blocks can be easily implemented using simple digital logic gates. An advantage of the stochastic electronic implementation is that it is robust to certain types of noise, which may become an issue in integrated circuit (IC) technology with feature sizes in the order of tens of nanometers due to their low noise margin, the effect of high-energy cosmic rays and the low supply voltage. In our framework, the flipping of random individual bits would not affect the system performance because information is encoded in a bit stream. PMID:27047326

Bayesian Estimation and Inference Using Stochastic Electronics.

PubMed

Thakur, Chetan Singh; Afshar, Saeed; Wang, Runchun M; Hamilton, Tara J; Tapson, Jonathan; van Schaik, André

2016-01-01

In this paper, we present the implementation of two types of Bayesian inference problems to demonstrate the potential of building probabilistic algorithms in hardware using single set of building blocks with the ability to perform these computations in real time. The first implementation, referred to as the BEAST (Bayesian Estimation and Stochastic Tracker), demonstrates a simple problem where an observer uses an underlying Hidden Markov Model (HMM) to track a target in one dimension. In this implementation, sensors make noisy observations of the target position at discrete time steps. The tracker learns the transition model for target movement, and the observation model for the noisy sensors, and uses these to estimate the target position by solving the Bayesian recursive equation online. We show the tracking performance of the system and demonstrate how it can learn the observation model, the transition model, and the external distractor (noise) probability interfering with the observations. In the second implementation, referred to as the Bayesian INference in DAG (BIND), we show how inference can be performed in a Directed Acyclic Graph (DAG) using stochastic circuits. We show how these building blocks can be easily implemented using simple digital logic gates. An advantage of the stochastic electronic implementation is that it is robust to certain types of noise, which may become an issue in integrated circuit (IC) technology with feature sizes in the order of tens of nanometers due to their low noise margin, the effect of high-energy cosmic rays and the low supply voltage. In our framework, the flipping of random individual bits would not affect the system performance because information is encoded in a bit stream.

hwhap_Ep13_First Flight

NASA Image and Video Library

2017-10-06

>> HOUSTON, WE HAVE A PODCAST. WELCOME TO THE OFFICIAL PODCAST OF THE NASA JOHNSON SPACE CENTER, EPISODE 13: “BEFORE HIS FIRST FLIGHT.” I’M GARY JORDAN AND I’LL BE YOUR HOST TODAY. SO THIS IS THE PODCAST WHERE WE BRING IN THE EXPERTS, LIKE NASA SCIENTISTS, ENGINEERS, SOMETIMES EVEN ASTRONAUTS, AND THEY ALL TELL YOU THE COOLEST THINGS GOING ON HERE AT NASA. SO TODAY, WE’RE TALKING WITH MARK VANDE HEI. HE’S A U.S. ASTRONAUT HERE AT THE JOHNSON SPACE CENTER IN HOUSTON, TEXAS, AND HE JUST LAUNCHED TO THE INTERNATIONAL SPACE STATION ON SEPTEMBER 12th, 2017 TO GO TO SPACE FOR THE VERY FIRST TIME. WE HAD A GREAT DISCUSSION ABOUT HIS EXPECTATIONS FOR FLYING TO SPACE AND SOME OF THE WORK AND HIS TRAINING THAT HE HAD TO GO THROUGH TO GET READY FOR HIS VOYAGE TO THE STATION. SO WITH NO FURTHER DELAY, LET’S GO LIGHTSPEED AND JUMP RIGHT AHEAD TO OUR TALK WITH MR. MARK VANDE HEI. ENJOY. [ MUSIC ] >> T MINUS FIVE SECONDS AND COUNTING. MARK. [ INDISTINCT RADIO CHATTER ] >> HOUSTON, WE HAVE A PODCAST. [ MUSIC ] >> ALL RIGHT, WELL, THANKS FOR COMING TODAY, MARK. I KNOW YOU’RE VERY BUSY, ESPECIALLY COMING SO CLOSE TO YOUR LAUNCH DATE. SO THAT’S SEPTEMBER AGAIN, RIGHT? >> IT IS SEPTEMBER 13th. >> IT IS SEPTEMBER, OKAY. SO THAT’S WITH-- NOW, IT’S KIND OF CHANGED UP A BIT, RIGHT? SO NOW WE’RE TALKING-- YOU’RE LAUNCHING WITH ALEXANDER AND JOE, RIGHT? >> THAT’S CORRECT. >> ALEXANDER MISURKIN AND JOE ACABA. SO, I MEAN, THIS IS YOUR VERY FIRST FLIGHT COMING UP SOON, SO YOU’VE BEEN BUSY TRAINING FOR YEARS. I MEAN, YOU WERE SELECTED IN 2009, IF I’M NOT MISTAKEN, RIGHT? >> THAT’S CORRECT. >> THERE’S A LOT OF TRAINING TO BE HAD SO, I MEAN, LET’S TALK ABOUT SOME OF THOSE THINGS. LIKE, WHAT WERE YOUR-- WHAT ARE YOUR EXPECTATIONS AND WHAT ARE YOU PREPARING FOR REALLY? I MEAN, WHAT DOES AN ASTRONAUT NEED TO KNOW BEFORE THEY LAUNCH? >> SO, THE PRIMARY THING WE NEED TO KNOW IS HOW TO-- I WOULD SAY THE PRIMARY THING WE NEED TO KNOW IS HOW TO FOLLOW INSTRUCTIONS. >> ALL RIGHT. >> BECAUSE WE REALLY ARE SERVING AS THE EYES AND HANDS OF A LOT OF OTHER PEOPLE THAT AREN’T THERE WITH US BUT ARE ABLE TO SUPPORT US. >> MM-HMM. >> SO THAT’S THE PRIMARY THING. YOU ALSO NEED TO KNOW HOW TO WORK WELL WITH THE OTHER PEOPLE THAT YOU’RE LIVING WITH. >> THAT’S RIGHT. >> AND MAKE SURE YOU TAKE CARE OF EACH OTHER, MAKE SURE THAT EVERYTHING’S FULLY FUNCTIONAL, AND THEN AFTER THAT I WOULD SAY WE HAVE TO HAVE ALL THE TECHNICAL SKILLS TO DO OUR JOB THAT ARE OPERATE THE SCIENCE EXPERIMENTS AND BE ABLE TO KEEP THE SPACE STATION ACTUALLY RUNNING. >> NICE. NOW, I MEAN, SO WE TALKED A LITTLE BIT ON A PREVIOUS EPISODE WITH RANDY BRESNIK ABOUT SOME OF THE THINGS YOU HAVE TO LEARN, BUT JUST LIKE AN OVERVIEW OF SOME OF THE THINGS, LIKE, IN TERMS OF KNOWING WHAT TO DO ON THE STATION. >> MM-HMM. >> YOU’RE TALKING ALL THE DIFFERENT SYSTEMS, RIGHT? SO, KOMRADE DESCRIBED MORE FIXING THE TOILET. >> YEAH, YEAH. >> AND YOU KNOW, LEARNING HOW TO DO AN EVA AND EVERYTHING IN BETWEEN. >> YEAH. >> SO IS THAT KIND OF WHAT YOU’VE BEEN DOING OVER THE PAST-- >> ABSOLUTELY. I’VE GOT-- KOMRADE’S GOING TO BE THE COMMANDER SO THERE’S SOME-- CERTAINLY SOME ADDITIONAL THINGS HE’S GOT TO LEARN. >> OKAY. >> BUT, BY AND LARGE, THE CREW MEMBERS ON THE SPACE STATION, WHEN THERE’S NOT AN EMERGENCY TAKING PLACE, WE’RE ALL KIND OF EQUAL. >> MM-HMM. >> CERTAINLY THE COMMANDER, WHEN AN EMERGENCY IS HAPPENING, HE’S-- THAT’S THE PERSON THAT’S MAKING THOSE TOUGH CALLS AND PULLING THE TEAM TOGETHER. >> MM-HMM. >> AND HE WILL ALSO COORDINATE ON BEHALF OF THE ENTIRE TEAM. BUT, CREW MEMBERS ON THE STATION ARE GENERALISTS. WE HAVE TO HAVE A SKILL SET THAT WILL ALLOW US TO DO WHATEVER THE GROUND NEEDS US TO DO AND THAT DOES INVOLVE EVA TRAINING, OF COURSE. >> MM-HMM. >> THAT INVOLVES ROBOTICS TRAINING. THAT INVOLVES MEDICAL TRAINING, TOO, JUST IN CASE SOMETHING COMES UP, WE’LL HAVE TO TAKE CARE OF EACH OTHER. THAT’S BEEN PRETTY INTERESTING. >> YEAH. >> DID KOMRADE TALK AT ALL ABOUT THAT? >> ABOUT THE-- WHICH PART? >> THE MEDICAL TRAINING? >> YEAH, OH, YEAH. I MEAN, JUST A TINY LITTLE BIT. WE ACTUALLY ONLY HAD ABOUT 25 MINUTES TO TALK, SO HE TALKED-- I MEAN, MOSTLY A LITTLE BIT. HE SAID, I MEAN, YOU HAVE TO-- YOU HAVE TO KNOW KIND OF THE BASICS OF MEDICAL TRAINING IN CASE THERE’S AN EMERGENCY SITUATION, BUT HE ALSO MENTIONED THAT YOU HAVE-- YOU CAN CALL DOWN TO DOCTORS AND THEY CAN WALK YOU THROUGH SOME OF THOSE THINGS. >> ABSOLUTELY. >> AND I GUESS THAT KIND OF HELPS, RIGHT? BECAUSE ESPECIALLY NOT BEING A DOCTOR AND YOU GUYS-- ONE THING I SAID LAST TIME WAS YOU HAVE TO BE A JACK OF ALL TRADES AND A MASTER OF ALL IN SORT OF A-- IN A WAY, I GUESS. YOU HAVE TO REALLY KNOW THE SYSTEMS. >> IN A WAY, BUT THE GROUND IS ALWAYS THERE TO HELP OUT. >> THAT’S TRUE. >> FOR EXAMPLE, WE HAD AN EVENT THAT INVOLVED US SIMULATING THAT ONE OF THE CREW MEMBERS NEEDED CPR. >> MM-HMM. >> AND IT HAD BEEN SIX MONTHS AT LEAST, MAYBE EVEN A YEAR, SINCE MY PREVIOUS TRAINING ON THAT AND THE INSTRUCTORS DID A GOOD JOB OF SAYING, “OKAY, GO FOR IT.” SO, I KNEW I SHOULD DO CHEST COMPRESSIONS. I KNEW I SHOULD GIVE-- DO BREATHS PERIODICALLY. >> RIGHT, RIGHT. >> BUT, I WASN’T 100% CERTAIN OF WHAT NUMBER OF BREATHS, WHAT NUMBER OF REPETITIONS. >> RIGHT. >> SO I JUST STARTED, AND THEN THEY REMINDED AS PART OF THE TRAINING THAT, “HEY, LOOK, WHEN YOU HAVE THAT UNCERTAINTY-- YOU DID A GOOD JOB OF GETTING STARTING, BUT THE GROUND’S THERE TO HELP ANSWER THAT QUESTION. YOU COULD’VE GOT-- SAID, “HEY, WE NEED THIS CONFERENCE RIGHT NOW AND LET’S GET A DOCTOR TALKING TO US AND MAKE SURE WE’RE DOING THE RIGHT THINGS.”” >> MM-HMM. >> SO BECAUSE YOU HAVE TO KNOW SO MUCH SOMETIMES THE DETAILS-- THE GROUND CAN REALLY HELP YOU OUT WITH THAT. >> YEAH, AND THEY’RE THERE 24/7, RIGHT? >> ABSOLUTELY. >> SO YOU CAN CALL DOWN AND SAY, “HEY, SOMETHING’S GOING ON. I NEED HELP.” >> YES. YES. >> AND YOU GUYS WALK THROUGH ALL OF THOSE DIFFERENT THINGS. SO, I MEAN, ON TOP OF JUST TRAINING FOR SOME OF THE THINGS ON THE INTERNATIONAL SPACE STATION THAT YOU’RE GOING TO BE DOING, ESPECIALLY EMERGENCY SITUATIONS, YOU GO THROUGH OTHER TYPES OF TRAINING TOO, RIGHT? DON’T YOU DO SURVIVAL TRAINING AND THINGS LIKE THAT? >> YEAH, ABSOLUTELY. WE HAVE THE-- FIRST OF ALL, THERE’S LAND SURVIVAL TRAINING-- ONE OF THE FIRST THINGS YOU DO AS ASTRONAUT CANDIDATES. >> MM-HMM. >> I BELIEVE THE NEXT CLASS IS GOING TO DO THAT AT FORT RUCKER, IT’S AN ARMY BASE. >> OKAY. >> THEN, THERE’S LAND SURVIVAL TRAIN-- NO, I ALREADY TALKED ABOUT THAT. THERE’S LAND SURVIVAL TRAINING THAT WE DO AS ASTRONAUT CANDIDATES. >> RIGHT. >> AND THEN, THE NEXT SURVIVAL TRAINING YOU DO IS ACTUALLY AFTER YOU’RE ASSIGNED TO A SOYUZ CREW. THERE’S WINTER SURVIVAL TRAINING IN CASE YOUR SOYUZ LANDS SOME PLACE WHERE THE SEARCH AND RESCUE FORCES CAN’T GET TO YOU AS QUICKLY AS YOU’D LIKE. >> OH. >> AND YOU MAY HAVE TO BE SOME PLACE IN THE WINTER IN RUSSIA AND HAVE TO BE ABLE TO SURVIVE FOR A COUPLE DAYS. >> OH, WOW. >> WORST CASE. >> RIGHT, RIGHT. >> SO WE DO THAT TRAINING. THAT’S ALSO A VERY GOOD TIME FOR THE CREW TO BOND WITH EACH OTHER, AS YOU CAN IMAGINE. >> YEAH. >> THERE’S ALSO NOMINALLY, THE SOYUZ LANDS ON LAND. >> RIGHT. >> BUT, WE ALSO HAVE WATER SURVIVAL TRAINING. >> OKAY, JUST IN CASE IT DOES LAND ON WATER. >> JUST IN CASE. WELL, IF THERE’S A REALLY URGENT NEED TO DESCEND. >> RIGHT. >> AND WE’RE NOT GOING TO WORRY ABOUT WHERE ON THE EARTH WE HIT. >> RIGHT. >> POSSIBLY, IF IT’S THAT-- NORMALLY, WE’RE VERY-- >> A LOT OF BAD THINGS HAVE TO HAPPEN IN A ROW TO GET TO THAT POINT. >> YES. WE REALLY WANT TO LAND IN SPECIFIC PLACES, BUT JUST IN CASE, THERE’S THE OPTION. MUCH OF THE EARTH IS COVERED WITH WATER, SO WE LEARNED HOW TO DEAL WITH THAT SITUATION AS WELL. >> RIGHT. SO, YOU DID DO THE WINTER SURVIVAL TRAINING, RIGHT? YOU HAD TO GO THROUGH THAT. WHAT ARE-- DO YOU HAVE ANY GOOD STORIES OF-- YOU SAID IT WAS A GOOD TIME TO BOND WITH YOUR CREWMATES, SO ARE THERE ANY GOOD STORIES THERE? >> SURE. SO, THE TRAINING CONSISTS OF STAYING UP. FOR US, WE STAYED UP FOR TWO NIGHTS. >> MM-HMM. >> THE FIRST NIGHT YOU EGRESS THE SOYUZ CAPSULE THAT THEY PUT OUT IN THE FOREST. WE’VE GOT A REALLY GOOD SET OF COLD WEATHER GEAR THAT WE PUT ON. >> MM-HMM. >> AND SO, WE PUT ALL THAT STUFF ON, AND THEN WE USE THE SEAT LINERS, THAT ARE MOLDED TO US, THAT ARE IN THE-- I WOULD CALL IT KIND OF LIKE A BUCKET INSIDE THE SOYUZ. >> OH. >> WE CAN TAKE THOSE OUT AND USE THOSE AS SLEDS. SO WE PUT A BUNCH OF GEAR ON THAT. >> OH, I SEE. >> AND YOU GOT TO DRAG THOSE THROUGH TO A PLACE TO FIND A PLACE TO SET UP CAMP. >> COOL. >> OF COURSE, THE PARACHUTE THAT THE SOYUZ LANDS WITH IS HUGE, SO THAT’S A MASSIVE RESOURCE OF CLOTH. >> MM-HMM. >> SO THE FIRST NIGHT, WHAT WE DID IS HAD TO SET UP A LEAN-TO AND USED BOTH TIMBER THAT WE FOUND IN THE AREA, AND STRINGS FROM THE PARACHUTE, AND THE ACTUAL CLOTH FROM THE PARACHUTE, AS WELL AS A LOTO OF BRANCHES TO SET UP A SHELTER. BUT, THAT WAS REALLY-- THAT NIGHT WAS ALL ABOUT THE FIRE. >> OH. >> BECAUSE THE LEAN-TO JUST KEPT US FROM LOSING ALL THE HEAT, BUT WE WERE KIND OF SLEEPING-- THERE WAS TWO PEOPLE KIND OF SLEEPING ON TOP OF EACH OTHER JUST ABOUT-- >> SORRY, A LEAN-TO IS LIKE-- IS THAT A SHELTER THAT, I’M ASSUMING, LEANS UP AGAINST SOMETHING? IS THAT WHAT THAT IS? >> A LEAN-TO-- IMAGINE IF YOU HAD A PLANE THAT WAS-- LIKE, A HALF OF A ROOF. >> OKAY. >> AND ALL IT IS IS ONE WALL THAT GOES FROM MAYBE ABOUT WAIST HIGH DOWN TO THE GROUND, WITH ENOUGH SPACE UNDERNEATH IT SO THAT TWO PEOPLE COULD BE SLEEPING UNDERNEATH IT WITH THE LENGTH OF THEIR BODIES FACING OUT TO THE OPEN. >> I SEE, OKAY. >> AND WHAT WE DO WITH THAT IS WE LIGHT A FIRE ON THE OPEN SIDE SO THAT THEY GET A LOT OF WARMTH, AND THE FACT THAT YOU HAVE THAT BACKDROP HELPS REFLECT SOME OF THAT HEAT DOWN TOWARDS YOU. >> NICE. BUT, IT DOESN’T TRAP ANY OF THE SMOKE OR ANYTHING LIKE THAT? >> IDEALLY, NO. >> YEAH. >> NO. BUT, THAT’S WHY I SAID, IT’S ALL ABOUT THE FIRE. >> RIGHT. >> IF THE FIRE GOES OUT, THAT LEAN-TO IS REALLY WORTHLESS. >> RIGHT. >> SO, ONE PERSON’S AWAKE AND CONSTANTLY CUTTING WOOD, BECAUSE TO KEEP THE FIRE GOING IT’S AMAZING HOW MUCH WOOD YOU NEED IN THAT ENVIRONMENT. >> WOW. >> WE DID THAT. MY TWO RUSSIANS THAT I-- INITIALLY I WAS GOING TO LAUNCH WITH TWO RUSSIANS, SO I DID THAT WITH TWO RUSSIANS. >> I SEE. >> THEY HAD BOTH DONE THIS BEFORE. THEY WERE REALLY, REALLY GOOD WITH THE MATERIAL WE HAD. >> NICE. >> AND WERE SMART ENOUGH THAT THEY KNEW THAT THE NEXT DAY WE’D HAVE TO SET UP A TEEPEE. SO, OUR LEAN-TO KIND OF HAD A FEW PIECES THAT WE COULD USE FOR THE TEEPEE READY TO GO, SO WE JUST HAD TO CHANGE THE LEAN-TO AND WE KIND OF TURNED IT INTO A TEEPEE ON THE NEXT DAY. >> OH. >> SO, THE TEEPEE WAS GREAT. WE-- IT’S MUCH MORE COMFORTABLE. IT HAD A MUCH SMALLER FIRE INSIDE THE TEEPEE. >> OH, OKAY. >> SO, YOU HAD TO MAKE THE TEEPEE ON THE SECOND DAY BECAUSE IT’S-- I GUESS, IT’S MORE INTENSIVE TO BUILD? IS THAT WHY? >> IT TAKES LONGER TO BUILD. >> I SEE. >> BUT, IT’S ALSO MUCH BETTER SHELTER. >> OKAY. >> SO, IT’S THE TYPE OF THING THAT-- QUITE HONESTLY, I THINK ALL OF US WOULD’VE PREFERRED TO GO RIGHT TO THE TEEPEE, BECAUSE-- I MEAN, I’M NOT 100% CERTAIN IT REALLY IS-- TAKES LONGER TO BUILD, BUT THE RUSSIANS WANTED US TO HAVE THE EXPERIENCE BUILDING BOTH TYPES. >> I SEE. >> AND TO UNDERSTAND WHAT IT TOOK TO LIVE IN BOTH OF THEM. >> OKAY, OKAY. >> YOU NEED A LOT LESS LUMBER TO KEEP THE TEEPEE WARM, BUT AGAIN, WE WERE BOTH-- WE WERE EXPERIENCING BOTH SITUATIONS. >> MM-HMM. WOW. AND THEN, I GUESS, YOU HAVE SURVIVAL TRAINING. WHAT OTHER KINDS OF THINGS DO YOU GO THROUGH? >> WELL, ONE OF THE BIG DEALS FOR ASTRONAUTS THAT WORK AT NASA IS WE COME FROM A LOT OF DIFFERENT BACKGROUNDS-- >> OKAY. >> --FROM MICROBIOLOGIST TO NAVY SEALS. SO, WE’VE GOT TO BE ABLE TO HAVE A CULTURE WHERE ALL THOSE PEOPLE CAN COME TOGETHER AND OPERATE IN A-- OPERATE HIGHLY TECHNICAL MACHINES IN AN ENVIRONMENT WHERE IF YOU MESS IT UP YOU COULD DIE. >> RIGHT. >> SO, ANOTHER THING THAT’S REALLY VERY, VERY INTERESTING IS WE USE T-38s. IT’S A-- IT’S THE SAME TYPE OF AIRCRAFT THAT THE AIR FORCE USES TO TRAIN PILOTS. >> OKAY. >> SO WE USED THOSE. >> MM-HMM. >> THE NICE THING ABOUT IS, MUCH LIKE-- WE CAN’T FLY PEOPLE IN SPACE VERY OFTEN, BUT WE CAN PUT PEOPLE IN THESE JETS VERY OFTEN AND IT-- YOU HAVE TO-- THE JET MOVES REALLY, REALLY FAST, SO YOU HAVE TO BE ABLE TO THINK FAST. YOU’VE ALSO GOT TO COORDINATE WITH THE GROUND AND THEY WILL DIRECT YOU WHAT TO DO, AND AT TIMES YOU HAVE TO MAKE DECISIONS THAT REQUIRE YOU TO SAY, “HEY, I GET WHAT YOU JUST SAID, BUT WE REALLY NEED TO DO THIS BECAUSE WE’RE IN A TOUGH SITUATION,” FOR EXAMPLE. >> OKAY. >> AND YOU HAVE TO COORDINATE WITH THE OTHER CREW MEMBER BECAUSE IT’S A TWO COCKPIT AIRCRAFT. THERE’S A PILOT AND TYPICALLY WE CALL HIM A BACK SEATER. YOU WORK AS THE NAVIGATOR AND COMMUNICATOR IN A NOMINAL SITUATION. >> AND WAS THAT YOUR JOB? >> WELL, BECAUSE I’M NOT A MILITARY PILOT, YES. >> OKAY. >> SO ALL OF THE FRONT SEATERS ARE MILITARY PILOTS IF THEY’RE ASTRONAUTS, AND THEY ARE INSTRUCTOR PILOTS, TYPICALLY FROM THE MILITARY AS WELL IF THEY’RE NOT ASTRONAUTS. IT’S A GREAT DEAL TO HAVE TO GO FLY AROUND IN A JET AS PART OF YOUR JOB. >> RIGHT. DID YOU END UP FLYING A FELLOW ASTRONAUT? OR DID YOU FLY WITH ONE OF THE PILOTS THAT THEY HAD, I GUESS? >> INITIALLY, YOU FLY WITH INSTRUCTORS. >> OKAY. >> BUT, BY AND LARGE, ALMOST EVERY FLIGHT IS WITH THE-- ANOTHER ASTRONAUT PILOT. >> I SEE. DID ANY OF THEM MESS WITH YOU AT ANY TIME OR TRY TO MAKE YOU THROW UP OR ANYTHING LIKE THAT? >> NO. SO, ONE TIME THOUGH-- SO ONE OF THE THINGS THEY ALWAYS TELL-- BECAUSE THEY’RE VERY EXPERIENCED AND WE’RE NOT, IT’S REAL EASY TO JUST ASSUME THAT THEY KNOW HOW TO DO EVERYTHING. THEY CAN FLY THAT JET COMPLETELY BY THEMSELVES. >> AWESOME. >> SO IT CAN BE A LITTLE INTIMIDATING WHEN YOU GET IN THE BACK SEAT. YOU KNOW THE FRONT SEATER CAN DO EVERYTHING BY THEMSELVES. >> MM-HMM. >> BUT, THEY REALLY WANT YOU TO BE ENGAGED AND RECOGNIZE THAT IF THEY DO SOMETHING STUPID THAT WOULD KILL THEM IT’S GOING TO KILL BOTH OF US. >> RIGHT. >> YOU’RE A NANO SECOND BEHIND THEM. AND WE TRAIN AND THE ASTRONAUT PILOTS ALLOW US TO DO EVERYTHING. THEY’LL ALLOW US TO FLY THE JET, DO THE COMMUNICATIONS, DO THE NAVIGATION, JUST TO GET GOOD AT THAT, BECAUSE THERE’S A-- FOR EXAMPLE, IF SOMETHING HAPPENED TO THE PILOT, YOU MIGHT HAVE TO DO THAT. >> RIGHT. >> AND IT’S MORE FUN FOR US. AND ACTUALLY, A LOT OF THE ASTRONAUT PILOTS HAVE EXPERIENCED WITH BEING AN INSTRUCTOR PILOTS, SO THEY’RE GOOD AT THAT. >> MM-HMM. >> WELL, ONE TIME, BARRY WILMORE WAS TRYING TO MAKE SURE I WAS PAYING ATTENTION, AND I WAS SUPPOSED TO BE CLIMBING TO A SPECIFIC ALTITUDE, AND JUST MAYBE ABOUT 500 FEET BEFORE I NEEDED TO START LEVELING OFF, HE SAID, “SO, WHERE DO YOU GO TO CHURCH?” AND I STOPPED PAYING ATTENTION TO WHAT WAS GOING ON IN THE JET AND THEN I STARTED TALKING TO HIM. AND THEN HE DID THAT ON PURPOSE SO THAT HE-- SO THEN I RECOGNIZED I NEED TO PRIORITIZE WHAT I WAS DOING TO THE JET MORE, AND SO THEN HE WAITED UNTIL I WAS REALLY FLYING STRAIGHT THROUGH THE ALTITUDE I WAS SUPPOSED TO BE LEVELING OFF AT AND SAID, “CHECK YOUR ALTITUDE.” AND THEN I DID. ANOTHER TIME, WE’RE NOT-- AS A BACK SEATER, I’M NOT ALLOWED TO FLY WITHIN 200 FEET OF THE GROUND, BUT YOU CAN FLY TOWARDS AN AIRPORT, GET TO 200 FEET, AND THEN ACT LIKE THERE’S A PROBLEM ON THE RUNWAY, AND THEN BASICALLY ADD POWER TO THE JET AND GO THROUGH THE TAKE OFF PROCESS. >> I SEE. >> WELL, EARLIER ON IN MY TRAINING, I WAS FLYING WITH ANOTHER GUY AND HE DID A REALLY GOOD JOB OF LETTING ME MESS UP AS MUCH AS POSSIBLE BEFORE HE’D CORRECT ME SO THAT I WOULD LEARN. SAME TYPE OF THING, I GAVE IT A LOT OF POWER, I STARTED CLIMBING. >> MM-HMM. >> I DIDN’T-- I WASN’T EXPERIENCED ENOUGH TO RECOGNIZE THAT RIGHT AFTER I STARTED CLIMBING I NEEDED TO REDUCE THE POWER. >> OH. >> SO, I WAS REALLY, REALLY SPEEDING UP AND I ONLY HAD TO CLIMB UP TO 3,000 FEET, WHICH YOU DO REALLY FAST IN THAT JET IF YOU HAVEN’T TAKEN THE POWER OUT. >> WHOA. >> AND SO, SAME THING, I GOT TO 3,000 FEET, I WAS CLIMBING REALLY, REALLY FAST, HE SAID, “CHECK YOUR ALTITUDE.” AND MY IMMEDIATE RESPONSE WASN’T TO TAKE OUT THE POWER, IT WAS JUST TO PITCH THE NOSE FORWARD, WHICH MEANT THAT ANYTHING THAT I HAD LOOSE IN THE JET JUST HIT THE CEILING BECAUSE I JUST WENT DOWN SO FAST ALL THE SUDDEN. >> WHOA. >> REALLY GOOD TRAINING. >> YEAH. >> I DIDN’T FORGET THAT LESSON. >> YEAH. THAT’S GOOD THAT YOU GUYS ARE ALWAYS KEEPING EACH OTHER IN CHECK. I’M SURE THAT ALL YOUR ASTRONAUT-- YOUR FELLOW ASTRONAUTS ARE CONSTANTLY DOING THIS, RIGHT? THEY’RE GIVING YOU ADVICE AND ANYTHING LIKE THAT. >> ABSOLUTELY. >> NOW, YOU BEING A FIRST TIME FLYER, I’M SURE THEY’VE GIVEN YOU SOME OF THOSE EXPERIENCES, ESPECIALLY SOME OF YOUR CLASSMATES, RIGHT? >> MM-HMM. >> SO WE HAVE REID WISEMAN, AND I’M TRYING TO THINK. >> MIKE HOPKINS. >> MIKE HOPKINS. >> KJELL LINDGREN. >> KJELL-- ALL THESE GUYS HAVE FLOWN BEFORE. >> KATE RUBINS. >> YEAH, THAT’S RIGHT, KATE MOST RECENTLY. SO, HAVE THESE GUYS GIVEN YOU SOME ADVICE, COME TO YOU AND SAY, “HEY, THIS”-- YOU KNOW, ANY KIND OF THINGS THAT YOU HAVE TO BE WATCHING OUT FOR? >> ABSOLUTELY. >> YEAH. >> AND NOT JUST THEM, ALL OF THEM. >> RIGHT. >> EVERYTHING FROM IF YOU’RE HAVING A BAD DAY DON’T TALK TO IT ON THE-- DON’T TALK TO PEOPLE ABOUT IT ON THE RADIO, TO EXPECTATIONS ON HOW TO-- AS YOU’RE GETTING READY FOR THE LAUNCH AND YOUR FAMILY’S IN KAZAKHSTAN, GETTING READY FOR THAT, WHAT TO EXPECT OUT OF THAT. >> ANY GOOD NUGGETS THAT THEY’VE TOLD YOU? >> CHRIS CASSIDY TOLD ME THAT ONE OF THE THINGS TO DO WHEN YOU’RE DOING A PROCEDURE IS TO MAKE SURE-- THERE’S NOTES BLOCKS IN A LOT OF THE PROCEDURES. >> MM-HMM. >> AND HE SAID, “THE NOTES BLOCKS AREN’T REQUIRED FOR US TO READ.” >> HMM. >> BUT, YOU REALLY NEED TO READ THOSE BECAUSE THEY TYPICALLY GIVE YOU THE BIG PICTURE. >> HMM. >> AND SO, WHEN YOU READ THOSE CAREFULLY, THEN AS YOU’RE DOING THE STEPS IT’LL PREVENT YOU FROM DOING THOSE STEPS BLINDLY, WHICH HELPS YOU BE A LITTLE MORE ACCURATE IN HOW YOU’RE DOING THE PROCEDURE. SO IF YOU KNOW WHY YOU’RE DOING THIS PARTICULAR THING THEN IT’S A LOT EASIER TO RECOGNIZE WHEN YOU’RE PRESSING THE WRONG-- ABOUT TO PRESS THE WRONG BUTTON BECAUSE IT DOESN’T MAKE SENSE. >> I SEE. >> MAYBE YOU MISREAD THAT STEP LATER ON. >> OKAY, SO LIKE, ALL THE LITTLE DETAILS, I’M SURE. >>THERE’S A-- OH, YEAH. YES, YES. >> SO, I MEAN, IS THERE ANYTHING THAT YOU-- THAT ANY ASTRONAUT HAS GIVEN YOU SO FAR JUST TO ALWAYS KEEP THIS IN MIND. I GUESS, THE NOTES IS ONE OF THEM, BUT ESPECIALLY-- MAYBE SOYUZ ASCENT OR SOMETHING, YOU KNOW, MAYBE LEAN BACK. I REMEMBER, WHAT WAS-- I WAS TALKING WITH SHANE KIMBROUGH JUST RECENTLY AND THEY SAID ONCE HE GETS TO A CERTAIN POINT YOU GOT TO MAKE SURE YOU STRAP DOWN, OTHERWISE YOU’RE GOING TO GO FLYING UP OR SOMETHING LIKE THAT. ANY KIND OF PIECES OF ADVICE LIKE THAT? WELL, IT DOESN’T EVEN HAVE TO BE OPERATIONAL. IT COULD BE YOU’RE GOING TO THE BATHROOM AND YOU HAVE TO MAKE SURE THAT YOU TURN THE FAN ON FIRST OR ONE OF THOSE THINGS. >> MM-HMM. >> I’M SURE YOU GO THROUGH ALL OF THOSE THINGS. >> KEEP TRACK OF YOUR STUFF. SO, ONE OF THE THINGS THAT WE’RE VERY COMFORTABLE WITH ON EARTH IS WHEN YOU PUT SOMETHING DOWN IT’S DOWN. >> MM-HMM. >> AND WE TEND TO THINK OF LEAVING THINGS ON A TWO DIMENSIONAL SURFACE AND STAYING THERE. >> YEAH. >> BUT, YOU HAVE AN EXTRA DIMENSION IN SPACE AND YOU HAVE TO PUT A LITTLE EXTRA EFFORT INTO REMEMBERING, LIKE, ANOTHER DIMENSION THAT IT COULD BE SOME PLACE ELSE, TOO. >> THAT’S RIGHT. >> THAT CAN BE CHALLENGING FOR PEOPLE, IS JUST REALLY SLOWING YOURSELF DOWN ENOUGH TO LOOK AT WHERE YOU PUT SOMETHING AND VISUALIZE WHAT’S AROUND YOU. BECAUSE YOU COULD COME BACK TO THE SAME PLACE, AND IF YOU WEREN’T VERY DELIBERATE ABOUT LOOKING AT THAT PLACE FROM AN ORIENTATION THAT YOU ALWAYS TAKE, YOU MIGHT COME IN THERE UPSIDE DOWN AND BE LIKE, “WELL, I REMEMBER PUTTING IT SOMEWHERE IN HERE, BUT NOTHING LOOKS-- I CAN’T PICTURE IT IN THIS SPOT.” >> YEAH. >> SO, THINGS LIKE THAT. >> I REMEMBER TALKING TO MIKE HOPKINS A COUPLE-- WELL, PROBABLY MORE THAN A COUPLE MONTHS AGO, BUT HE-- ONE THINGS THAT ALWAYS STUCK WITH ME WAS HE WAS TALKING ABOUT HE WAS WORKING ON THIS RACK, I GUESS, AND HE HAD TO PULL IT BACK AND GET TO-- GET BEHIND IT. AND JUST THE WAY THAT HE WAS DOING IT, HE JUST-- IT WAS HARD TO REACH. AND I DON’T KNOW IF HE’S TOLD YOU THE SAME STORY, BUT IT WAS HARD TO REACH AND HE CALLS TO THE GROUND, TELLS HIM HIS PROBLEM, AND HE’S LIKE-- AND THEY’RE LIKE, “WELL, FLIP UPSIDE DOWN.” AND HE’S LIKE, “OH, YEAH, I CAN DO THAT.” AND SO, I GUESS YOU’RE TRAINING ON THE GROUND, BUT YOU DO HAVE THE LIMITATIONS OF GRAVITY ON THE GROUND EVEN THOUGH YOU HAVE ALL THESE MOCK UPS. BUT, FLIPPING UPSIDE DOWN WAS-- IT SOLVED THE PROBLEM IMMEDIATELY. HE GOT A WHOLE NEW VANTAGE POINT, BUT YOU CAN’T PRACTICE FLIPPING UP ON-- IN 1G ON THE AIRPLANE. >> YOU CAN'T. YEAH, DEFINITELY CAN’T. >> OH. SO AN ASTRONAUT CLASS, JUST ACTUALLY RECENTLY GOT SELECTED. DOES THIS BRING BACK ANY KIND OF ANY MEMORIES OF WHEN YOU GOT SELECTED AS AN ASTRONAUT BACK IN 2009? >> YES, DEFINITELY. I’VE SEEN A LOT OF THOSE ASTRONAUT HOPEFULS THAT HAVE BEEN EITHER IN THE GYM. >> YEAH. >> OR GOING TO THEIR INTERVIEWS OR WHATEVER. THAT IS AN EMOTIONAL ROLLERCOASTER. I DON’T ENVY THEM AT ALL. >> BECAUSE YOU WENT THROUGH IT. >> ABSOLUTELY, YEAH. >> YEAH, YEAH. >> IT’S-- I THINK I DID A PRETTY GOOD JOB OF ASSUMING THERE WAS NO HOPE THAT I WOULD GET THE JOB AND THAT MADE IT A LOT LESS STRESSFUL. IN FACT, THE ONLY TIME THAT I GOT KIND OF LIKE, “WHOA, BE CAREFUL,” WAS WHEN I THOUGHT I HAD JUST DONE SOMETHING REALLY, REALLY SUCCESSFUL AND MAYBE THERE’S A CHANCE I’LL GET THIS JOB. I THOUGHT, “NO, NO, NO. DON’T DO THAT TO YOURSELF.” >> BECAUSE THAT’S WHEN YOU GET-- YOU MAKE YOURSELF ALL NERVOUS, RIGHT, I GUESS? >> THAT’S WHEN YOU-- IF YOU HAVE NOTHING TO LOSE, THEN IT’S NO BIG DEAL. >> RIGHT. >> I JUST WOULD’VE-- IF I DIDN’T GET THE JOB I WOULD’VE HAD-- STILL HAD A REALLY COOL EXPERIENCE GETTING THE FIRST HAND EXPERIENCE OF WHAT THE ASTRONAUT SELECTION PROCESS IS LIKE, IF NOTHING ELSE. >> YEAH, I MEAN, WHAT IS IT LIKE, RIGHT? I MEAN, YOU SAY IT’S STRESSFUL AND THERE’S THINGS, BUT WHAT ARE THEY DOING THROUGHOUT THIS INTERVIEW PROCESS? >> WELL, I WOULD SAY IT’S-- I’M CERTAIN THAT THE PROCESS THAT THIS CLASS THAT REALLY HASN’T BEEN SELECTED YET, BUT IS IN THE PROCESS OF FINISHING BEING SELECTED. >> UH-HUH, AT THIS TIME THROUGH. >> I’M SURE THEIR-- I KNOW THEIR PROCESS HAS CHANGED SINCE WE WENT THROUGH, BUT THERE’S PSYCHOLOGICAL EXAMINATIONS THAT WE DID. >> OH, WOW. YEAH. >> THERE WAS GROUP PROBLEM SOLVING EXERCISES THAT WE DID. THERE WAS A LOT OF MEDICAL EXAMS, ESPECIALLY BY THE SECOND INTERVIEW. A LOT OF THAT IS CHECKING TO MAKE SURE THAT YOU DON’T HAVE ANY MEDICAL ISSUES. >> RIGHT. THERE ARE-- OF COURSE, THERE’S AN INTERVIEW. EACH TIME YOU COME TO VISIT NASA, THE FIRST TIME AND THE SECOND TIME, THERE’S AN HOUR LONG INTERVIEW. >> MM-HMM. >> THERE-- >> SO, IT’S TO TIMES THAT YOU COME? YOU COME-- >> WELL, THE FIRST TIME-- >> OKAY. >> FOR MY CLASS, THE FIRST TIME THEY INTERVIEWED PEOPLE THEY INVITED 120 PEOPLE TO COME. >> OKAY. >> AND THEN, OF THAT 120 THEY PARED IT DOWN TO 40 OR 50 FOR A SECOND INTERVIEW. >> WOW. >> AND BECAUSE THE MEDICAL EXAMS, YOU CAN IMAGINE ARE SO EXPENSIVE, THEY ONLY GIVE THE MEDICAL EXAMS MOSTLY TO THAT SMALLER GROUP. >> MAKES SENSE. I MEAN, HONESTLY, LIKE TO BE AN ASTRONAUT, NOT ONLY DO YOU HAVE TO BE SUPER SMART AND BE ABLE TO GET ALONG WITH YOUR CREWMATES AND EVERYTHING, BUT YOU HAVE TO MAKE SURE YOU’RE IN TIP TOP PHYSICAL SHAPE AND THAT NOTHING COULD POSSIBLY GO WRONG. YOU WERE FORTUNATE ENOUGH TO ACTUALLY GET THE CALL TO BE-- >> YES, YEAH. >> WHAT WAS THAT LIKE? WHERE WERE YOU? >> I WAS ACTUALLY IN THE MISSION CONTROL CENTER WORKING AS A CAPCOM THAT DAY. >> OH. >> SO IT WAS-- I’M PRETTY SURE THEY DIDN’T KNOW WHERE I WAS. I ANSWERED MY CELL PHONE AND IT WAS TOUGH BECAUSE I WAS SO EXCITED, BUT I WASN’T IN A SITUATION WHERE I WAS ALLOWED TO ANNOUNCE IT TO ANYBODY. >> RIGHT. >> SO I’M SITTING AROUND A WHOLE BUNCH OF OTHER PEOPLE THAT I’M WORKING WITH AND I JUST WANTED TO CHEER, BUT I JUST-- AND I HAD TO-- BUT, I WAS STILL WORKING ON CONSOLE. I HAD TO BE LISTENING FOR THE CREW TO CALL AND I HAD TO BE LISTENING TO WHAT THE GROUND WAS TALKING ABOUT. >> YEAH. >> SO I HAD TO JUST ACT LIKE IT DIDN’T HAPPEN AND JUST GET BACK TO WORK. >> SO, IN THAT SITUATION, FROM WHAT I UNDERSTAND, YOU’RE ONLY ALLOWED TO TELL VERY FEW PEOPLE, LIKE YOUR WIFE AND YOUR PARENTS. >> I TOLD MY WIFE-- YUP. >> AND THAT’S PRETTY MUCH IT. >> YEAH, I THINK I SENT MY WIFE AN EMAIL, TOLD HER WHAT HAD HAPPENED, AND THEN ONLY ABOUT THREE HOURS LATER DID I-- THAT I SENT HER ANOTHER EMAIL THAT SAID, “OH, AND DON’T TELL ANYBODY ELSE.” >> OH. [ LAUGHING ] >> YEAH, LET’S JUST SAY THAT WASN’T QUITE AS SUCCESSFUL AS I SHOULD’VE MADE IT. >> OH, MAN, THAT HAD TO BE-- I CAN’T EVEN IMAGINE JUST GETTING THAT CALL. THAT WOULD BE-- >> I WAS-- YEAH, I WAS PRETTY EXCITED. >> YEAH. >> LET’S GO BACK TO SOME OF THE OTHER TRAINING. SO YOU HAVE-- WE TALKED ABOUT A LITTLE JUST TRAINING FOR ON ORBIT, SURVIVAL TRAINING. HOW ABOUT, I GUESS, SOYUZ TRAINING. NOW, YOU SAID THAT NOW THEY SWITCHED THE CREWS AROUND AND NOW YOU HAVE TO LEARN A LOT MORE. NOW YOU HAVE TO-- YOU HAVE TO BE IN THE KIND OF NOT THE HOT SEAT BUT I GUESS ONE OF THE HOT SEATS? IS THAT HOW THAT WORKS? >> YES. >> OKAY. >> AS I INITIALLY STARTED TRAINING I WAS IN THE RIGHT SEAT. >> OKAY. >> WHICH HAS VERY LIMITED RESPONSIBILITIES. THE CREW-- WELL, EXAMPLE, JACK FISCHER AND FYODOR YURCHIKHIN, WHEN THEY LAUNCHED THEY DIDN’T HAVE ANYBODY IN THE RIGHT SEAT. >> RIGHT. >> THEY DON’T-- YOU DON’T NEED SOMEONE TO BE THERE. >> OKAY. >> THERE ARE SOME THINGS THAT ARE MORE UNCOMFORTABLE FOR-- IT’S VERY, VERY HELPFUL TO HAVE A RIGHT SEATER, AND I REALIZED THAT WHEN I STARTED TRAINING AS A LEFT SEATER BECAUSE YOU NEED SO MUCH MORE TIME TO TRAIN AS A LEFT SEATER. >> MM-HMM. >> YOU DON’T ALWAYS HAVE THE RIGHT SEATER THERE. AND SO, JUST HAVING AN ADDITIONAL PERSON WHO YOU CAN SAY, “HEY, REMIND ME WHEN-- TELL ME WHEN FIVE MINUTES GOES BY,” OR “CALCULATE AT WHAT RATE THE PRESSURE'S DROPPING SO THAT WE CAN FIGURE OUT HOW MUCH TIME WE HAVE TO-- CAN WE WAIT TO LAND AT OUR NOMINAL LANDING SPOT? OR DO WE HAVE TO START THE LANDING PROCESS IMMEDIATELY, WHEREVER THAT TAKES US?” I’M TALKING ABOUT SITUATIONS IN THE SIMULATIONS IN RUSSIA WHERE THEY’RE MAKING IT A REALLY BAD DAY IN THE SOYUZ. >> RIGHT. YEAH. >> SO, WHEN I CHANGED TO BEING A LEFT SEATER IT WAS A LOT-- YOU’RE REALLY HELPING TO OPERATE THE SPACECRAFT. >> MM-HMM. >> THE TRAINING’S GOOD, BUT YOU CAN IMAGINE THE FIRST TIME YOU’RE IN THERE PRESSING BUTTONS AND RECOGNIZING THAT, “IF I MESS THIS UP THIS IS REALLY GOING TO BE BAD.” AND I’VE DONE IT SO MANY TIMES NOW THAT I’M WELL PAST WORRYING ABOUT THAT. >> OH, YEAH. >> BUT, THERE'S A LOT THAT GOES ON AND IT’S-- THE TRAINERS THERE DO A REALLY GOOD JOB OF MAKING YOU READY FOR A REALLY, REALLY BAD DAY, BUT EVEN GIVEN SIX MALFUNCTIONS-- WELL, FOR EXAMPLE, ONE OF THE SIMULATIONS THAT I DON’T THINK I’LL EVER FORGET WAS WE WERE DOCKING WITH THE SPACE STATION AND THIS-- THE AUTOMATIC SYSTEMS TO DOCK HAD STOPPED WORKING, SO THE COMMANDER HAD TO TAKE OVER AND DO EVERYTHING MANUALLY. >> MM-HMM. >> AND THEN, WE GOT UP TO THE SPACE STATION, WE MADE CONTACT WITH THE SPACE STATION. I WAS EXPECTING THE SIMULATION TO END AT ANY MOMENT, BECAUSE ALL WE HAD TO DO AT THIS POINT WAS-- THE WAY THE SOYUZ DOCKING MECHANISM WORKS IS THERE’S A PROBE THAT STICKS OUT THE FRONT, AND THEN ONCE IT MAKES CONNECTION WITH THE SPACE STATION THEN THE NEXT STEP IS YOU RETRACT THAT PROBE AND THAT DRAWS THE TWO SPACECRAFTS TOGETHER. >> OKAY. >> SO WE’RE IN THAT SITUATION, WE’RE CONNECTED NOW TO THE SPACE STATION, BUT THE RETRACTION MECHANISM DIDN’T WORK. >> OH. >> SO WE COULDN’T GET THAT LAST DISTANCE TO CLOSE THE GAP WITH THE SPACE STATION. AND SO, WE’RE GOING THROUGH THE TROUBLESHOOTING FOR THAT. IT WASN’T-- NOTHING HAD TO HAPPEN SUPER FAST. >> MM-HMM. >> WE HAD TIME, SO WE’RE KIND OF GOING THROUGH THAT PROCEDURE. >> OKAY. >> AND THEN, IN THE MIDST OF THAT, SUDDENLY SIMULATED SMOKE STARTED COMING FROM UNDERNEATH THE SPACECRAFT. >> FANTASTIC. >> SO HERE WE ARE-- SO IN THE MIDST OF THAT, WE HAD A FIRE WHERE WE COULDN’T GET TO THE SPACE STATION. WE HAD TO DO AN EMERGENCY UNDOCKING AND THEN HAD-- SO WE HAD TO GO THROUGH THE WHOLE EMERGENCY DESCENT PROCESS. >> WOW. >> AND IT WAS JUST TOTAL-- IT WAS A LOT OF-- TONS OF STUFF HAD TO HAPPEN REALLY FAST AT THAT POINT. >> WOW. YEAH, BECAUSE I MEAN, IF YOU’RE GOING THROUGH THE SIMULATION YOU THINK, LIKE YOU SAID, THIS IS THE LAST THING. >> YEAH, I WAS MENTALLY KIND OF ON THE, LIKE, WINDING DOWN, LIKE, “OKAY, IT WON’T BE LONG NOW AND WE’LL BE DONE.” >> YEAH. >> AND THEN, IT WAS LIKE A WHOLE OTHER SIMULATION STARTED. >> WOW. OH, MY GOSH. THE THINGS YOU GUYS HAVE TO GO THROUGH IS JUST UNREAL. >> BUT, IT’S REALLY KIND OF COOL, TOO. >> IT IS. IT IS. BUT, THAT’S WHAT YOU HAVE TO DO, RIGHT? SO A LOT OF THE-- A LOT OF THE TRAINING IS NOT ONLY KIND OF UNDERSTANDING THE SYSTEMS AND DOING JUST THE DAY TO DAY STUFF, BUT REALLY, “HEY, IF THIS SCENARIO HAPPENS, THIS IS WHAT YOU DO. IF THIS SCENARIO”-- LIKE, A LOT OF PROCEDURAL STUFF. >> AND NOT ONLY THAT, BUT IT’S IMPORTANT THAT WE’RE DOING IT AS A CREW BECAUSE THE STYLES OF EACH PERSON ARE DIFFERENT. AND UNDERSTANDING WHAT THE EXPECTATIONS OF THAT SOYUZ COMMANDER ARE FOR ME AS A LEFT SEATER VERSUS THE CREW WHO HAD TRAINED FOR YEARS TO DO THAT ROLE WHERE I WAS GETTING ANOTHER SIX MONTHS TO DO THAT. >> YEAH. >> SO THE TEAMWORK ASPECT IS HUGE. >> RIGHT. I MEAN, THAT’S TRUE FOR SOME OF THESE THINGS, BUT ALSO, I GUESS, EVA TRAINING, TRAINING IN THE NEUTRAL BUOYANCY LABORATORY. >> YES. >> SO I’M SURE YOU’VE DONE THAT BEFORE, RIGHT? >> A LOT, YUP. >> YEAH, SO WHAT KIND-- HOW OFTEN HAVE YOU BEEN IN DOING THAT KIND OF TRAINING AND SORT OF WHAT IS IT LIKE? >> BEFORE I GOT ASSIGNED, I DID IT ABOUT AN AVERAGE OF SEVEN TIMES A YEAR. >> OKAY. >> AND I THINK I WAS KIND OF PUSHING TO GET MORE OPPORTUNITIES TO DO THAT. >> OKAY. >> NOW THAT I’VE BEEN ASSIGNED, IT’S PROBABLY BEEN A LITTLE LESS THAN THAT. >> INTERESTING. >> BUT, IT’S ALWAYS A SIX HOUR-- IT’S TYPICALLY SIX HOURS UNDERWATER-- >> RIGHT. >> --IN THE EXTERNAL MOBILITY UNIT IS WHAT WE CALL IT, THE SPACEWALKING SPACESUIT. >> MM-HMM, EMU. >> MM-HMM. AND JUST IN CASE PEOPLE AREN’T AWARE, THE WAY THAT WORKS IS THERE’S DIVERS THAT ARE AROUND US TO HELP BALANCE THE SUIT TO MAKE IT AS GOOD AS POSSIBLE A SIMULATION OF WEIGHTLESSNESS. >> RIGHT. >> IT’S-- BECAUSE OF THE AIR VOLUME IN THE SUIT AND THE FACT THAT THE SUIT IS ACTUALLY QUITE HEAVY, IT WOULD BE REALLY EASY TO END UP IN A SITUATION WHERE YOUR LEGS ARE REALLY, REALLY LIGHT AND YOUR CHEST IS HEAVY, AND YOU WOULDN’T HAVE THE STRENGTH TO FLIP YOURSELF SO THAT YOUR FEET ARE BACK UNDERNEATH YOU AGAIN. >> RIGHT. >> SO THE DIVERS WILL HELP TRY TO MAKE IT SEEM A LITTLE MORE LIKE YOU’RE OUT IN SPACE, HOWEVER, THE SUIT IS FLOATING. YOU’RE NOT FLOATING INSIDE THE SUIT. >> YEAH. >> SO IF YOU’RE UPSIDE DOWN IN THE SUIT THEN ALL THE WEIGHT OF YOUR BODY MIGHT BE RESTING ON YOUR SHOULDERS, SO IT’S-- IT CAN NEVER BE A PERFECT SIMULATION. >> YEAH. I GUESS, I MEAN, FROM WHAT I’VE HEARD IS KIND OF-- SO, LIKE YOU SAID IT, YOU’RE UNDERWATER IN THIS HUGE POOL THAT’S LIKE 40 FEET DEEP, JUST ENORMOUS, AND THEY HAVE FULL SCALE MOCKUPS OF THE ISS UNDERNEATH SO YOU CAN ACTUALLY KIND OF FEEL LIKE WHAT IT WOULD BE TO BE ON THE STATION AND HAVE KIND OF THE MUSCLE MEMORY TO KNOW, “OKAY, THIS IS HERE, AND THIS IS HERE, AND THEN THIS HANDRAIL’S HERE,” SO YOU KNOW KIND OF WHERE TO GRAB ON AND EVERYTHING. BUT, FROM WHAT I UNDERSTAND, IS YOU’RE RIGHT, IT’S PROBABLY AS CLOSE TO SIMULATING WHAT IT’S LIKE TO ACTUALLY DO A SPACEWALK AS POSSIBLE. >> MM-HMM. >> BUT, FIRST OF ALL, YEAH, IF YOU’RE UPSIDE DOWN IN SPACE, THAT’S IT, YOU’RE JUST UPSIDE DOWN BUT YOU’RE STILL KIND OF FLOATING IN THE SUIT. >> MM-HMM. >> WHEREAS, YOU STILL HAVE GRAVITY ON EARTH, SO YOU’RE RIGHT, YOU FEEL THE WHOLE WEIGHT. BUT THEN ALSO MOVING, YOU STILL HAVE THAT WATER RESISTANCE, RIGHT. >> THAT’S TRUE. THAT’S VERY TRUE. >> SO I GUESS THINGS FLY A LITTLE BIT QUICKER IN SPACE THAN THEY WOULD IF YOU WERE TO TOSS THEM OR MOVE YOUR HAND OR SOMETHING IN UNDERWATER. AND I’M SURE YOU’VE KIND OF NOTICED A LITTLE BIT OF THAT, RIGHT? AND MAYBE THE DIVERS ARE SORT OF-- ARE SORT OF PUSHING THINGS A LITTLE BIT FASTER SO THAT IT SIMULATES IT? >> NO, WE-- I THINK SOMETIMES BECAUSE IT’S SO HARD FOR THE DIVERS TO TELL WHAT YOU’RE TRYING TO DO. >> OKAY, YEAH. >. THEY TEND TO LIKE LET YOU DO WHAT YOU NEED TO DO, UNLESS THEY CAN TELL IF THERE’S A SITUATION WHERE IT’S CLEARLY NOT. OR, YOU MIGHT-- WHAT I STARTED DOING WITH THE DIVERS IS I REALIZED THAT SOME THINGS THERE’S NO NEED FOR YOU TO FIGHT THROUGH JUST TOUGHING SOMETHING OUT. >> MM-HMM. >> SOMETIMES THEY’LL SAY-- WELL, FOR EXAMPLE, WE HAVE A BODY RESTRAINT TETHER. >> OKAY. >> IT’S KIND OF LIKE A SNAKE THAT YOU CAN RIGIDIZE IN A CERTAIN SHAPE. >> MM-HMM. >> AND IT’S LIKE A THIRD ARM. YOU CAN USE IT TO ATTACH YOURSELF TO THE SPACE STATION SO YOU HAVE TWO HANDS FREE AND YOU CAN DO WORK. >> MM-HMM. >> OR, IF YOU HAVE A LARGE WHAT WE CALL AN ORU, AN ORBITAL REPLACEABLE UNIT. >> OKAY, IT’S LIKE A SPARE PART ALMOST? >> A SPARE PART. >> YEAH. RIGHT. >> IT COULD BE VERY LARGE. IT COULD BE REALLY TINY. >> OKAY. >> YOU CAN ATTACH THAT TO THAT BODY RESTRAINT TETHER AND TRANSLATE ALONG AND IT'LL JUST BE THERE. >> OKAY. >> WELL, IMAGINE THAT THAT THING WANTS TO FLOAT UP TO THE SURFACE OF THE WATER. >> RIGHT. >> OR WANTS TO SINK TO THE BOTTOM OF THE POOL. THE DIVERS WILL HOLD ON TO THAT, BUT THEN YOU COULD POTENTIALLY HAVE THIS ARM STICKING OFF OF YOUR HIP AND IF A DIVER DOESN’T REALIZE THAT YOU’RE TRYING REALLY HARD TO ROTATE TOWARDS YOUR RIGHT SHOULDER YOU’RE NOT JUST TRYING TO ROTATE YOURSELF, YOU’RE SUDDENLY TRYING TO ROTATE THIS DIVER WITH A TANK WHO’S HOLDING ON TO THAT. >> RIGHT. >> SO WHEN I REALIZED THAT THAT BECOMES AN ISSUE SOMETIMES IS THAT I JUST SAY, “HEY, I’M NOT SURE WHY, BUT I’M HAVING A HARD TIME ROTATING TOWARDS MY RIGHT SHOULDER.” AND THEN SUDDENLY IT’LL BECOME VERY EASY TO ROTATE TOWARDS MY RIGHT SHOULDER. >> SO YOU DON’T HAVE DIRECT COMMUNICATIONS WITH THE DIVERS THEN? >> OH, THERE’S UNDERWATER SPEAKERS. >> OH. >> SO EVERYTHING YOU’RE SAYING-- IF THERE’S A LOT OF NOISE UNDERWATER, BECAUSE WHEN WE DO SCUBA STUFF SOMETIMES IT IS HARD TO HEAR. >> UH-HUH. >> WHEN YOU’RE BLOWING BUBBLES OUT, THERE’S A LOT OF NOISE FROM THE BUBBLES. BUT IF THEY STOP BREATHING FOR A MOMENT THEY CAN HEAR WHAT YOU’RE SAYING AND THEY’RE REALLY, REALLY GOOD ABOUT KEEPING TRACK OF WHAT WE’RE SAYING. >> THAT’S RIGHT. YEAH, AND THEY DO-- I MEAN, I’VE SPOKEN WITH DIVERS IN THE PAST AND THEY DO-- SO YOU GUYS DO SIX HOUR KIND OF SIMULATIONS UNDERWATER AND THEY DO TWO HOUR ROTATIONS. >> MM-HMM. >> AND IT’S A LITTLE BIT DIFFERENT BECAUSE THE ASTRONAUTS ARE IN THE EMUs, SO YOU GUYS HAVE THE LIQUID COOLING GARMENT, AND YOU GUYS ARE AT A PRETTY GOOD TEMPERATURE. BUT FOR THEM, TWO HOURS IS A LONG TIME TO BE IN THE POOL AND THE TEMPERATURES, SO THEY DO THAT KIND OF ROTATION THING. >> YEAH, THAT’S TRUE. YEAH, IT’S ALSO PARTLY BECAUSE IT’S SUCH-- THEY’RE RESPONSIBLE FOR OUR SAFETY AND IT’S A VERY-- THEY’VE GOT TO BE VERY, VERY ATTENTIVE SO THEY GOT TO MAKE SURE THEY’RE SUPER ALERT. AND THERE ARE LIMITATIONS FOR HOW LONG YOU CAN DIVE ON THOSE TANKS. >> YEAH. YEAH. SO, I MEAN, ONE OF THE THINGS I THINK ABOUT WITH BEING AN ASTRONAUT AND PREPARING TO BE AN ASTRONAUT IS JUST HOW PHYSICALLY ABLE YOU HAVE TO BE. YOU HAVE TO-- BECAUSE YOU’RE TALK-- I MEAN, WE’RE TALKING ABOUT SPACESUITS, THESE ARE VERY HEAVY AND BEING ABLE TO SPEND SIX HOURS UNDERWATER IN A POOL, NOT EATING, YOU KNOW, I’D BE SO HUNGRY AFTER SIX HOURS. BUT, THINGS LIKE THAT, WHAT DO YOU DO TO STAY HEALTHY AND TO MAKE SURE YOU’RE PHYSICALLY AT YOUR PEAK TO MAKE SURE YOU’RE ABLE TO DO ALL OF THESE CRAZY THINGS-- SURVIVE IN RUSSIA IN THE WINTER, AND STUFF LIKE THAT? >> SO, I HAD A BOSS ONE TIME WHEN I FIRST-- EARLY IN MY ARMY CAREER, THAT SAID MAKE PHYSICAL TRAINING THE FIRST PRIORITY OF EVERY DAY. >> HMM. >> AND I THINK SOMETIMES WE DON’T GIVE OURSELVES PERMISSION TO DO THAT. WE MIGHT FEEL A LITTLE GUILTY, LIKE IT ALMOST SEEMS SELFISH. >> YEAH. >> BUT, BECAUSE MY BOSS TOLD ME THAT, IT REALLY IS SOMETHING THAT STUCK WITH ME AND I REALLY I CAN’T AFFORD TO ALWAYS MAKE IT THE FIRST PRIORITY OF EVERY DAY. >> MM-HMM. >> BUT, I’VE RECOGNIZED THAT IT REALLY DOES NEED TO BE A PRIORITY AND THE NICE THING ABOUT THIS JOB IS THE JOB GIVES US OPPORTUNITIES TO DO THAT. >> MM-HMM. >> IT’S GOT A GREAT FACILITY. WE’VE GOT GREAT TRAINERS AND WE’VE ALSO GOT-- IF WE INJURE OURSELVES WE’VE GOT PEOPLE THAT’LL HELP US GET REHABILITATED AS QUICKLY AS POSSIBLE. >> AND YOU GUYS-- THE ASTRONAUTS ACTUALLY HAVE THEIR OWN GYM HERE, RIGHT, AT THE JOHNSON SPACE CENTER? >> IT’S ACTUALLY NOT REALLY CALLED THE ASTRONAUT GYM. >> OH, OKAY. >> IT’S MORE DESIGNED TOWARDS A REHABILITATION FACILITY. >> OH. >> SO, WHEN PEOPLE COME BACK FROM SPACE, WE NEED-- THEY’VE GOT TO READAPT TO LIVING IN GRAVITY AGAIN. >> RIGHT. >> AND THAT’S REALLY THE PRIMARY FUNCTION. >> MM-HMM. >>IT WORKS OUT THAT AS A SECONDARY BENEFIT OF THAT IS WE GET SOME REALLY GOOD WORKOUT FACILITIES. >> THAT’S RIGHT. I REMEMBER TALKING WITH, AGAIN, SHANE KIMBROUGH A COUPLE WEEKS AGO, I THINK AT THIS POINT. YEAH, A COUPLE WEEKS AGO AND HE HAD-- I GOT THE CHANCE TO TALK WITH HIM JUST TWO DAYS AFTER HE LANDED. >> MM-HMM. >> AND HE WAS ALREADY WORKING OUT. IT’S CRAZY. I MEAN, HE WAS TALKING ABOUT BEING DIZZY JUST RIGHT AFTER LANDING, AND THEN, BAM, HE’S UP ON HIS FEET AND BEING REHABILITATED. >> MM-HMM. >> THAT’S CRAZY. SO, ARE THERE ANY OTHER SORT OF TRAINING ASPECTS THAT, LIKE, WE NEED TO KNOW BASED-- >> INTERESTING STUFF? >> YEAH, INTERESTING STUFF THAT YOU GO THROUGH THAT JUST, YOU KNOW, A CIVILIAN LIKE US DON’T REALLY GET TO EXPERIENCE. YOU KNOW, I KNOW ABOUT THE SURVIVAL TRAINING, ALL THE DIFFERENT THINGS THAT YOU DO TO PREPARE FOR BEING ON ORBIT, LEARNING ALL THE SYSTEMS, LEARNING HOW TO DO EVAs, ALL THESE DIFFERENT THINGS. >> YEAH, THERE’S ANOTHER FACILITY THAT I THINK IS REALLY, REALLY NEAT. IT’S CALLED THE VIRTUAL REALITY LAB HERE AT JOHNSON SPACE CENTER. >> OH. >> HAVE YOU EVER BEEN OVER THERE? >> YOU KNOW, I’VE SEEN IT. OH, IS THAT THE ONE WHERE YOU SIT IN THE CHAIR AND THEY PUT THE GOGGLES OVER YOU AND YOU HAVE THE HANDS-- YES, I’VE DONE THAT, YEAH. >> THAT’S AMAZING. THERE’S TWO THINGS THAT I’VE REALLY GOTTEN A KICK OUT OF LATELY DOING OVER THERE. ONE IS THE-- PRACTICING USING THE SAFER-- >> OH, OKAY. >> SO, EVERYTIME WE DO A SPACE WALK, WE’RE ALWAYS TETHERED TO THE SPACE STATION, SO THAT-- AND WE’RE LOCALLY TETHERED, SO IF YOU LET GO, YOU SHOULD STAY RIGHT WITHIN HANDS REACH OF SOMETHING. >> RIGHT. >> BUT ALSO ANOTHER, MUCH LONGER TETHER, JUST IN CASE WE MESS THAT UP, THAT WILL KEEP US SAFELY ATTACHED TO THE SPACE STATION. BUT IF WE MESS BOTH OF THOSE THINGS UP, THERE’S ALSO A THING CALLED THE SIMPLIFIED AID FOR EVA RESCUE. IT’S CALLED A SAFER. >> SAFER. >> IT LOOKS LIKE A BACKPACK THAT WE WEAR THAT’S BASICALLY A JET PACK. >> YEAH. >> BUT IT’S GOT VERY LIMITED RESOURCES AND YOU NEED TO KNOW HOW TO USE IT. SO, TO PRACTICE FLYING YOURSELF AS AN INDEPENDENT SPACECRAFT BACK TO THE SPACE STATION REQUIRES A LITTLE BIT OF TRAINING. SO, WHAT THEY DO IN THAT TRAINING IS THEY’LL TELL YOU, “OKAY, HERE’S WHERE WE’RE GOING TO START. YOU CAN SEE THE SPACE STATION RIGHT THERE.” I MEAN, YOU’RE WEARING THOSE GOGGLES, SO YOU CAN LOOK IN ANY DIRECTION AND YOU SEE EITHER STARS OR THE EARTH OR THE SPACE STATION. >> MM-HMM. >> AND THEN, THEY’LL SAY, “OKAY, WE’RE GOING TO START THE SIMULATION.” AND THEY’LL PUSH YOU OFF OF THE SPACE STATION. >> WHOA! >> SO THE SPACE STATION WILL BE SPINNING AND YOU’LL BE-- THE DISTANCE WILL BE INCREASING BETWEEN YOU AND THE SPACE STATION. >> SO YOU’RE SORT OF TUMBLING IN THIS SIMULATION, RIGHT? >> YES, ABSOLUTELY. >> OH, WHOA! >> AND YOU HAVE TO DO THAT BECAUSE IT TAKES A LITTLE BIT OF TIME FOR-- THEY KNOW THAT IT TAKES SOME TIME TO DEPLOY THE SAFER AND THE HAND CONTROLLERS AND THINGS LIKE THAT. >> OKAY. >> SO, MAYBE TEN SECONDS. I CAN’T REMEMBER EXACTLY. >> MM-HMM. >> AND THEY’LL TELL YOU-- BECAUSE, YOU’RE INITIALLY-- THEY DON’T HAVE A MOCK UP WHERE YOU HAVE TO ACTUALLY DEPLOY THE SAFER. YOU START OFF WITH HOLDING IT IN YOUR HANDS. >> OH. >> BECAUSE THEY KNOW IT’S GOING TO TAKE SOME TIME, THEY DON’T LET YOU START IT RIGHT AWAY. >> MAKES SENSE, OKAY. >> SO THEY’LL SAY, “OKAY, NOW YOU CAN START IT.” BUT, THE FIRST THING YOU’VE GOT TO DO IS CALL THE GROUND AND SAY, “HEY, THIS IS EV2. I’M NOT CONNECTED TO THE SPACE STATION. I’M HEADING NADIR AND I’M DEPLOYING THE SAFER.” WHICH, YOU CAN IMAGINE, WOULD BE A VERY UNCOMFORTABLE SITUATION. >> OH, YEAH. YEAH, THAT’S A VERY CALM WAY OF SAYING, “HEY, I’M PLUMMETING TOWARDS EARTH, BY THE WAY.” >> AND IT’S A PRETTY SLOW SPEED, THANKFULLY. >> THAT’S TRUE. >> BECAUSE IT WOULD HAVE TO BE A SPEED WHERE YOU PUSHED YOURSELF OFF. >> OKAY, OKAY. >> BUT THE SAFER’S REALLY NEAT. ONCE YOU DEPLOY IT, IT WILL STOP ITSELF. SO, YOU MIGHT BE SPINNING, BUT ONCE YOU-- IT’S GOT SENSORS, SO IT WILL STOP ALL THE ROTATIONS. SO, YOU’LL BE FIXED IN ONE LOCATION. IT MIGHT BE LOOKING AWAY FROM THE SPACE STATION, BUT AT LEAST YOU’RE NOT ROTATING ANYMORE. AND THEN WE’RE TRAINED FIRST TO YAW, TO FIND THE SPACE STATION. >> OKAY. >> AND THEN-- SO WE START THAT YAW AND THEN ONCE YOU GET TO THE RIGHT STOP PLACE, THEN YOU PRESS A BUTTON AND IT’LL STOP THAT ROTATION AGAIN. >> FANCY. >> BASICALLY, YOU HAVE A LITTLE BIT OF AN IMPULSE. DON’T USE UP MUCH OF THE RESOURCES. >> RIGHT. >> YOU WAIT, BE PATIENT, WAIT FOR THE SPACE STATION TO BE LINED UP, AND THEN YOU STOP IT, AND THEN YOU CAN ADJUST YOUR PITCH. >> OKAY. >> GIVE IT JUST A LITTLE BIT, BE PATIENT, WAIT SO YOU’RE JUST LINED UP. AND THEN YOU CHANGE IT FROM ADJUSTING ROTATIONS TO ADJUSTING THE TRANSLATIONS. >> OKAY. >> SO, IDEALLY, AT THAT POINT, YOU’RE LINED UP EXACTLY WHERE YOU WANT TO GO, WHICH SHOULD BE EXACTLY WHERE YOU LEFT FROM, AND THEN YOU JUST GIVE IT A POSITIVE X. SO YOU START TRANSLATING DIRECTLY TOWARDS IT, JUST A LITTLE BIT. AND, IF YOUR AIM IS GOOD, YOU SHOULDN’T HAVE TO MAKE ANY ADJUSTMENTS AND YOU HAVE PLENTY OF RESOURCES TO GET BACK. >> ALL RIGHT. >> IF YOU MESS UP-- MAYBE YOU FORGOT HOW TO CONTROL IT-- YOU COULD BURN THROUGH HALF OF YOUR STUFF AND JUST COMPLETELY MISS THE SPACE STATION. >> OKAY, SO, IT’S NOT LIKE A JETPACK HOW YOU WOULD IMAGINE IN LIKE A SCI-FI MOVIE, WHERE YOU’RE JUST KIND OF ZOOMING AROUND. IT’S STOP, PRESS A BUTTON, TURN, PRESS A BUTTON, LEAN FORWARD, OR WHATEVER IT IS. >> YOU DON’T WANT TO OVERDO ANY OF THOSE THINGS. >> RIGHT. >> YOU WANT TO DO EVERYTHING-- YOU WANT TO BE VERY CALM ABOUT IT. >> VERY METHODICAL, YEAH. >> AND THEY’LL DO IT AT A VARIETY OF LOCATIONS. THEY’LL DO IT FROM DIFFERENT VELOCITIES OF SEPARATION. >> OKAY. >> SO, THAT’S REALLY GOOD TRAINING. >> YEAH. >> ANOTHER THING-- DO YOU HAVE ANY QUESTIONS ABOUT THAT? >> NO-- WELL, I MEAN, THE ONE THING I WAS GOING TO ASK WAS: DO YOU GUYS HAVE A COMPETITION TO SEE HOW ACCURATE YOU CAN GO ON THAT FIRST-- BECAUSE YOU SAID YOU’VE GOT TO LINE UP AND THE HOPE IS THAT YOU PRESS THE BUTTON ONCE AND THEN YOU GO RIGHT WHERE-- DO YOU GUYS HAVE COMPETITIONS TO SEE WHO’S THE MOST ACCURATE? >> I HAVEN’T EVER WALKED OUT OF THERE AND TRIED TO COMPARE HOW MUCH PROPELLANT I HAD LEFT TO SOMEBODY ELSE. BUT MAYBE THAT MIGHT BE A GOOD THING TO DO IN THE FUTURE. WE’LL HAVE LIKE AN ASTRONAUT OLYMPICS. >> YEAH, THAT WOULD BE FUN. >> THAT WOULD BE REALLY FUN. >> YEAH! >> OR REALLY HUMBLING. >> YEAH! GO THROUGH THE TRAINING AND SEE-- DO LIKE LITTLE THINGS LIKE THAT. >> “HOW’D YOU SCORE?” >> “I HAD THIS MUCH PROPELLANT LEFT.” >> “OOH! I HAD THIS MUCH.” >> NO, BUT GO ON. YOU WERE GOING TO SAY SOMETHING ELSE. >> OH, ANOTHER THING THAT I THOUGHT WAS REALLY INTERESTING IN VIRTUAL REALITY LAB IS THEY TRAIN YOU HOW TO DO MASS HANDLING. SO, YOU PUT ON THOSE GLASSES AGAIN. >> OKAY. >> THIS TIME, AGAIN, YOU’RE SITTING IN THE CHAIR. BUT THEY HAVE, BASICALLY, HANDLES, LIKE WE WOULD HAVE FOR AN ORU. >> MM-HMM. >> IT COULD BE SOMETHING THAT, IN SPACE, HAS A MASS OF 1,000 KILOGRAMS. IT COULD BE SOMETHING THAT’S 200 KILOGRAMS. BUT THEY CAN SET UP THE COMPUTER, THE SIMULATION TO OPERATE THAT WAY. AND IT’S ATTACHED TO A BUNCH OF STRINGS IN EACH DIRECTION. >> OH. >> SO, YOU CAN START IT MOVING AND YOU’LL FEEL THE FORCE. AS YOU GET IT MOVING-- YOU CAN IMAGINE IF IT’S A TON-- >> RIGHT. >> AS YOU GET IT MOVING, IT’S HARDER TO GET IT TO STOP MOVING. AND MAYBE IT’S HARD TO GET-- >> OH. >> SO THINGS ARE, WE CALL IT, WEIGHTLESS. >> RIGHT. >> BUT THEY HAVE A LOT OF INERTIA. THEY HAVE THE SAME AMOUNT OF INERTIA AS THEY HAVE ON THE GROUND. >> MM-HMM. >> IF SOMETHING WEIGHS A LOT, IT’S GOING TO TAKE MORE FORCE TO GET IT STARTED MOVING-- >> MM-HMM. >> --AND MORE FORCE TO STOP IT MOVING. AND IT’S A REALLY INTERESTING-- IT’S THE CLOSEST TO DEALING WITH WEIGHTLESSNESS THAT I’VE EVER FELT, BECAUSE I HAD A LARGE OBJECT THAT I NEEDED TO LINE UP OVER SOME PINS. AND THEN, ONCE I GOT IT OVER THE PINS, I HAD TO LOWER IT DOWN. THE FIRST TIME I DID IT, I THINK, AS MOST PEOPLE WOULD, YOU HAVE A TENDENCY TO WANT TO BE MOVING IT ALL THE TIME. SO, I GRABBED THIS OBJECT. IT SEEMS REALLY HEAVY. I GET IT STARTED MOVING, BUT I KIND OF KEEP PUSHING IT. I’M USING MY STRENGTH TO KEEP IT MOVING. >> RIGHT. >> AND THEN, I HAD TO USE EVEN MORE STRENGTH TO GET IT TO STOP MOVING. THE SECOND TIME I DID IT, I REALIZED THAT ONCE I GOT IT STARTED MOVING I COULD ALMOST-- I COULD TAKE MY HAND-- BECAUSE IT WAS ALREADY MOVING. NOTHING’S GOING TO STOP IT FROM MOVING. >> MM-HMM. >> SO, ONCE I GOT IT JUST MOVING REALLY SLOWLY I JUST PUT MY FINGERTIPS ON THOSE HANDLES AND THEY KEPT MOVING. >> OH. >> AND THEN I JUST-- VERY RELAXED AND VERY CALMLY WAITED FOR IT TO GET TO THE RIGHT SPOT. AND I GAVE IT VERY LITTLE PRESSURE TO STOP IT, THIS MASSIVE OBJECT. >> WOW! >> AND THEN I-- WHEN I WANTED TO MOVE IT DOWN-- I JUST GAVE IT A LITTLE BIT OF A NUDGE. AS SOON AS I KNEW THAT IT WAS MOVING IN THE RIGHT DIRECTION, I JUST USED MY FINGERTIPS AND LET IT GO. AND I SUSPECT, WHEN YOU’RE IN SPACE, DOING A SPACE WALK THAT, BECAUSE WE’RE IN THE POOL, YOU’RE GOING TO HAVE THIS TENDENCY, WHEN WE WERE TRAINING AS A NEWBIE, TO WANT TO FEEL LIKE YOU’VE GOT TO CONTINUOUSLY FORCE YOURSELF TO KEEP MOVING. >> RIGHT. >> BUT ONCE YOU START GETTING YOURSELF TO MOVE IN THE RIGHT DIRECTION, YOU JUST HAVE TO USE FINGERTIP PRESSURE TO TEND YOURSELF AND MAKE SURE YOU’RE CONTINUING TO DO THE RIGHT THING. >> SO, THAT’S THE NICE PAIRING BETWEEN DOING SIMULATION RUNS IN THE NEUTRAL BUOYANCY LABORATORY AND THEN GOING TO THE VIRTUAL REALITY AND DOING-- YOU JUST GET A DIFFERENT PERSPECTIVE. >> EXACTLY. IN THE NBL-- IN THE NEUTRAL BUOYANCY LAB-- >> YEAH. >> YOU CAN MOVE 100 METERS. >> MM-HMM. >> IN THE VIRTUAL REALITY LAB, YOU CAN MOVE ABOUT A FOOT. YOU CAN MOVE SOMETHING ABOUT A FOOT. SO, IT’S REALLY JUST A FINE TUNING OF THINGS. >> IT’S THE LITTLE THINGS. BUT THEY’RE REALLY IMPORTANT, RIGHT? >> ABSOLUTELY. >> KNOWING THAT IF YOU TRY TO TUG THIS BIG, MASSIVE OBJECT REALLY, REALLY FAST, IT’S GOING TO BE REALLY HARD TO STOP. >> YES. >> THOSE ARE LITTLE THINGS BUT, ALSO, EXTREMELY IMPORTANT. ALL RIGHT. WELL, MARK, THANKS FOR TAKING THE TIME TO ACTUALLY SIT DOWN AND TALK THROUGH SOME OF THE ASTRONAUT TRAINING AND WHAT IT WAS LIKE TO BE SELECTED AS AN ASTRONAUT, ALL OF THE ABOVE. I KNOW YOU’RE VERY BUSY, SO I KNOW THIS IS A BIG CHUNK OF TIME FOR YOU. SO, THAT WAS AWESOME. BUT, FOR THE LISTENERS, IF YOU WANT TO KNOW MORE, AND FOLLOW MARK’S JOURNEY ONCE HE GOES TO THE INTERNATIONAL SPACE STATION, STAY TUNED UNTIL AFTER THE MUSIC CLOSING CREDITS THAT WE HAVE HERE AND WE’LL TELL YOU EXACTLY WHERE YOU NEED TO GO. SO, THANKS AGAIN, MARK, FOR COMING ON THE SHOW. >> THANK YOU. [ MUSIC ] >> HOUSTON, GO AHEAD. >> I’M ON THE SPACE SHUTTLE. >> ROGER, ZERO-G AND I FEEL FINE. >> SHUTTLE HAS CLEARED THE TOWER. >> WE CAME IN PEACE FOR ALL MANKIND. >> IT’S ACTUALLY A HUGE HONOR TO BREAK THE RECORD LIKE THIS. >> NOT BECAUSE THEY ARE EASY, BUT BECAUSE THEY ARE HARD. >> HOUSTON, WELCOME TO SPACE. >> HEY, THANKS FOR STICKING AROUND. SO, TODAY WE TALKED WITH MARK VANDE HEI. HE’S GOING TO BE LAUNCHING TO THE INTERNATIONAL SPACE STATION LATER THIS YEAR OR MAYBE RIGHT NOW, DEPENDING ON WHEN THIS PODCAST GETS POSTED. BUT MARK IS ON SOCIAL MEDIA. HE’S ON TWITTER @ASTRO_SABOT. THAT’S S-A-B-O-T, AND YOU CAN FOLLOW HIS JOURNEY ABOARD THE INTERNATIONAL SPACE STATION AS HE TALKS ABOUT HIS DAY-TO-DAY LIFE AND MAYBE TAKES SOME PHOTOS FROM THAT VANTAGE POINT 250 MILES ABOVE THE EARTH. YOU CAN ALSO SEE HIS JOURNEY AT NASA.GOV/ISS. WE HAVE UPDATES ALL THE TIME ON WHAT’S GOING ON ABOARD THE INTERNATIONAL SPACE STATION. SOME OF THE RESEARCH STUDIES AND EXPERIMENTS THAT MARK WILL BE TAKING PART OF WHILE HE’S ABOARD. ON SOCIAL MEDIA, WE’RE VERY ACTIVE. JUST GO TO FACEBOOK, TWITTER, OR INSTAGRAM. ON FACEBOOK IT’S INTERNATIONAL SPACE STATION, ON TWITTER IT’S @SPACE_STATION, AND ON INSTAGRAM IT’S @ISS. WE’LL BE FOLLOWING MARK THROUGHOUT HIS JOURNEY AND POSTING PICTURES OF HIM AND SOME OF THE THINGS THAT HE’S DOING WHILE ON THAT ORBITING COMPLEX. YOU CAN ALSO USE THE #ASKNASA ON ANY ONE OF THOSE PLATFORMS AND SUBMIT AN IDEA FOR THE PODCAST, MAYBE ASK ANY QUESTIONS, AND WE’LL MAKE SURE TO ANSWER IT IN A LATER PODCAST. THIS PODCAST WAS RECORDED ON MAY THE 4th. THAT’S RIGHT, WE RECORDED TWO PODCASTS ON MAY THE 4th. MAY THE FOURTH BE WITH YOU. SUPER LATE. I’M STILL GOING TO SAY IT. AND SPECIAL THANKS TO JOHN STOLL, ALEX PERRYMAN, PAT RYAN, AND JOHN STREETER FOR MAKING THIS PODCAST HAPPEN. AND THANKS AGAIN TO MR. MARK VANDE HEI FOR COMING ON THE SHOW. WE’LL BE BACK NEXT WEEK.

Physical Human Activity Recognition Using Wearable Sensors.

PubMed

Attal, Ferhat; Mohammed, Samer; Dedabrishvili, Mariam; Chamroukhi, Faicel; Oukhellou, Latifa; Amirat, Yacine

2015-12-11

This paper presents a review of different classification techniques used to recognize human activities from wearable inertial sensor data. Three inertial sensor units were used in this study and were worn by healthy subjects at key points of upper/lower body limbs (chest, right thigh and left ankle). Three main steps describe the activity recognition process: sensors' placement, data pre-processing and data classification. Four supervised classification techniques namely, k-Nearest Neighbor (k-NN), Support Vector Machines (SVM), Gaussian Mixture Models (GMM), and Random Forest (RF) as well as three unsupervised classification techniques namely, k-Means, Gaussian mixture models (GMM) and Hidden Markov Model (HMM), are compared in terms of correct classification rate, F-measure, recall, precision, and specificity. Raw data and extracted features are used separately as inputs of each classifier. The feature selection is performed using a wrapper approach based on the RF algorithm. Based on our experiments, the results obtained show that the k-NN classifier provides the best performance compared to other supervised classification algorithms, whereas the HMM classifier is the one that gives the best results among unsupervised classification algorithms. This comparison highlights which approach gives better performance in both supervised and unsupervised contexts. It should be noted that the obtained results are limited to the context of this study, which concerns the classification of the main daily living human activities using three wearable accelerometers placed at the chest, right shank and left ankle of the subject.

Physical Human Activity Recognition Using Wearable Sensors

PubMed Central

Attal, Ferhat; Mohammed, Samer; Dedabrishvili, Mariam; Chamroukhi, Faicel; Oukhellou, Latifa; Amirat, Yacine

2015-01-01

This paper presents a review of different classification techniques used to recognize human activities from wearable inertial sensor data. Three inertial sensor units were used in this study and were worn by healthy subjects at key points of upper/lower body limbs (chest, right thigh and left ankle). Three main steps describe the activity recognition process: sensors’ placement, data pre-processing and data classification. Four supervised classification techniques namely, k-Nearest Neighbor (k-NN), Support Vector Machines (SVM), Gaussian Mixture Models (GMM), and Random Forest (RF) as well as three unsupervised classification techniques namely, k-Means, Gaussian mixture models (GMM) and Hidden Markov Model (HMM), are compared in terms of correct classification rate, F-measure, recall, precision, and specificity. Raw data and extracted features are used separately as inputs of each classifier. The feature selection is performed using a wrapper approach based on the RF algorithm. Based on our experiments, the results obtained show that the k-NN classifier provides the best performance compared to other supervised classification algorithms, whereas the HMM classifier is the one that gives the best results among unsupervised classification algorithms. This comparison highlights which approach gives better performance in both supervised and unsupervised contexts. It should be noted that the obtained results are limited to the context of this study, which concerns the classification of the main daily living human activities using three wearable accelerometers placed at the chest, right shank and left ankle of the subject. PMID:26690450

An investigation of the usability of sound recognition for source separation of packaging wastes in reverse vending machines.

PubMed

Korucu, M Kemal; Kaplan, Özgür; Büyük, Osman; Güllü, M Kemal

2016-10-01

In this study, we investigate the usability of sound recognition for source separation of packaging wastes in reverse vending machines (RVMs). For this purpose, an experimental setup equipped with a sound recording mechanism was prepared. Packaging waste sounds generated by three physical impacts such as free falling, pneumatic hitting and hydraulic crushing were separately recorded using two different microphones. To classify the waste types and sizes based on sound features of the wastes, a support vector machine (SVM) and a hidden Markov model (HMM) based sound classification systems were developed. In the basic experimental setup in which only free falling impact type was considered, SVM and HMM systems provided 100% classification accuracy for both microphones. In the expanded experimental setup which includes all three impact types, material type classification accuracies were 96.5% for dynamic microphone and 97.7% for condenser microphone. When both the material type and the size of the wastes were classified, the accuracy was 88.6% for the microphones. The modeling studies indicated that hydraulic crushing impact type recordings were very noisy for an effective sound recognition application. In the detailed analysis of the recognition errors, it was observed that most of the errors occurred in the hitting impact type. According to the experimental results, it can be said that the proposed novel approach for the separation of packaging wastes could provide a high classification performance for RVMs. Copyright © 2016 Elsevier Ltd. All rights reserved.

Sequence-based heuristics for faster annotation of non-coding RNA families.

PubMed

Weinberg, Zasha; Ruzzo, Walter L

2006-01-01

Non-coding RNAs (ncRNAs) are functional RNA molecules that do not code for proteins. Covariance Models (CMs) are a useful statistical tool to find new members of an ncRNA gene family in a large genome database, using both sequence and, importantly, RNA secondary structure information. Unfortunately, CM searches are extremely slow. Previously, we created rigorous filters, which provably sacrifice none of a CM's accuracy, while making searches significantly faster for virtually all ncRNA families. However, these rigorous filters make searches slower than heuristics could be. In this paper we introduce profile HMM-based heuristic filters. We show that their accuracy is usually superior to heuristics based on BLAST. Moreover, we compared our heuristics with those used in tRNAscan-SE, whose heuristics incorporate a significant amount of work specific to tRNAs, where our heuristics are generic to any ncRNA. Performance was roughly comparable, so we expect that our heuristics provide a high-quality solution that--unlike family-specific solutions--can scale to hundreds of ncRNA families. The source code is available under GNU Public License at the supplementary web site.

Recognition of surgical skills using hidden Markov models

NASA Astrophysics Data System (ADS)

Speidel, Stefanie; Zentek, Tom; Sudra, Gunther; Gehrig, Tobias; Müller-Stich, Beat Peter; Gutt, Carsten; Dillmann, Rüdiger

2009-02-01

Minimally invasive surgery is a highly complex medical discipline and can be regarded as a major breakthrough in surgical technique. A minimally invasive intervention requires enhanced motor skills to deal with difficulties like the complex hand-eye coordination and restricted mobility. To alleviate these constraints we propose to enhance the surgeon's capabilities by providing a context-aware assistance using augmented reality techniques. To recognize and analyze the current situation for context-aware assistance, we need intraoperative sensor data and a model of the intervention. Characteristics of a situation are the performed activity, the used instruments, the surgical objects and the anatomical structures. Important information about the surgical activity can be acquired by recognizing the surgical gesture performed. Surgical gestures in minimally invasive surgery like cutting, knot-tying or suturing are here referred to as surgical skills. We use the motion data from the endoscopic instruments to classify and analyze the performed skill and even use it for skill evaluation in a training scenario. The system uses Hidden Markov Models (HMM) to model and recognize a specific surgical skill like knot-tying or suturing with an average recognition rate of 92%.

High-precision Pb Isotopes Reveal Two Small Magma Bodies Beneath the Summit of Kilauea Volcano

NASA Astrophysics Data System (ADS)

Pietruszka, A. J.; Heaton, D. E.; Marske, J. P.; Garcia, M. O.

2013-12-01

The summit magma storage reservoir of Kilauea Volcano is one of the most important components of the volcano's magmatic plumbing system, but its geometry is poorly known. High-precision Pb isotopic analyses of Kilauea summit lavas (1959-1982) define the minimum number of magma bodies within the summit reservoir and their volumes. The 206Pb/204Pb ratios of these lavas display a temporal decrease due to changes in the composition of the parental magma delivered to the volcano. Analyses of multiple lavas from some individual eruptions reveal small but significant differences in 206Pb/204Pb. The extra-caldera lavas from Aug. 1971 and Jul. 1974 display lower Pb isotope ratios and higher MgO contents (10 wt. %) than the intra-caldera lavas (MgO ~7-8 wt. %) from each eruption. From 1971 to 1982, the 206Pb/204Pb ratios of the lavas define two separate decreasing temporal trends. The intra-caldera lavas from 1971, 1974, 1975, Apr. 1982 and the lower MgO lavas from Sep. 1982 have higher 206Pb/204Pb ratios at a given time (compared to the extra-caldera lavas and the higher MgO lavas from Sep. 1982). These trends require that the intra- and extra-caldera lavas (and the Sep. 1982 lavas) were supplied from two separate, partially isolated magma bodies. Numerous studies (Fiske and Kinoshita, 1969; Klein et al., 1987) have long identified the locus of Kilauea's summit reservoir ~2 km southeast of Halemaumau (HMM) at a depth of ~2-7 km, but more recent investigations have discovered a second magma body located <1 km below the east rim of HMM (Battaglia et al., 2003; Johnson et al., 2010). The association between the vent locations of the extra-caldera lavas near the southeast rim of the caldera and their higher MgO contents suggests that these lavas tapped the deeper magma body. In contrast, the lower MgO intra-caldera lavas were likely derived from the shallow magma body beneath HMM. Residence time modeling based on the Pb isotope ratios of the lavas suggests that the magma volume of the deeper body is ~0.2 km3, whereas the shallow body holds a minimum of ~0.04 km3 of magma. These estimates are smaller than a previous calculation of ~2-3 km3 for Kilauea's summit reservoir based on trace element ratios (Pietruszka and Garcia, 1999), but are similar to the volume of the magma body that underlies Piton de la Fournaise Volcano on Réunion Island (Albarède, 1993).

Economic Evaluation of Voice Recognition (VR) for the Clinician’s Desktop at the Naval Hospital Roosevelt Roads

DTIC Science & Technology

1997-09-01

first PC-based, very large vocabulary dictation system with a continuous natural language free flow approach to speech recognition. (This system allows...indicating the likelihood that a particular stored HMM reference model is the best match for the input. This approach is called the Baum-Welch...InfoCentral, and Envoy 1.0; and Lotus Development Corp.’s SmartSuite 3, Approach 3.0, and Organizer. 2. IBM At a press conference in New York in June 1997, IBM

Modern Computational Techniques for the HMMER Sequence Analysis

PubMed Central

2013-01-01

This paper focuses on the latest research and critical reviews on modern computing architectures, software and hardware accelerated algorithms for bioinformatics data analysis with an emphasis on one of the most important sequence analysis applications—hidden Markov models (HMM). We show the detailed performance comparison of sequence analysis tools on various computing platforms recently developed in the bioinformatics society. The characteristics of the sequence analysis, such as data and compute-intensive natures, make it very attractive to optimize and parallelize by using both traditional software approach and innovated hardware acceleration technologies. PMID:25937944

The Role of miRNAs in the Progression of Prostate Cancer from Androgen-Dependent to Androgen-Independent Stages

DTIC Science & Technology

2012-09-01

regulated by miR-99a/let7c/125b-2 cluster. Using bioinformatic prediction algorithm TargetScan, we identified 7 genes that are commonly targeted by miR-99a...HPeak, a Hidden Markov Model (HMM)-based peak identifying algorithm (http://www.sph.umich.edu/csg/qin/HPeak/). Seven AR binding sites were reported by...and ARBS2 by ALGGEN- PROMO, a matrix algorithm for predicting transcription factor binding sites based on TRANSFAC (http://alggen.lsi.upc.es/cgi- bin

Maxwell AFB, Montgomery, Alabama. Revised Uniform Summary of Surface Weather Observations (RUSSWO)

DTIC Science & Technology

1974-09-19

VISION GAS . MAY 00-02 09 3,8 90 3,8 499 7,7 .1 11.6 3150 ___03.05, 1.1 4,0 1__ __ 4t, 15t6 139, *1 24#7 3188 0o.o13l ,5 397 __ 3t7 10,8 17,6 1__ 1 2497...WINDDIRECTION AND SPEED i (FROM HOURLY OBSERVATIONS) i ( ..1AXWELL AF B A-AA/HMM15C)MIRY 37m,72 sr-P STA TIONl STATIO lolUNK Tgllil "Ol N CLADA NOPAll (IS T

AdOn HDP-HMM: An Adaptive Online Model for Segmentation and Classification of Sequential Data.

PubMed

Bargi, Ava; Xu, Richard Yi Da; Piccardi, Massimo

2017-09-21

Recent years have witnessed an increasing need for the automated classification of sequential data, such as activities of daily living, social media interactions, financial series, and others. With the continuous flow of new data, it is critical to classify the observations on-the-fly and without being limited by a predetermined number of classes. In addition, a model should be able to update its parameters in response to a possible evolution in the distributions of the classes. This compelling problem, however, does not seem to have been adequately addressed in the literature, since most studies focus on offline classification over predefined class sets. In this paper, we present a principled solution for this problem based on an adaptive online system leveraging Markov switching models and hierarchical Dirichlet process priors. This adaptive online approach is capable of classifying the sequential data over an unlimited number of classes while meeting the memory and delay constraints typical of streaming contexts. In this paper, we introduce an adaptive ''learning rate'' that is responsible for balancing the extent to which the model retains its previous parameters or adapts to new observations. Experimental results on stationary and evolving synthetic data and two video data sets, TUM Assistive Kitchen and collated Weizmann, show a remarkable performance in terms of segmentation and classification, particularly for sequences from evolutionary distributions and/or those containing previously unseen classes.

Implementation of a health management mentoring program: year-1 evaluation of its impact on health system strengthening in Zambézia Province, Mozambique

PubMed Central

Edwards, Laura J.; Moisés, Abú; Nzaramba, Mathias; Cassimo, Aboobacar; Silva, Laura; Mauricio, Joaquim; Wester, C. William; Vermund, Sten H.; Moon, Troy D.

2015-01-01

Background: Avante Zambézia is an initiative of a Non-Governmental Organization (NGO), Friends in Global Health, LLC (FGH) and the Vanderbilt Institute for Global Health (VIGH) to provide technical assistance to the Mozambican Ministry of Health (MoH) in rural Zambézia Province. Avante Zambézia developed a district level Health Management Mentorship (HMM) program to strengthen health systems in ten of Zambézia’s 17 districts. Our objective was to preliminarily analyze changes in four domains of health system capacity after the HMM’s first year: accounting, Human Resources (HRs), Monitoring and Evaluation (M&E), and transportation management. Methods: Quantitative metrics were developed in each domain. During district visits for weeklong, on-site mentoring, the health management mentoring teams documented each indicator as a success ratio percentage. We analyzed data using linear regressions of each indicator’s mean success ratio across all districts submitting a report over time. Results: Of the four domains, district performance in the accounting domain was the strongest and most sustained. Linear regressions of mean monthly compliance for HR objectives indicated improvement in three of six mean success ratios. The M&E capacity domain showed the least overall improvement. The one indicator analyzed for transportation management suggested progress. Conclusion: Our outcome evaluation demonstrates improvement in health system performance during a HMM initiative. Evaluating which elements of our mentoring program are succeeding in strengthening district level health systems is vital in preparing to transition fiscal and managerial responsibility to local authorities. PMID:26029894

Actin filaments in the acrosomal reaction of Limulus sperm. Motion generated by alterations in the packing of the filaments.

PubMed

Tilney, L G

1975-02-01

When Limulus sperm are induced to undergo the acrosomal reaction, a process, 50 mum in length, is generated in a few seconds. This process rotates as it elongates; thus the acrosomal process literally screws through the jelly of the egg. Within the process is a bundle of filaments which before induction are coiled up inside the sperm. The filament bundle exists in three stable states in the sperm. One of the states can be isolated in pure form. It is composed of only three proteins whose molecular weights (mol wt) are 43,000, 55,000, and 95,000. The 43,000 mol wt protein is actin, based on its molecular weight, net charge, morphology, G-F transformation, and heavy meromyosin (HMM) binding. The 55,000 mol wt protein is in equimolar ratio to actin and is not tubulin, binds tenaciously to actin, and inhibits HMM binding. Evidence is presented that both the 55,000 mol wt protein and the 95,000 mol wt protein (possibly alpha-actinin) are also present in Limulus muscle. Presumably these proteins function in the sperm in holding the actin filaments together. Before the acrosomal reaction, the actin filaments are twisted over one another in a supercoil; when the reaction is completed, the filaments lie parallel to each other and form an actin paracrystal. This change in their packing appears to give rise to the motion of the acrosomal process and is under the control of the 55,000 mol wt protein and the 95,000 mol wt protein.

In Vivo Control of CpG and Non-CpG DNA Methylation by DNA Methyltransferases

PubMed Central

Arand, Julia; Spieler, David; Karius, Tommy; Branco, Miguel R.; Meilinger, Daniela; Meissner, Alexander; Jenuwein, Thomas; Xu, Guoliang; Leonhardt, Heinrich; Wolf, Verena; Walter, Jörn

2012-01-01

The enzymatic control of the setting and maintenance of symmetric and non-symmetric DNA methylation patterns in a particular genome context is not well understood. Here, we describe a comprehensive analysis of DNA methylation patterns generated by high resolution sequencing of hairpin-bisulfite amplicons of selected single copy genes and repetitive elements (LINE1, B1, IAP-LTR-retrotransposons, and major satellites). The analysis unambiguously identifies a substantial amount of regional incomplete methylation maintenance, i.e. hemimethylated CpG positions, with variant degrees among cell types. Moreover, non-CpG cytosine methylation is confined to ESCs and exclusively catalysed by Dnmt3a and Dnmt3b. This sequence position–, cell type–, and region-dependent non-CpG methylation is strongly linked to neighboring CpG methylation and requires the presence of Dnmt3L. The generation of a comprehensive data set of 146,000 CpG dyads was used to apply and develop parameter estimated hidden Markov models (HMM) to calculate the relative contribution of DNA methyltransferases (Dnmts) for de novo and maintenance DNA methylation. The comparative modelling included wild-type ESCs and mutant ESCs deficient for Dnmt1, Dnmt3a, Dnmt3b, or Dnmt3a/3b, respectively. The HMM analysis identifies a considerable de novo methylation activity for Dnmt1 at certain repetitive elements and single copy sequences. Dnmt3a and Dnmt3b contribute de novo function. However, both enzymes are also essential to maintain symmetrical CpG methylation at distinct repetitive and single copy sequences in ESCs. PMID:22761581

Temporal stability of visual search-driven biometrics

NASA Astrophysics Data System (ADS)

Yoon, Hong-Jun; Carmichael, Tandy R.; Tourassi, Georgia

2015-03-01

Previously, we have shown the potential of using an individual's visual search pattern as a possible biometric. That study focused on viewing images displaying dot-patterns with different spatial relationships to determine which pattern can be more effective in establishing the identity of an individual. In this follow-up study we investigated the temporal stability of this biometric. We performed an experiment with 16 individuals asked to search for a predetermined feature of a random-dot pattern as we tracked their eye movements. Each participant completed four testing sessions consisting of two dot patterns repeated twice. One dot pattern displayed concentric circles shifted to the left or right side of the screen overlaid with visual noise, and participants were asked which side the circles were centered on. The second dot-pattern displayed a number of circles (between 0 and 4) scattered on the screen overlaid with visual noise, and participants were asked how many circles they could identify. Each session contained 5 untracked tutorial questions and 50 tracked test questions (200 total tracked questions per participant). To create each participant's "fingerprint", we constructed a Hidden Markov Model (HMM) from the gaze data representing the underlying visual search and cognitive process. The accuracy of the derived HMM models was evaluated using cross-validation for various time-dependent train-test conditions. Subject identification accuracy ranged from 17.6% to 41.8% for all conditions, which is significantly higher than random guessing (1/16 = 6.25%). The results suggest that visual search pattern is a promising, temporally stable personalized fingerprint of perceptual organization.

Classification of Anticipatory Signals for Grasp and Release from Surface Electromyography.

PubMed

Siu, Ho Chit; Shah, Julie A; Stirling, Leia A

2016-10-25

Surface electromyography (sEMG) is a technique for recording natural muscle activation signals, which can serve as control inputs for exoskeletons and prosthetic devices. Previous experiments have incorporated these signals using both classical and pattern-recognition control methods in order to actuate such devices. We used the results of an experiment incorporating grasp and release actions with object contact to develop an intent-recognition system based on Gaussian mixture models (GMM) and continuous-emission hidden Markov models (HMM) of sEMG data. We tested this system with data collected from 16 individuals using a forearm band with distributed sEMG sensors. The data contain trials with shifted band alignments to assess robustness to sensor placement. This study evaluated and found that pattern-recognition-based methods could classify transient anticipatory sEMG signals in the presence of shifted sensor placement and object contact. With the best-performing classifier, the effect of label lengths in the training data was also examined. A mean classification accuracy of 75.96% was achieved through a unigram HMM method with five mixture components. Classification accuracy on different sub-movements was found to be limited by the length of the shortest sub-movement, which means that shorter sub-movements within dynamic sequences require larger training sets to be classified correctly. This classification of user intent is a potential control mechanism for a dynamic grasping task involving user contact with external objects and noise. Further work is required to test its performance as part of an exoskeleton controller, which involves contact with actuated external surfaces.

Genotype calling from next-generation sequencing data using haplotype information of reads

PubMed Central

Zhi, Degui; Wu, Jihua; Liu, Nianjun; Zhang, Kui

2012-01-01

Motivation: Low coverage sequencing provides an economic strategy for whole genome sequencing. When sequencing a set of individuals, genotype calling can be challenging due to low sequencing coverage. Linkage disequilibrium (LD) based refinement of genotyping calling is essential to improve the accuracy. Current LD-based methods use read counts or genotype likelihoods at individual potential polymorphic sites (PPSs). Reads that span multiple PPSs (jumping reads) can provide additional haplotype information overlooked by current methods. Results: In this article, we introduce a new Hidden Markov Model (HMM)-based method that can take into account jumping reads information across adjacent PPSs and implement it in the HapSeq program. Our method extends the HMM in Thunder and explicitly models jumping reads information as emission probabilities conditional on the states of adjacent PPSs. Our simulation results show that, compared to Thunder, HapSeq reduces the genotyping error rate by 30%, from 0.86% to 0.60%. The results from the 1000 Genomes Project show that HapSeq reduces the genotyping error rate by 12 and 9%, from 2.24% and 2.76% to 1.97% and 2.50% for individuals with European and African ancestry, respectively. We expect our program can improve genotyping qualities of the large number of ongoing and planned whole genome sequencing projects. Contact: dzhi@ms.soph.uab.edu; kzhang@ms.soph.uab.edu Availability: The software package HapSeq and its manual can be found and downloaded at www.ssg.uab.edu/hapseq/. Supplementary information: Supplementary data are available at Bioinformatics online. PMID:22285565

Classification of Anticipatory Signals for Grasp and Release from Surface Electromyography

PubMed Central

Siu, Ho Chit; Shah, Julie A.; Stirling, Leia A.

2016-01-01

Surface electromyography (sEMG) is a technique for recording natural muscle activation signals, which can serve as control inputs for exoskeletons and prosthetic devices. Previous experiments have incorporated these signals using both classical and pattern-recognition control methods in order to actuate such devices. We used the results of an experiment incorporating grasp and release actions with object contact to develop an intent-recognition system based on Gaussian mixture models (GMM) and continuous-emission hidden Markov models (HMM) of sEMG data. We tested this system with data collected from 16 individuals using a forearm band with distributed sEMG sensors. The data contain trials with shifted band alignments to assess robustness to sensor placement. This study evaluated and found that pattern-recognition-based methods could classify transient anticipatory sEMG signals in the presence of shifted sensor placement and object contact. With the best-performing classifier, the effect of label lengths in the training data was also examined. A mean classification accuracy of 75.96% was achieved through a unigram HMM method with five mixture components. Classification accuracy on different sub-movements was found to be limited by the length of the shortest sub-movement, which means that shorter sub-movements within dynamic sequences require larger training sets to be classified correctly. This classification of user intent is a potential control mechanism for a dynamic grasping task involving user contact with external objects and noise. Further work is required to test its performance as part of an exoskeleton controller, which involves contact with actuated external surfaces. PMID:27792155

Protein classification based on text document classification techniques.

PubMed

Cheng, Betty Yee Man; Carbonell, Jaime G; Klein-Seetharaman, Judith

2005-03-01

The need for accurate, automated protein classification methods continues to increase as advances in biotechnology uncover new proteins. G-protein coupled receptors (GPCRs) are a particularly difficult superfamily of proteins to classify due to extreme diversity among its members. Previous comparisons of BLAST, k-nearest neighbor (k-NN), hidden markov model (HMM) and support vector machine (SVM) using alignment-based features have suggested that classifiers at the complexity of SVM are needed to attain high accuracy. Here, analogous to document classification, we applied Decision Tree and Naive Bayes classifiers with chi-square feature selection on counts of n-grams (i.e. short peptide sequences of length n) to this classification task. Using the GPCR dataset and evaluation protocol from the previous study, the Naive Bayes classifier attained an accuracy of 93.0 and 92.4% in level I and level II subfamily classification respectively, while SVM has a reported accuracy of 88.4 and 86.3%. This is a 39.7 and 44.5% reduction in residual error for level I and level II subfamily classification, respectively. The Decision Tree, while inferior to SVM, outperforms HMM in both level I and level II subfamily classification. For those GPCR families whose profiles are stored in the Protein FAMilies database of alignments and HMMs (PFAM), our method performs comparably to a search against those profiles. Finally, our method can be generalized to other protein families by applying it to the superfamily of nuclear receptors with 94.5, 97.8 and 93.6% accuracy in family, level I and level II subfamily classification respectively. Copyright 2005 Wiley-Liss, Inc.

Modeling carbachol-induced hippocampal network synchronization using hidden Markov models

NASA Astrophysics Data System (ADS)

Dragomir, Andrei; Akay, Yasemin M.; Akay, Metin

2010-10-01

In this work we studied the neural state transitions undergone by the hippocampal neural network using a hidden Markov model (HMM) framework. We first employed a measure based on the Lempel-Ziv (LZ) estimator to characterize the changes in the hippocampal oscillation patterns in terms of their complexity. These oscillations correspond to different modes of hippocampal network synchronization induced by the cholinergic agonist carbachol in the CA1 region of mice hippocampus. HMMs are then used to model the dynamics of the LZ-derived complexity signals as first-order Markov chains. Consequently, the signals corresponding to our oscillation recordings can be segmented into a sequence of statistically discriminated hidden states. The segmentation is used for detecting transitions in neural synchronization modes in data recorded from wild-type and triple transgenic mice models (3xTG) of Alzheimer's disease (AD). Our data suggest that transition from low-frequency (delta range) continuous oscillation mode into high-frequency (theta range) oscillation, exhibiting repeated burst-type patterns, occurs always through a mode resembling a mixture of the two patterns, continuous with burst. The relatively random patterns of oscillation during this mode may reflect the fact that the neuronal network undergoes re-organization. Further insight into the time durations of these modes (retrieved via the HMM segmentation of the LZ-derived signals) reveals that the mixed mode lasts significantly longer (p < 10-4) in 3xTG AD mice. These findings, coupled with the documented cholinergic neurotransmission deficits in the 3xTG mice model, may be highly relevant for the case of AD.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yoon, Hong-Jun; Carmichael, Tandy; Tourassi, Georgia

Previously, we have shown the potential of using an individual s visual search pattern as a possible biometric. That study focused on viewing images displaying dot-patterns with different spatial relationships to determine which pattern can be more effective in establishing the identity of an individual. In this follow-up study we investigated the temporal stability of this biometric. We performed an experiment with 16 individuals asked to search for a predetermined feature of a random-dot pattern as we tracked their eye movements. Each participant completed four testing sessions consisting of two dot patterns repeated twice. One dot pattern displayed concentric circlesmore » shifted to the left or right side of the screen overlaid with visual noise, and participants were asked which side the circles were centered on. The second dot-pattern displayed a number of circles (between 0 and 4) scattered on the screen overlaid with visual noise, and participants were asked how many circles they could identify. Each session contained 5 untracked tutorial questions and 50 tracked test questions (200 total tracked questions per participant). To create each participant s "fingerprint", we constructed a Hidden Markov Model (HMM) from the gaze data representing the underlying visual search and cognitive process. The accuracy of the derived HMM models was evaluated using cross-validation for various time-dependent train-test conditions. Subject identification accuracy ranged from 17.6% to 41.8% for all conditions, which is significantly higher than random guessing (1/16 = 6.25%). The results suggest that visual search pattern is a promising, fairly stable personalized fingerprint of perceptual organization.« less

Neural Networks for Signal Processing 5. Proceedings of the 1995 IEEE Workshop (5th) Held in Cambridge, MA on 31 Aug-2 Sep 95.

DTIC Science & Technology

1995-01-01

expensive) option is to track the mean and variance of each input feature instead of the min and max. Then a sigmoid is the natural choice for a mapping...Scaling Down: Applying Large Vocabulary Hybrid HMM-MLP Methods to Telephone Recognition of Digits and Natural Numbers 223 Kristine Ma, Nelson Morgan...1 if Yt > 1 Yt + I if Yt < 0 where ct is uncorrelated Gaussian noise with a variance of o-2 = 0.01. Figure 2 (left) shows the time series. Figure 2

National Dam Safety Program. Martindale Dam (NDI Number PA-00444, PennDER Number 11-17), Ohio River Basin, Trout Run, Cambria County, Pennsylvania. Phase I Inspection Report.

DTIC Science & Technology

1980-08-01

0025 UNCLASSIFIED NL m -hmmII hhh~ENDhE~E EEEEL~ ___ OHIO RIVER BASIN TROUT RUN, CAMBRIA COUNTY PENNSYLVANIA NOI No. PA 00444 ~LEVEL tPennDER No. 11-17...COUNTY, COMMONWEALTH OF PENNSYLVANIA NDI No. PA 00444 PennDER No. 11-17 --PHASE--I -INSPECT-I ON--REPRT m - i-’ JNATIONAL.DAM. AFETY PROGRAM I,.ti/t UK...Construction History - The dam was designed by Andrew B. Crichton , Civil and Mining Engineer, Johnstown, Pennsylvania. The dam was constructed in 1909 and 1910

Heuristic algorithm for optical character recognition of Arabic script

NASA Astrophysics Data System (ADS)

Yarman-Vural, Fatos T.; Atici, A.

1996-02-01

In this paper, a heuristic method is developed for segmentation, feature extraction and recognition of the Arabic script. The study is part of a large project for the transcription of the documents in Ottoman Archives. A geometrical and topological feature analysis method is developed for segmentation and feature extraction stages. Chain code transformation is applied to main strokes of the characters which are then classified by the hidden Markov model (HMM) in the recognition stage. Experimental results indicate that the performance of the proposed method is impressive, provided that the thinning process does not yield spurious branches.

Automatic identification of individual killer whales.

PubMed

Brown, Judith C; Smaragdis, Paris; Nousek-McGregor, Anna

2010-09-01

Following the successful use of HMM and GMM models for classification of a set of 75 calls of northern resident killer whales into call types [Brown, J. C., and Smaragdis, P., J. Acoust. Soc. Am. 125, 221-224 (2009)], the use of these same methods has been explored for the identification of vocalizations from the same call type N2 of four individual killer whales. With an average of 20 vocalizations from each of the individuals the pairwise comparisons have an extremely high success rate of 80 to 100% and the identifications within the entire group yield around 78%.

Domain fusion analysis by applying relational algebra to protein sequence and domain databases

PubMed Central

Truong, Kevin; Ikura, Mitsuhiko

2003-01-01

Background Domain fusion analysis is a useful method to predict functionally linked proteins that may be involved in direct protein-protein interactions or in the same metabolic or signaling pathway. As separate domain databases like BLOCKS, PROSITE, Pfam, SMART, PRINTS-S, ProDom, TIGRFAMs, and amalgamated domain databases like InterPro continue to grow in size and quality, a computational method to perform domain fusion analysis that leverages on these efforts will become increasingly powerful. Results This paper proposes a computational method employing relational algebra to find domain fusions in protein sequence databases. The feasibility of this method was illustrated on the SWISS-PROT+TrEMBL sequence database using domain predictions from the Pfam HMM (hidden Markov model) database. We identified 235 and 189 putative functionally linked protein partners in H. sapiens and S. cerevisiae, respectively. From scientific literature, we were able to confirm many of these functional linkages, while the remainder offer testable experimental hypothesis. Results can be viewed at . Conclusion As the analysis can be computed quickly on any relational database that supports standard SQL (structured query language), it can be dynamically updated along with the sequence and domain databases, thereby improving the quality of predictions over time. PMID:12734020

Clustering Multivariate Time Series Using Hidden Markov Models

PubMed Central

Ghassempour, Shima; Girosi, Federico; Maeder, Anthony

2014-01-01

In this paper we describe an algorithm for clustering multivariate time series with variables taking both categorical and continuous values. Time series of this type are frequent in health care, where they represent the health trajectories of individuals. The problem is challenging because categorical variables make it difficult to define a meaningful distance between trajectories. We propose an approach based on Hidden Markov Models (HMMs), where we first map each trajectory into an HMM, then define a suitable distance between HMMs and finally proceed to cluster the HMMs with a method based on a distance matrix. We test our approach on a simulated, but realistic, data set of 1,255 trajectories of individuals of age 45 and over, on a synthetic validation set with known clustering structure, and on a smaller set of 268 trajectories extracted from the longitudinal Health and Retirement Survey. The proposed method can be implemented quite simply using standard packages in R and Matlab and may be a good candidate for solving the difficult problem of clustering multivariate time series with categorical variables using tools that do not require advanced statistic knowledge, and therefore are accessible to a wide range of researchers. PMID:24662996

Continuous Human Action Recognition Using Depth-MHI-HOG and a Spotter Model

PubMed Central

Eum, Hyukmin; Yoon, Changyong; Lee, Heejin; Park, Mignon

2015-01-01

In this paper, we propose a new method for spotting and recognizing continuous human actions using a vision sensor. The method is comprised of depth-MHI-HOG (DMH), action modeling, action spotting, and recognition. First, to effectively separate the foreground from background, we propose a method called DMH. It includes a standard structure for segmenting images and extracting features by using depth information, MHI, and HOG. Second, action modeling is performed to model various actions using extracted features. The modeling of actions is performed by creating sequences of actions through k-means clustering; these sequences constitute HMM input. Third, a method of action spotting is proposed to filter meaningless actions from continuous actions and to identify precise start and end points of actions. By employing the spotter model, the proposed method improves action recognition performance. Finally, the proposed method recognizes actions based on start and end points. We evaluate recognition performance by employing the proposed method to obtain and compare probabilities by applying input sequences in action models and the spotter model. Through various experiments, we demonstrate that the proposed method is efficient for recognizing continuous human actions in real environments. PMID:25742172

BAGEL4: a user-friendly web server to thoroughly mine RiPPs and bacteriocins.

PubMed

van Heel, Auke J; de Jong, Anne; Song, Chunxu; Viel, Jakob H; Kok, Jan; Kuipers, Oscar P

2018-05-21

Interest in secondary metabolites such as RiPPs (ribosomally synthesized and posttranslationally modified peptides) is increasing worldwide. To facilitate the research in this field we have updated our mining web server. BAGEL4 is faster than its predecessor and is now fully independent from ORF-calling. Gene clusters of interest are discovered using the core-peptide database and/or through HMM motifs that are present in associated context genes. The databases used for mining have been updated and extended with literature references and links to UniProt and NCBI. Additionally, we have included automated promoter and terminator prediction and the option to upload RNA expression data, which can be displayed along with the identified clusters. Further improvements include the annotation of the context genes, which is now based on a fast blast against the prokaryote part of the UniRef90 database, and the improved web-BLAST feature that dynamically loads structural data such as internal cross-linking from UniProt. Overall BAGEL4 provides the user with more information through a user-friendly web-interface which simplifies data evaluation. BAGEL4 is freely accessible at http://bagel4.molgenrug.nl.

Automated recognition of bird song elements from continuous recordings using dynamic time warping and hidden Markov models: a comparative study.

PubMed

Kogan, J A; Margoliash, D

1998-04-01

The performance of two techniques is compared for automated recognition of bird song units from continuous recordings. The advantages and limitations of dynamic time warping (DTW) and hidden Markov models (HMMs) are evaluated on a large database of male songs of zebra finches (Taeniopygia guttata) and indigo buntings (Passerina cyanea), which have different types of vocalizations and have been recorded under different laboratory conditions. Depending on the quality of recordings and complexity of song, the DTW-based technique gives excellent to satisfactory performance. Under challenging conditions such as noisy recordings or presence of confusing short-duration calls, good performance of the DTW-based technique requires careful selection of templates that may demand expert knowledge. Because HMMs are trained, equivalent or even better performance of HMMs can be achieved based only on segmentation and labeling of constituent vocalizations, albeit with many more training examples than DTW templates. One weakness in HMM performance is the misclassification of short-duration vocalizations or song units with more variable structure (e.g., some calls, and syllables of plastic songs). To address these and other limitations, new approaches for analyzing bird vocalizations are discussed.

Kinetic Characterization of Nonmuscle Myosin IIB at the Single Molecule Level*

PubMed Central

Nagy, Attila; Takagi, Yasuharu; Billington, Neil; Sun, Sara A.; Hong, Davin K. T.; Homsher, Earl; Wang, Aibing; Sellers, James R.

2013-01-01

Nonmuscle myosin IIB (NMIIB) is a cytoplasmic myosin, which plays an important role in cell motility by maintaining cortical tension. It forms bipolar thick filaments with ∼14 myosin molecule dimers on each side of the bare zone. Our previous studies showed that the NMIIB is a moderately high duty ratio (∼20–25%) motor. The ADP release step (∼0.35 s−1) of NMIIB is only ∼3 times faster than the rate-limiting phosphate release (0.13 ± 0.01 s−1). The aim of this study was to relate the known in vitro kinetic parameters to the results of single molecule experiments and to compare the kinetic and mechanical properties of single- and double-headed myosin fragments and nonmuscle IIB thick filaments. Examination of the kinetics of NMIIB interaction with actin at the single molecule level was accomplished using total internal reflection fluorescence (TIRF) with fluorescence imaging with 1-nm accuracy (FIONA) and dual-beam optical trapping. At a physiological ATP concentration (1 mm), the rate of detachment of the single-headed and double-headed molecules was similar (∼0.4 s−1). Using optical tweezers we found that the power stroke sizes of single- and double-headed heavy meromyosin (HMM) were each ∼6 nm. No signs of processive stepping at the single molecule level were observed in the case of NMIIB-HMM in optical tweezers or TIRF/in vitro motility experiments. In contrast, robust motility of individual fluorescently labeled thick filaments of full-length NMIIB was observed on actin filaments. Our results are in good agreement with the previous steady-state and transient kinetic studies and show that the individual nonprocessive nonmuscle myosin IIB molecules form a highly processive unit when polymerized into filaments. PMID:23148220

HuMiChip: Development of a Functional Gene Array for the Study of Human Microbiomes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tu, Q.; Deng, Ye; Lin, Lu

Microbiomes play very important roles in terms of nutrition, health and disease by interacting with their hosts. Based on sequence data currently available in public domains, we have developed a functional gene array to monitor both organismal and functional gene profiles of normal microbiota in human and mouse hosts, and such an array is called human and mouse microbiota array, HMM-Chip. First, seed sequences were identified from KEGG databases, and used to construct a seed database (seedDB) containing 136 gene families in 19 metabolic pathways closely related to human and mouse microbiomes. Second, a mother database (motherDB) was constructed withmore » 81 genomes of bacterial strains with 54 from gut and 27 from oral environments, and 16 metagenomes, and used for selection of genes and probe design. Gene prediction was performed by Glimmer3 for bacterial genomes, and by the Metagene program for metagenomes. In total, 228,240 and 801,599 genes were identified for bacterial genomes and metagenomes, respectively. Then the motherDB was searched against the seedDB using the HMMer program, and gene sequences in the motherDB that were highly homologous with seed sequences in the seedDB were used for probe design by the CommOligo software. Different degrees of specific probes, including gene-specific, inclusive and exclusive group-specific probes were selected. All candidate probes were checked against the motherDB and NCBI databases for specificity. Finally, 7,763 probes covering 91.2percent (12,601 out of 13,814) HMMer confirmed sequences from 75 bacterial genomes and 16 metagenomes were selected. This developed HMM-Chip is able to detect the diversity and abundance of functional genes, the gene expression of microbial communities, and potentially, the interactions of microorganisms and their hosts.« less

Performance enhancement for audio-visual speaker identification using dynamic facial muscle model.

PubMed

Asadpour, Vahid; Towhidkhah, Farzad; Homayounpour, Mohammad Mehdi

2006-10-01

Science of human identification using physiological characteristics or biometry has been of great concern in security systems. However, robust multimodal identification systems based on audio-visual information has not been thoroughly investigated yet. Therefore, the aim of this work to propose a model-based feature extraction method which employs physiological characteristics of facial muscles producing lip movements. This approach adopts the intrinsic properties of muscles such as viscosity, elasticity, and mass which are extracted from the dynamic lip model. These parameters are exclusively dependent on the neuro-muscular properties of speaker; consequently, imitation of valid speakers could be reduced to a large extent. These parameters are applied to a hidden Markov model (HMM) audio-visual identification system. In this work, a combination of audio and video features has been employed by adopting a multistream pseudo-synchronized HMM training method. Noise robust audio features such as Mel-frequency cepstral coefficients (MFCC), spectral subtraction (SS), and relative spectra perceptual linear prediction (J-RASTA-PLP) have been used to evaluate the performance of the multimodal system once efficient audio feature extraction methods have been utilized. The superior performance of the proposed system is demonstrated on a large multispeaker database of continuously spoken digits, along with a sentence that is phonetically rich. To evaluate the robustness of algorithms, some experiments were performed on genetically identical twins. Furthermore, changes in speaker voice were simulated with drug inhalation tests. In 3 dB signal to noise ratio (SNR), the dynamic muscle model improved the identification rate of the audio-visual system from 91 to 98%. Results on identical twins revealed that there was an apparent improvement on the performance for the dynamic muscle model-based system, in which the identification rate of the audio-visual system was enhanced from 87 to 96%.

A male-specific QTL for social interaction behavior in mice mapped with automated pattern detection by a hidden Markov model incorporated into newly developed freeware.

PubMed

Arakawa, Toshiya; Tanave, Akira; Ikeuchi, Shiho; Takahashi, Aki; Kakihara, Satoshi; Kimura, Shingo; Sugimoto, Hiroki; Asada, Nobuhiko; Shiroishi, Toshihiko; Tomihara, Kazuya; Tsuchiya, Takashi; Koide, Tsuyoshi

2014-08-30

Owing to their complex nature, social interaction tests normally require the observation of video data by a human researcher, and thus are difficult to use in large-scale studies. We previously established a statistical method, a hidden Markov model (HMM), which enables the differentiation of two social states ("interaction" and "indifference"), and three social states ("sniffing", "following", and "indifference"), automatically in silico. Here, we developed freeware called DuoMouse for the rapid evaluation of social interaction behavior. This software incorporates five steps: (1) settings, (2) video recording, (3) tracking from the video data, (4) HMM analysis, and (5) visualization of the results. Using DuoMouse, we mapped a genetic locus related to social interaction. We previously reported that a consomic strain, B6-Chr6C(MSM), with its chromosome 6 substituted for one from MSM/Ms, showed more social interaction than C57BL/6 (B6). We made four subconsomic strains, C3, C5, C6, and C7, each of which has a shorter segment of chromosome 6 derived from B6-Chr6C, and conducted social interaction tests on these strains. DuoMouse indicated that C6, but not C3, C5, and C7, showed higher interaction, sniffing, and following than B6, specifically in males. The data obtained by human observation showed high concordance to those from DuoMouse. The results indicated that the MSM-derived chromosomal region present in C6-but not in C3, C5, and C7-associated with increased social behavior. This method to analyze social interaction will aid primary screening for difference in social behavior in mice. Copyright © 2014 Elsevier B.V. All rights reserved.

Speech Acquisition and Automatic Speech Recognition for Integrated Spacesuit Audio Systems

NASA Technical Reports Server (NTRS)

Huang, Yiteng; Chen, Jingdong; Chen, Shaoyan

2010-01-01

A voice-command human-machine interface system has been developed for spacesuit extravehicular activity (EVA) missions. A multichannel acoustic signal processing method has been created for distant speech acquisition in noisy and reverberant environments. This technology reduces noise by exploiting differences in the statistical nature of signal (i.e., speech) and noise that exists in the spatial and temporal domains. As a result, the automatic speech recognition (ASR) accuracy can be improved to the level at which crewmembers would find the speech interface useful. The developed speech human/machine interface will enable both crewmember usability and operational efficiency. It can enjoy a fast rate of data/text entry, small overall size, and can be lightweight. In addition, this design will free the hands and eyes of a suited crewmember. The system components and steps include beam forming/multi-channel noise reduction, single-channel noise reduction, speech feature extraction, feature transformation and normalization, feature compression, model adaption, ASR HMM (Hidden Markov Model) training, and ASR decoding. A state-of-the-art phoneme recognizer can obtain an accuracy rate of 65 percent when the training and testing data are free of noise. When it is used in spacesuits, the rate drops to about 33 percent. With the developed microphone array speech-processing technologies, the performance is improved and the phoneme recognition accuracy rate rises to 44 percent. The recognizer can be further improved by combining the microphone array and HMM model adaptation techniques and using speech samples collected from inside spacesuits. In addition, arithmetic complexity models for the major HMMbased ASR components were developed. They can help real-time ASR system designers select proper tasks when in the face of constraints in computational resources.

A novel HMM distributed classifier for the detection of gait phases by means of a wearable inertial sensor network.

PubMed

Taborri, Juri; Rossi, Stefano; Palermo, Eduardo; Patanè, Fabrizio; Cappa, Paolo

2014-09-02

In this work, we decided to apply a hierarchical weighted decision, proposed and used in other research fields, for the recognition of gait phases. The developed and validated novel distributed classifier is based on hierarchical weighted decision from outputs of scalar Hidden Markov Models (HMM) applied to angular velocities of foot, shank, and thigh. The angular velocities of ten healthy subjects were acquired via three uni-axial gyroscopes embedded in inertial measurement units (IMUs) during one walking task, repeated three times, on a treadmill. After validating the novel distributed classifier and scalar and vectorial classifiers-already proposed in the literature, with a cross-validation, classifiers were compared for sensitivity, specificity, and computational load for all combinations of the three targeted anatomical segments. Moreover, the performance of the novel distributed classifier in the estimation of gait variability in terms of mean time and coefficient of variation was evaluated. The highest values of specificity and sensitivity (>0.98) for the three classifiers examined here were obtained when the angular velocity of the foot was processed. Distributed and vectorial classifiers reached acceptable values (>0.95) when the angular velocity of shank and thigh were analyzed. Distributed and scalar classifiers showed values of computational load about 100 times lower than the one obtained with the vectorial classifier. In addition, distributed classifiers showed an excellent reliability for the evaluation of mean time and a good/excellent reliability for the coefficient of variation. In conclusion, due to the better performance and the small value of computational load, the here proposed novel distributed classifier can be implemented in the real-time application of gait phases recognition, such as to evaluate gait variability in patients or to control active orthoses for the recovery of mobility of lower limb joints.

Quantitative Image Feature Engine (QIFE): an Open-Source, Modular Engine for 3D Quantitative Feature Extraction from Volumetric Medical Images.

PubMed

Echegaray, Sebastian; Bakr, Shaimaa; Rubin, Daniel L; Napel, Sandy

2017-10-06

The aim of this study was to develop an open-source, modular, locally run or server-based system for 3D radiomics feature computation that can be used on any computer system and included in existing workflows for understanding associations and building predictive models between image features and clinical data, such as survival. The QIFE exploits various levels of parallelization for use on multiprocessor systems. It consists of a managing framework and four stages: input, pre-processing, feature computation, and output. Each stage contains one or more swappable components, allowing run-time customization. We benchmarked the engine using various levels of parallelization on a cohort of CT scans presenting 108 lung tumors. Two versions of the QIFE have been released: (1) the open-source MATLAB code posted to Github, (2) a compiled version loaded in a Docker container, posted to DockerHub, which can be easily deployed on any computer. The QIFE processed 108 objects (tumors) in 2:12 (h/mm) using 1 core, and 1:04 (h/mm) hours using four cores with object-level parallelization. We developed the Quantitative Image Feature Engine (QIFE), an open-source feature-extraction framework that focuses on modularity, standards, parallelism, provenance, and integration. Researchers can easily integrate it with their existing segmentation and imaging workflows by creating input and output components that implement their existing interfaces. Computational efficiency can be improved by parallelizing execution at the cost of memory usage. Different parallelization levels provide different trade-offs, and the optimal setting will depend on the size and composition of the dataset to be processed.

Closed-loop control of a fragile network: application to seizure-like dynamics of an epilepsy model

PubMed Central

Ehrens, Daniel; Sritharan, Duluxan; Sarma, Sridevi V.

2015-01-01

It has recently been proposed that the epileptic cortex is fragile in the sense that seizures manifest through small perturbations in the synaptic connections that render the entire cortical network unstable. Closed-loop therapy could therefore entail detecting when the network goes unstable, and then stimulating with an exogenous current to stabilize the network. In this study, a non-linear stochastic model of a neuronal network was used to simulate both seizure and non-seizure activity. In particular, synaptic weights between neurons were chosen such that the network's fixed point is stable during non-seizure periods, and a subset of these connections (the most fragile) were perturbed to make the same fixed point unstable to model seizure events; and, the model randomly transitions between these two modes. The goal of this study was to measure spike train observations from this epileptic network and then apply a feedback controller that (i) detects when the network goes unstable, and then (ii) applies a state-feedback gain control input to the network to stabilize it. The stability detector is based on a 2-state (stable, unstable) hidden Markov model (HMM) of the network, and detects the transition from the stable mode to the unstable mode from using the firing rate of the most fragile node in the network (which is the output of the HMM). When the unstable mode is detected, a state-feedback gain is applied to generate a control input to the fragile node bringing the network back to the stable mode. Finally, when the network is detected as stable again, the feedback control input is switched off. High performance was achieved for the stability detector, and feedback control suppressed seizures within 2 s after onset. PMID:25784851

Dynamics of Weeds in the Soil Seed Bank: A Hidden Markov Model to Estimate Life History Traits from Standing Plant Time Series.

PubMed

Borgy, Benjamin; Reboud, Xavier; Peyrard, Nathalie; Sabbadin, Régis; Gaba, Sabrina

2015-01-01

Predicting the population dynamics of annual plants is a challenge due to their hidden seed banks in the field. However, such predictions are highly valuable for determining management strategies, specifically in agricultural landscapes. In agroecosystems, most weed seeds survive during unfavourable seasons and persist for several years in the seed bank. This causes difficulties in making accurate predictions of weed population dynamics and life history traits (LHT). Consequently, it is very difficult to identify management strategies that limit both weed populations and species diversity. In this article, we present a method of assessing weed population dynamics from both standing plant time series data and an unknown seed bank. We use a Hidden Markov Model (HMM) to obtain estimates of over 3,080 botanical records for three major LHT: seed survival in the soil, plant establishment (including post-emergence mortality), and seed production of 18 common weed species. Maximum likelihood and Bayesian approaches were complementarily used to estimate LHT values. The results showed that the LHT provided by the HMM enabled fairly accurate estimates of weed populations in different crops. There was a positive correlation between estimated germination rates and an index of the specialisation to the crop type (IndVal). The relationships between estimated LHTs and that between the estimated LHTs and the ecological characteristics of weeds provided insights into weed strategies. For example, a common strategy to cope with agricultural practices in several weeds was to produce less seeds and increase germination rates. This knowledge, especially of LHT for each type of crop, should provide valuable information for developing sustainable weed management strategies.

MacSyFinder: A Program to Mine Genomes for Molecular Systems with an Application to CRISPR-Cas Systems

PubMed Central

Abby, Sophie S.; Néron, Bertrand; Ménager, Hervé; Touchon, Marie; Rocha, Eduardo P. C.

2014-01-01

Motivation Biologists often wish to use their knowledge on a few experimental models of a given molecular system to identify homologs in genomic data. We developed a generic tool for this purpose. Results Macromolecular System Finder (MacSyFinder) provides a flexible framework to model the properties of molecular systems (cellular machinery or pathway) including their components, evolutionary associations with other systems and genetic architecture. Modelled features also include functional analogs, and the multiple uses of a same component by different systems. Models are used to search for molecular systems in complete genomes or in unstructured data like metagenomes. The components of the systems are searched by sequence similarity using Hidden Markov model (HMM) protein profiles. The assignment of hits to a given system is decided based on compliance with the content and organization of the system model. A graphical interface, MacSyView, facilitates the analysis of the results by showing overviews of component content and genomic context. To exemplify the use of MacSyFinder we built models to detect and class CRISPR-Cas systems following a previously established classification. We show that MacSyFinder allows to easily define an accurate “Cas-finder” using publicly available protein profiles. Availability and Implementation MacSyFinder is a standalone application implemented in Python. It requires Python 2.7, Hmmer and makeblastdb (version 2.2.28 or higher). It is freely available with its source code under a GPLv3 license at https://github.com/gem-pasteur/macsyfinder. It is compatible with all platforms supporting Python and Hmmer/makeblastdb. The “Cas-finder” (models and HMM profiles) is distributed as a compressed tarball archive as Supporting Information. PMID:25330359

Dynamics of Weeds in the Soil Seed Bank: A Hidden Markov Model to Estimate Life History Traits from Standing Plant Time Series

PubMed Central

Borgy, Benjamin; Reboud, Xavier; Peyrard, Nathalie; Sabbadin, Régis; Gaba, Sabrina

2015-01-01

Predicting the population dynamics of annual plants is a challenge due to their hidden seed banks in the field. However, such predictions are highly valuable for determining management strategies, specifically in agricultural landscapes. In agroecosystems, most weed seeds survive during unfavourable seasons and persist for several years in the seed bank. This causes difficulties in making accurate predictions of weed population dynamics and life history traits (LHT). Consequently, it is very difficult to identify management strategies that limit both weed populations and species diversity. In this article, we present a method of assessing weed population dynamics from both standing plant time series data and an unknown seed bank. We use a Hidden Markov Model (HMM) to obtain estimates of over 3,080 botanical records for three major LHT: seed survival in the soil, plant establishment (including post-emergence mortality), and seed production of 18 common weed species. Maximum likelihood and Bayesian approaches were complementarily used to estimate LHT values. The results showed that the LHT provided by the HMM enabled fairly accurate estimates of weed populations in different crops. There was a positive correlation between estimated germination rates and an index of the specialisation to the crop type (IndVal). The relationships between estimated LHTs and that between the estimated LHTs and the ecological characteristics of weeds provided insights into weed strategies. For example, a common strategy to cope with agricultural practices in several weeds was to produce less seeds and increase germination rates. This knowledge, especially of LHT for each type of crop, should provide valuable information for developing sustainable weed management strategies. PMID:26427023

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions.

PubMed

Götz, Markus; Wortmann, Philipp; Schmid, Sonja; Hugel, Thorsten

2018-01-30

Single-molecule Förster resonance energy transfer (smFRET) has become a widely used biophysical technique to study the dynamics of biomolecules. For many molecular machines in a cell proteins have to act together with interaction partners in a functional cycle to fulfill their task. The extension of two-color to multi-color smFRET makes it possible to simultaneously probe more than one interaction or conformational change. This not only adds a new dimension to smFRET experiments but it also offers the unique possibility to directly study the sequence of events and to detect correlated interactions when using an immobilized sample and a total internal reflection fluorescence microscope (TIRFM). Therefore, multi-color smFRET is a versatile tool for studying biomolecular complexes in a quantitative manner and in a previously unachievable detail. Here, we demonstrate how to overcome the special challenges of multi-color smFRET experiments on proteins. We present detailed protocols for obtaining the data and for extracting kinetic information. This includes trace selection criteria, state separation, and the recovery of state trajectories from the noisy data using a 3D ensemble Hidden Markov Model (HMM). Compared to other methods, the kinetic information is not recovered from dwell time histograms but directly from the HMM. The maximum likelihood framework allows us to critically evaluate the kinetic model and to provide meaningful uncertainties for the rates. By applying our method to the heat shock protein 90 (Hsp90), we are able to disentangle the nucleotide binding and the global conformational changes of the protein. This allows us to directly observe the cooperativity between the two nucleotide binding pockets of the Hsp90 dimer.

A novel Bayesian change-point algorithm for genome-wide analysis of diverse ChIPseq data types.

PubMed

Xing, Haipeng; Liao, Willey; Mo, Yifan; Zhang, Michael Q

2012-12-10

ChIPseq is a widely used technique for investigating protein-DNA interactions. Read density profiles are generated by using next-sequencing of protein-bound DNA and aligning the short reads to a reference genome. Enriched regions are revealed as peaks, which often differ dramatically in shape, depending on the target protein(1). For example, transcription factors often bind in a site- and sequence-specific manner and tend to produce punctate peaks, while histone modifications are more pervasive and are characterized by broad, diffuse islands of enrichment(2). Reliably identifying these regions was the focus of our work. Algorithms for analyzing ChIPseq data have employed various methodologies, from heuristics(3-5) to more rigorous statistical models, e.g. Hidden Markov Models (HMMs)(6-8). We sought a solution that minimized the necessity for difficult-to-define, ad hoc parameters that often compromise resolution and lessen the intuitive usability of the tool. With respect to HMM-based methods, we aimed to curtail parameter estimation procedures and simple, finite state classifications that are often utilized. Additionally, conventional ChIPseq data analysis involves categorization of the expected read density profiles as either punctate or diffuse followed by subsequent application of the appropriate tool. We further aimed to replace the need for these two distinct models with a single, more versatile model, which can capably address the entire spectrum of data types. To meet these objectives, we first constructed a statistical framework that naturally modeled ChIPseq data structures using a cutting edge advance in HMMs(9), which utilizes only explicit formulas-an innovation crucial to its performance advantages. More sophisticated then heuristic models, our HMM accommodates infinite hidden states through a Bayesian model. We applied it to identifying reasonable change points in read density, which further define segments of enrichment. Our analysis revealed how our Bayesian Change Point (BCP) algorithm had a reduced computational complexity-evidenced by an abridged run time and memory footprint. The BCP algorithm was successfully applied to both punctate peak and diffuse island identification with robust accuracy and limited user-defined parameters. This illustrated both its versatility and ease of use. Consequently, we believe it can be implemented readily across broad ranges of data types and end users in a manner that is easily compared and contrasted, making it a great tool for ChIPseq data analysis that can aid in collaboration and corroboration between research groups. Here, we demonstrate the application of BCP to existing transcription factor(10,11) and epigenetic data(12) to illustrate its usefulness.

LMethyR-SVM: Predict Human Enhancers Using Low Methylated Regions based on Weighted Support Vector Machines.

PubMed

Xu, Jingting; Hu, Hong; Dai, Yang

The identification of enhancers is a challenging task. Various types of epigenetic information including histone modification have been utilized in the construction of enhancer prediction models based on a diverse panel of machine learning schemes. However, DNA methylation profiles generated from the whole genome bisulfite sequencing (WGBS) have not been fully explored for their potential in enhancer prediction despite the fact that low methylated regions (LMRs) have been implied to be distal active regulatory regions. In this work, we propose a prediction framework, LMethyR-SVM, using LMRs identified from cell-type-specific WGBS DNA methylation profiles and a weighted support vector machine learning framework. In LMethyR-SVM, the set of cell-type-specific LMRs is further divided into three sets: reliable positive, like positive and likely negative, according to their resemblance to a small set of experimentally validated enhancers in the VISTA database based on an estimated non-parametric density distribution. Then, the prediction model is obtained by solving a weighted support vector machine. We demonstrate the performance of LMethyR-SVM by using the WGBS DNA methylation profiles derived from the human embryonic stem cell type (H1) and the fetal lung fibroblast cell type (IMR90). The predicted enhancers are highly conserved with a reasonable validation rate based on a set of commonly used positive markers including transcription factors, p300 binding and DNase-I hypersensitive sites. In addition, we show evidence that the large fraction of the LMethyR-SVM predicted enhancers are not predicted by ChromHMM in H1 cell type and they are more enriched for the FANTOM5 enhancers. Our work suggests that low methylated regions detected from the WGBS data are useful as complementary resources to histone modification marks in developing models for the prediction of cell-type-specific enhancers.

Web life: The Evil Mad Scientist Project

NASA Astrophysics Data System (ADS)

2009-04-01

What is it? Have you ever tried to electrocute a hot dog? Wondered how to make a robot out of a toothbrush, watch battery and phone-pager motor? Seen a cantaloupe melon and thought, "Hmm, I could make this look like the Death Star from the original Star Wars films"? If you have not, but you would like to - preferably as soon as you can find a pager motor - then this is the site for you. The Evil Mad Scientist Project (EMSP) blog is packed full of ideas for unusual, silly and frequently physics-related creations that bring science out of the laboratory and into kitchens, backyards and tool sheds.

Construction of language models for an handwritten mail reading system

NASA Astrophysics Data System (ADS)

Morillot, Olivier; Likforman-Sulem, Laurence; Grosicki, Emmanuèle

2012-01-01

This paper presents a system for the recognition of unconstrained handwritten mails. The main part of this system is an HMM recognizer which uses trigraphs to model contextual information. This recognition system does not require any segmentation into words or characters and directly works at line level. To take into account linguistic information and enhance performance, a language model is introduced. This language model is based on bigrams and built from training document transcriptions only. Different experiments with various vocabulary sizes and language models have been conducted. Word Error Rate and Perplexity values are compared to show the interest of specific language models, fit to handwritten mail recognition task.

Perturbation theory in the catalytic rate constant of the Henri-Michaelis-Menten enzymatic reaction.

PubMed

Bakalis, Evangelos; Kosmas, Marios; Papamichael, Emmanouel M

2012-11-01

The Henry-Michaelis-Menten (HMM) mechanism of enzymatic reaction is studied by means of perturbation theory in the reaction rate constant k (2) of product formation. We present analytical solutions that provide the concentrations of the enzyme (E), the substrate (S), as well as those of the enzyme-substrate complex (C), and the product (P) as functions of time. For k (2) small compared to k (-1), we properly describe the entire enzymatic activity from the beginning of the reaction up to longer times without imposing extra conditions on the initial concentrations E ( o ) and S ( o ), which can be comparable or much different.

Artificial Intelligence Software for Assessing Postural Stability

NASA Technical Reports Server (NTRS)

Lieberman, Erez; Forth, Katharine; Paloski, William

2013-01-01

A software package reads and analyzes pressure distributions from sensors mounted under a person's feet. Pressure data from sensors mounted in shoes, or in a platform, can be used to provide a description of postural stability (assessing competence to deficiency) and enables the determination of the person's present activity (running, walking, squatting, falling). This package has three parts: a preprocessing algorithm for reading input from pressure sensors; a Hidden Markov Model (HMM), which is used to determine the person's present activity and level of sensing-motor competence; and a suite of graphical algorithms, which allows visual representation of the person's activity and vestibular function over time.

Domain fusion analysis by applying relational algebra to protein sequence and domain databases.

PubMed

Truong, Kevin; Ikura, Mitsuhiko

2003-05-06

Domain fusion analysis is a useful method to predict functionally linked proteins that may be involved in direct protein-protein interactions or in the same metabolic or signaling pathway. As separate domain databases like BLOCKS, PROSITE, Pfam, SMART, PRINTS-S, ProDom, TIGRFAMs, and amalgamated domain databases like InterPro continue to grow in size and quality, a computational method to perform domain fusion analysis that leverages on these efforts will become increasingly powerful. This paper proposes a computational method employing relational algebra to find domain fusions in protein sequence databases. The feasibility of this method was illustrated on the SWISS-PROT+TrEMBL sequence database using domain predictions from the Pfam HMM (hidden Markov model) database. We identified 235 and 189 putative functionally linked protein partners in H. sapiens and S. cerevisiae, respectively. From scientific literature, we were able to confirm many of these functional linkages, while the remainder offer testable experimental hypothesis. Results can be viewed at http://calcium.uhnres.utoronto.ca/pi. As the analysis can be computed quickly on any relational database that supports standard SQL (structured query language), it can be dynamically updated along with the sequence and domain databases, thereby improving the quality of predictions over time.

The identification of complete domains within protein sequences using accurate E-values for semi-global alignment

PubMed Central

Kann, Maricel G.; Sheetlin, Sergey L.; Park, Yonil; Bryant, Stephen H.; Spouge, John L.

2007-01-01

The sequencing of complete genomes has created a pressing need for automated annotation of gene function. Because domains are the basic units of protein function and evolution, a gene can be annotated from a domain database by aligning domains to the corresponding protein sequence. Ideally, complete domains are aligned to protein subsequences, in a ‘semi-global alignment’. Local alignment, which aligns pieces of domains to subsequences, is common in high-throughput annotation applications, however. It is a mature technique, with the heuristics and accurate E-values required for screening large databases and evaluating the screening results. Hidden Markov models (HMMs) provide an alternative theoretical framework for semi-global alignment, but their use is limited because they lack heuristic acceleration and accurate E-values. Our new tool, GLOBAL, overcomes some limitations of previous semi-global HMMs: it has accurate E-values and the possibility of the heuristic acceleration required for high-throughput applications. Moreover, according to a standard of truth based on protein structure, two semi-global HMM alignment tools (GLOBAL and HMMer) had comparable performance in identifying complete domains, but distinctly outperformed two tools based on local alignment. When searching for complete protein domains, therefore, GLOBAL avoids disadvantages commonly associated with HMMs, yet maintains their superior retrieval performance. PMID:17596268

Tumor propagation model using generalized hidden Markov model

NASA Astrophysics Data System (ADS)

Park, Sun Young; Sargent, Dustin

2017-02-01

Tumor tracking and progression analysis using medical images is a crucial task for physicians to provide accurate and efficient treatment plans, and monitor treatment response. Tumor progression is tracked by manual measurement of tumor growth performed by radiologists. Several methods have been proposed to automate these measurements with segmentation, but many current algorithms are confounded by attached organs and vessels. To address this problem, we present a new generalized tumor propagation model considering time-series prior images and local anatomical features using a Hierarchical Hidden Markov model (HMM) for tumor tracking. First, we apply the multi-atlas segmentation technique to identify organs/sub-organs using pre-labeled atlases. Second, we apply a semi-automatic direct 3D segmentation method to label the initial boundary between the lesion and neighboring structures. Third, we detect vessels in the ROI surrounding the lesion. Finally, we apply the propagation model with the labeled organs and vessels to accurately segment and measure the target lesion. The algorithm has been designed in a general way to be applicable to various body parts and modalities. In this paper, we evaluate the proposed algorithm on lung and lung nodule segmentation and tracking. We report the algorithm's performance by comparing the longest diameter and nodule volumes using the FDA lung Phantom data and a clinical dataset.

Intelligent classifier for dynamic fault patterns based on hidden Markov model

NASA Astrophysics Data System (ADS)

Xu, Bo; Feng, Yuguang; Yu, Jinsong

2006-11-01

It's difficult to build precise mathematical models for complex engineering systems because of the complexity of the structure and dynamics characteristics. Intelligent fault diagnosis introduces artificial intelligence and works in a different way without building the analytical mathematical model of a diagnostic object, so it's a practical approach to solve diagnostic problems of complex systems. This paper presents an intelligent fault diagnosis method, an integrated fault-pattern classifier based on Hidden Markov Model (HMM). This classifier consists of dynamic time warping (DTW) algorithm, self-organizing feature mapping (SOFM) network and Hidden Markov Model. First, after dynamic observation vector in measuring space is processed by DTW, the error vector including the fault feature of being tested system is obtained. Then a SOFM network is used as a feature extractor and vector quantization processor. Finally, fault diagnosis is realized by fault patterns classifying with the Hidden Markov Model classifier. The importing of dynamic time warping solves the problem of feature extracting from dynamic process vectors of complex system such as aeroengine, and makes it come true to diagnose complex system by utilizing dynamic process information. Simulating experiments show that the diagnosis model is easy to extend, and the fault pattern classifier is efficient and is convenient to the detecting and diagnosing of new faults.

On-line Flagging of Anomalies and Adaptive Sequential Hypothesis Testing for Fine-feature Characterization of Geosynchronous Satellites

NASA Astrophysics Data System (ADS)

Chaudhary, A.; Payne, T.; Kinateder, K.; Dao, P.; Beecher, E.; Boone, D.; Elliott, B.

The objective of on-line flagging in this paper is to perform interactive assessment of geosynchronous satellites anomalies such as cross-tagging of a satellites in a cluster, solar panel offset change, etc. This assessment will utilize a Bayesian belief propagation procedure and will include automated update of baseline signature data for the satellite, while accounting for the seasonal changes. Its purpose is to enable an ongoing, automated assessment of satellite behavior through its life cycle using the photometry data collected during the synoptic search performed by a ground or space-based sensor as a part of its metrics mission. The change in the satellite features will be reported along with the probabilities of Type I and Type II errors. The objective of adaptive sequential hypothesis testing in this paper is to define future sensor tasking for the purpose of characterization of fine features of the satellite. The tasking will be designed in order to maximize new information with the least number of photometry data points to be collected during the synoptic search by a ground or space-based sensor. Its calculation is based on the utilization of information entropy techniques. The tasking is defined by considering a sequence of hypotheses in regard to the fine features of the satellite. The optimal observation conditions are then ordered in order to maximize new information about a chosen fine feature. The combined objective of on-line flagging and adaptive sequential hypothesis testing is to progressively discover new information about the features of a geosynchronous satellites by leveraging the regular but sparse cadence of data collection during the synoptic search performed by a ground or space-based sensor. Automated Algorithm to Detect Changes in Geostationary Satellite's Configuration and Cross-Tagging Phan Dao, Air Force Research Laboratory/RVB By characterizing geostationary satellites based on photometry and color photometry, analysts can evaluate satellite operational status and affirm its true identity. The process of ingesting photometry data and deriving satellite physical characteristics can be directed by analysts in a batch mode, meaning using a batch of recent data, or by automated algorithms in an on-line mode in which the assessment is updated with each new data point. Tools used for detecting change to satellite's status or identity, whether performed with a human in the loop or automated algorithms, are generally not built to detect with minimum latency and traceable confidence intervals. To alleviate those deficiencies, we investigate the use of Hidden Markov Models (HMM), in a Bayesian Network framework, to infer the hidden state (changed or unchanged) of a three-axis stabilized geostationary satellite using broadband and color photometry. Unlike frequentist statistics which exploit only the stationary statistics of the observables in the database, HMM also exploits the temporal pattern of the observables as well. The algorithm also operates in “learning” mode to gradually evolve the HMM and accommodate natural changes such as due to the seasonal dependence of GEO satellite's light curve. Our technique is designed to operate with missing color data. The version that ingests both panchromatic and color data can accommodate gaps in color photometry data. That attribute is important because while color indices, e.g. Johnson R and B, enhance the belief (probability) of a hidden state, in real world situations, flux data is collected sporadically in an untasked collect, and color data is limited and sometimes absent. Fluxes are measured with experimental error whose effect on the algorithm will be studied. Photometry data in the AFRL's Geo Color Photometry Catalog and Geo Observations with Latitudinal Diversity Simultaneously (GOLDS) data sets are used to simulate a wide variety of operational changes and identity cross tags. The algorithm is tested against simulated sequences of observed magnitudes, mimicking both the cadence of untasked SSN and other ground sensors, occasional operational changes and possible occurrence of cross tags of in-cluster satellites. We would like to show that the on-line algorithm can detect change; sometimes right after the first post-change data point is analyzed, for zero latency. We also want to show the unsupervised “learning” capability that allows the HMM to evolve with time without user's assistance. For example, the users are not required to “label” the true state of the data points.

Motor Task Variation Induces Structural Learning

PubMed Central

Braun, Daniel A.; Aertsen, Ad; Wolpert, Daniel M.; Mehring, Carsten

2009-01-01

Summary When we have learned a motor skill, such as cycling or ice-skating, we can rapidly generalize to novel tasks, such as motorcycling or rollerblading [1–8]. Such facilitation of learning could arise through two distinct mechanisms by which the motor system might adjust its control parameters. First, fast learning could simply be a consequence of the proximity of the original and final settings of the control parameters. Second, by structural learning [9–14], the motor system could constrain the parameter adjustments to conform to the control parameters' covariance structure. Thus, facilitation of learning would rely on the novel task parameters' lying on the structure of a lower-dimensional subspace that can be explored more efficiently. To test between these two hypotheses, we exposed subjects to randomly varying visuomotor tasks of fixed structure. Although such randomly varying tasks are thought to prevent learning, we show that when subsequently presented with novel tasks, subjects exhibit three key features of structural learning: facilitated learning of tasks with the same structure, strong reduction in interference normally observed when switching between tasks that require opposite control strategies, and preferential exploration along the learned structure. These results suggest that skill generalization relies on task variation and structural learning. PMID:19217296

Motor task variation induces structural learning.

PubMed

Braun, Daniel A; Aertsen, Ad; Wolpert, Daniel M; Mehring, Carsten

2009-02-24

When we have learned a motor skill, such as cycling or ice-skating, we can rapidly generalize to novel tasks, such as motorcycling or rollerblading [1-8]. Such facilitation of learning could arise through two distinct mechanisms by which the motor system might adjust its control parameters. First, fast learning could simply be a consequence of the proximity of the original and final settings of the control parameters. Second, by structural learning [9-14], the motor system could constrain the parameter adjustments to conform to the control parameters' covariance structure. Thus, facilitation of learning would rely on the novel task parameters' lying on the structure of a lower-dimensional subspace that can be explored more efficiently. To test between these two hypotheses, we exposed subjects to randomly varying visuomotor tasks of fixed structure. Although such randomly varying tasks are thought to prevent learning, we show that when subsequently presented with novel tasks, subjects exhibit three key features of structural learning: facilitated learning of tasks with the same structure, strong reduction in interference normally observed when switching between tasks that require opposite control strategies, and preferential exploration along the learned structure. These results suggest that skill generalization relies on task variation and structural learning.

SPLASH: structural pattern localization analysis by sequential histograms.

PubMed

Califano, A

2000-04-01

The discovery of sparse amino acid patterns that match repeatedly in a set of protein sequences is an important problem in computational biology. Statistically significant patterns, that is patterns that occur more frequently than expected, may identify regions that have been preserved by evolution and which may therefore play a key functional or structural role. Sparseness can be important because a handful of non-contiguous residues may play a key role, while others, in between, may be changed without significant loss of function or structure. Similar arguments may be applied to conserved DNA patterns. Available sparse pattern discovery algorithms are either inefficient or impose limitations on the type of patterns that can be discovered. This paper introduces a deterministic pattern discovery algorithm, called Splash, which can find sparse amino or nucleic acid patterns matching identically or similarly in a set of protein or DNA sequences. Sparse patterns of any length, up to the size of the input sequence, can be discovered without significant loss in performances. Splash is extremely efficient and embarrassingly parallel by nature. Large databases, such as a complete genome or the non-redundant SWISS-PROT database can be processed in a few hours on a typical workstation. Alternatively, a protein family or superfamily, with low overall homology, can be analyzed to discover common functional or structural signatures. Some examples of biologically interesting motifs discovered by Splash are reported for the histone I and for the G-Protein Coupled Receptor families. Due to its efficiency, Splash can be used to systematically and exhaustively identify conserved regions in protein family sets. These can then be used to build accurate and sensitive PSSM or HMM models for sequence analysis. Splash is available to non-commercial research centers upon request, conditional on the signing of a test field agreement. acal@us.ibm.com, Splash main page http://www.research.ibm.com/splash

Seasonal changes in antioxidative/oxidative profile of mining and non-mining populations of Syrian beancaper as determined by soil conditions.

PubMed

López-Orenes, Antonio; Bueso, María C; Conesa, Héctor M; Calderón, Antonio A; Ferrer, María A

2017-01-01

Soil pollution by heavy metals/metalloids (HMMs) is a problem worldwide. To prevent dispersion of contaminated particles by erosion, the maintenance of a vegetative cover is needed. Successful plant establishment in multi-polluted soils can be hampered not only by HMM toxicities, but also by soil nutrient deficiencies and the co-occurrence of abiotic stresses. Some plant species are able to thrive under these multi-stress scenarios often linked to marked fluctuations in environmental factors. This study aimed to investigate the metabolic adjustments involved in Zygophyllum fabago acclimative responses to conditions prevailing in HMM-enriched mine-tailings piles, during Mediterranean spring and summer. To this end, fully expanded leaves, and rhizosphere soil, of three contrasting mining and non-mining populations of Z. fabago grown spontaneously in south-eastern Spain were sampled in two consecutive years. Approximately 50 biochemical, physiological and edaphic parameters were examined, including leaf redox components, primary and secondary metabolites, endogenous levels of salicylic acid, and physicochemical properties of soil (fertility parameters and total concentration of HMMs). Multivariate data analysis showed a clear distinction in antioxidative/oxidative profiles between and within the populations studied. Levels of chlorophylls, proteins and proline characterized control plants whereas antioxidant capacity and C- and S-based antioxidant compounds were biomarkers of mining plants. Seasonal variations were characterized by higher levels of alkaloids and PAL and soluble peroxidase activities in summer, and by soluble sugars and hydroxycinnamic acids in spring irrespective of the population considered. Although the antioxidant systems are subjected to seasonal variations, the way and the intensity with which every population changes its antioxidative/oxidative profile seem to be determined by soil conditions. In short, Z. fabago displays a high physiological plasticity that allow it to successfully shift its metabolism to withstand the multiple stresses that plants must cope with in mine tailings piles under Mediterranean climatic conditions. Copyright © 2016 Elsevier B.V. All rights reserved.

Normalization of High Dimensional Genomics Data Where the Distribution of the Altered Variables Is Skewed

PubMed Central

Landfors, Mattias; Philip, Philge; Rydén, Patrik; Stenberg, Per

2011-01-01

Genome-wide analysis of gene expression or protein binding patterns using different array or sequencing based technologies is now routinely performed to compare different populations, such as treatment and reference groups. It is often necessary to normalize the data obtained to remove technical variation introduced in the course of conducting experimental work, but standard normalization techniques are not capable of eliminating technical bias in cases where the distribution of the truly altered variables is skewed, i.e. when a large fraction of the variables are either positively or negatively affected by the treatment. However, several experiments are likely to generate such skewed distributions, including ChIP-chip experiments for the study of chromatin, gene expression experiments for the study of apoptosis, and SNP-studies of copy number variation in normal and tumour tissues. A preliminary study using spike-in array data established that the capacity of an experiment to identify altered variables and generate unbiased estimates of the fold change decreases as the fraction of altered variables and the skewness increases. We propose the following work-flow for analyzing high-dimensional experiments with regions of altered variables: (1) Pre-process raw data using one of the standard normalization techniques. (2) Investigate if the distribution of the altered variables is skewed. (3) If the distribution is not believed to be skewed, no additional normalization is needed. Otherwise, re-normalize the data using a novel HMM-assisted normalization procedure. (4) Perform downstream analysis. Here, ChIP-chip data and simulated data were used to evaluate the performance of the work-flow. It was found that skewed distributions can be detected by using the novel DSE-test (Detection of Skewed Experiments). Furthermore, applying the HMM-assisted normalization to experiments where the distribution of the truly altered variables is skewed results in considerably higher sensitivity and lower bias than can be attained using standard and invariant normalization methods. PMID:22132175

Surface-Controlled Properties of Myosin Studied by Electric Field Modulation.

PubMed

van Zalinge, Harm; Ramsey, Laurence C; Aveyard, Jenny; Persson, Malin; Mansson, Alf; Nicolau, Dan V

2015-08-04

The efficiency of dynamic nanodevices using surface-immobilized protein molecular motors, which have been proposed for diagnostics, drug discovery, and biocomputation, critically depends on the ability to precisely control the motion of motor-propelled, individual cytoskeletal filaments transporting cargo to designated locations. The efficiency of these devices also critically depends on the proper function of the propelling motors, which is controlled by their interaction with the surfaces they are immobilized on. Here we use a microfluidic device to study how the motion of the motile elements, i.e., actin filaments propelled by heavy mero-myosin (HMM) motor fragments immobilized on various surfaces, is altered by the application of electrical loads generated by an external electric field with strengths ranging from 0 to 8 kVm(-1). Because the motility is intimately linked to the function of surface-immobilized motors, the study also showed how the adsorption properties of HMM on various surfaces, such as nitrocellulose (NC), trimethylclorosilane (TMCS), poly(methyl methacrylate) (PMMA), poly(tert-butyl methacrylate) (PtBMA), and poly(butyl methacrylate) (PBMA), can be characterized using an external field. It was found that at an electric field of 5 kVm(-1) the force exerted on the filaments is sufficient to overcome the frictionlike resistive force of the inactive motors. It was also found that the effect of assisting electric fields on the relative increase in the sliding velocity was markedly higher for the TMCS-derivatized surface than for all other polymer-based surfaces. An explanation of this behavior, based on the molecular rigidity of the TMCS-on-glass surfaces as opposed to the flexibility of the polymer-based ones, is considered. To this end, the proposed microfluidic device could be used to select appropriate surfaces for future lab-on-a-chip applications as illustrated here for the almost ideal TMCS surface. Furthermore, the proposed methodology can be used to gain fundamental insights into the functioning of protein molecular motors, such as the force exerted by the motors under different operational conditions.

Soft context clustering for F0 modeling in HMM-based speech synthesis

NASA Astrophysics Data System (ADS)

Khorram, Soheil; Sameti, Hossein; King, Simon

2015-12-01

This paper proposes the use of a new binary decision tree, which we call a soft decision tree, to improve generalization performance compared to the conventional `hard' decision tree method that is used to cluster context-dependent model parameters in statistical parametric speech synthesis. We apply the method to improve the modeling of fundamental frequency, which is an important factor in synthesizing natural-sounding high-quality speech. Conventionally, hard decision tree-clustered hidden Markov models (HMMs) are used, in which each model parameter is assigned to a single leaf node. However, this `divide-and-conquer' approach leads to data sparsity, with the consequence that it suffers from poor generalization, meaning that it is unable to accurately predict parameters for models of unseen contexts: the hard decision tree is a weak function approximator. To alleviate this, we propose the soft decision tree, which is a binary decision tree with soft decisions at the internal nodes. In this soft clustering method, internal nodes select both their children with certain membership degrees; therefore, each node can be viewed as a fuzzy set with a context-dependent membership function. The soft decision tree improves model generalization and provides a superior function approximator because it is able to assign each context to several overlapped leaves. In order to use such a soft decision tree to predict the parameters of the HMM output probability distribution, we derive the smoothest (maximum entropy) distribution which captures all partial first-order moments and a global second-order moment of the training samples. Employing such a soft decision tree architecture with maximum entropy distributions, a novel speech synthesis system is trained using maximum likelihood (ML) parameter re-estimation and synthesis is achieved via maximum output probability parameter generation. In addition, a soft decision tree construction algorithm optimizing a log-likelihood measure is developed. Both subjective and objective evaluations were conducted and indicate a considerable improvement over the conventional method.

Nuclear Thermal Propulsion Development Risks

NASA Technical Reports Server (NTRS)

Kim, Tony

2015-01-01

There are clear advantages of development of a Nuclear Thermal Propulsion (NTP) for a crewed mission to Mars. NTP for in-space propulsion enables more ambitious space missions by providing high thrust at high specific impulse ((is) approximately 900 sec) that is 2 times the best theoretical performance possible for chemical rockets. Missions can be optimized for maximum payload capability to take more payload with reduced total mass to orbit; saving cost on reduction of the number of launch vehicles needed. Or missions can be optimized to minimize trip time significantly to reduce the deep space radiation exposure to the crew. NTR propulsion technology is a game changer for space exploration to Mars and beyond. However, 'NUCLEAR' is a word that is feared and vilified by some groups and the hostility towards development of any nuclear systems can meet great opposition by the public as well as from national leaders and people in authority. The public often associates the 'nuclear' word with weapons of mass destruction. The development NTP is at risk due to unwarranted public fears and clear honest communication of nuclear safety will be critical to the success of the development of the NTP technology. Reducing cost to NTP development is critical to its acceptance and funding. In the past, highly inflated cost estimates of a full-scale development nuclear engine due to Category I nuclear security requirements and costly regulatory requirements have put the NTP technology as a low priority. Innovative approaches utilizing low enriched uranium (LEU). Even though NTP can be a small source of radiation to the crew, NTP can facilitate significant reduction of crew exposure to solar and cosmic radiation by reducing trip times by 3-4 months. Current Human Mars Mission (HMM) trajectories with conventional propulsion systems and fuel-efficient transfer orbits exceed astronaut radiation exposure limits. Utilizing extra propellant from one additional SLS launch and available energy in the NTP fuel, HMM radiation exposure can be reduced significantly.

A Novel HMM Distributed Classifier for the Detection of Gait Phases by Means of a Wearable Inertial Sensor Network

PubMed Central

Taborri, Juri; Rossi, Stefano; Palermo, Eduardo; Patanè, Fabrizio; Cappa, Paolo

2014-01-01

In this work, we decided to apply a hierarchical weighted decision, proposed and used in other research fields, for the recognition of gait phases. The developed and validated novel distributed classifier is based on hierarchical weighted decision from outputs of scalar Hidden Markov Models (HMM) applied to angular velocities of foot, shank, and thigh. The angular velocities of ten healthy subjects were acquired via three uni-axial gyroscopes embedded in inertial measurement units (IMUs) during one walking task, repeated three times, on a treadmill. After validating the novel distributed classifier and scalar and vectorial classifiers-already proposed in the literature, with a cross-validation, classifiers were compared for sensitivity, specificity, and computational load for all combinations of the three targeted anatomical segments. Moreover, the performance of the novel distributed classifier in the estimation of gait variability in terms of mean time and coefficient of variation was evaluated. The highest values of specificity and sensitivity (>0.98) for the three classifiers examined here were obtained when the angular velocity of the foot was processed. Distributed and vectorial classifiers reached acceptable values (>0.95) when the angular velocity of shank and thigh were analyzed. Distributed and scalar classifiers showed values of computational load about 100 times lower than the one obtained with the vectorial classifier. In addition, distributed classifiers showed an excellent reliability for the evaluation of mean time and a good/excellent reliability for the coefficient of variation. In conclusion, due to the better performance and the small value of computational load, the here proposed novel distributed classifier can be implemented in the real-time application of gait phases recognition, such as to evaluate gait variability in patients or to control active orthoses for the recovery of mobility of lower limb joints. PMID:25184488

Style Matching or Ability Building? An Empirical Study on FD Learners' Learning in Well-Structured and Ill-Structured Asynchronous Online Learning Environments

ERIC Educational Resources Information Center

Zheng, Robert Z.; Flygare, Jill A.; Dahl, Laura B.

2009-01-01

The present study investigated (1) the impact of cognitive styles on learner performance in well-structured and ill-structured learning, and (2) scaffolding as a cognitive tool to improve learners' cognitive abilities, especially field dependent (FD) learners' ability to thrive in an ill-structured learning environment. Two experiments were…

Grammatical pattern learning by human infants and cotton-top tamarin monkeys

PubMed Central

Saffran, Jenny; Hauser, Marc; Seibel, Rebecca; Kapfhamer, Joshua; Tsao, Fritz; Cushman, Fiery

2008-01-01

There is a surprising degree of overlapping structure evident across the languages of the world. One factor leading to cross-linguistic similarities may be constraints on human learning abilities. Linguistic structures that are easier for infants to learn should predominate in human languages. If correct, then (a) human infants should more readily acquire structures that are consistent with the form of natural language, whereas (b) non-human primates’ patterns of learning should be less tightly linked to the structure of human languages. Prior experiments have not directly compared laboratory-based learning of grammatical structures by human infants and non-human primates, especially under comparable testing conditions and with similar materials. Five experiments with 12-month-old human infants and adult cotton-top tamarin monkeys addressed these predictions, employing comparable methods (familiarization-discrimination) and materials. Infants rapidly acquired complex grammatical structures by using statistically predictive patterns, failing to learn structures that lacked such patterns. In contrast, the tamarins only exploited predictive patterns when learning relatively simple grammatical structures. Infant learning abilities may serve both to facilitate natural language acquisition and to impose constraints on the structure of human languages. PMID:18082676

Joint High Speed Sealift (JHSS) Baseline Shaft & Strut (BSS) Model 5653-3: Series 2, Propeller Disk LDV Wake Survey; and Series 3, Stock Propeller Powering and Stern Flap Evaluation Experiments

DTIC Science & Technology

2007-09-01

B27 B3b. Open water performance characteristics, stock propellers 5234 and 5235 ................... B28 B4. Principal dimensions of...n~nu~ .. *ASA B27 B3b. Open water performance characteristics, stock propellers 5234 and 5235 5234 5235 FAMED OPEN WATER COEFFICIENTS FOR PROPELLER...LA V 00 H MN 0𔃾 LAW M 00 MN4 0 ( LA M 4. ~ ~ ~ ~ r 4. LALA (0 m N 0 0 H H- H H H Hm H H vN( N( N((((m 4 HmmN N4 L 0 .( N ( ( HW H HLA mL (n(mNHO H

An Overview of Mars Vicinity Transportation Concepts for a Human Mars Mission

NASA Technical Reports Server (NTRS)

Dexter, Carol E.; Kos, Larry

1998-01-01

To send a piloted mission to Mars, transportation systems must be developed for the Earth to Orbit, trans Mars injection (TMI), capture into Mars orbit, Mars descent, surface stay, Mars ascent, trans Earth injection (TEI), and Earth return phases. This paper presents a brief overview of the transportation systems for the Human Mars Mission (HMM) only in the vicinity of Mars. This includes: capture into Mars orbit, Mars descent, surface stay, and Mars ascent. Development of feasible mission scenarios now is important for identification of critical technology areas that must be developed to support future human missions. Although there is no funded human Mars mission today, architecture studies are focusing on missions traveling to Mars between 2011 and the early 2020's.

Structure learning in action

PubMed Central

Braun, Daniel A.; Mehring, Carsten; Wolpert, Daniel M.

2010-01-01

‘Learning to learn’ phenomena have been widely investigated in cognition, perception and more recently also in action. During concept learning tasks, for example, it has been suggested that characteristic features are abstracted from a set of examples with the consequence that learning of similar tasks is facilitated—a process termed ‘learning to learn’. From a computational point of view such an extraction of invariants can be regarded as learning of an underlying structure. Here we review the evidence for structure learning as a ‘learning to learn’ mechanism, especially in sensorimotor control where the motor system has to adapt to variable environments. We review studies demonstrating that common features of variable environments are extracted during sensorimotor learning and exploited for efficient adaptation in novel tasks. We conclude that structure learning plays a fundamental role in skill learning and may underlie the unsurpassed flexibility and adaptability of the motor system. PMID:19720086

An Analysis of the Relationship between the Learning Process and Learning Motivation Profiles of Japanese Pharmacy Students Using Structural Equation Modeling.

PubMed

Yamamura, Shigeo; Takehira, Rieko

2018-04-23

Pharmacy students in Japan have to maintain strong motivation to learn for six years during their education. The authors explored the students’ learning structure. All pharmacy students in their 4th through to 6th year at Josai International University participated in the survey. The revised two factor study process questionnaire and science motivation questionnaire II were used to assess their learning process and learning motivation profiles, respectively. Structural equation modeling (SEM) was used to examine a causal relationship between the latent variables in the learning process and those in the learning motivation profile. The learning structure was modeled on the idea that the learning process affects the learning motivation profile of respondents. In the multi-group SEM, the estimated mean of the deep learning to learning motivation profile increased just after their clinical clerkship for 6th year students. This indicated that the clinical experience benefited students’ deep learning, which is probably because the experience of meeting with real patients encourages meaningful learning in pharmacy studies.

PANTHER version 11: expanded annotation data from Gene Ontology and Reactome pathways, and data analysis tool enhancements.

PubMed

Mi, Huaiyu; Huang, Xiaosong; Muruganujan, Anushya; Tang, Haiming; Mills, Caitlin; Kang, Diane; Thomas, Paul D

2017-01-04

The PANTHER database (Protein ANalysis THrough Evolutionary Relationships, http://pantherdb.org) contains comprehensive information on the evolution and function of protein-coding genes from 104 completely sequenced genomes. PANTHER software tools allow users to classify new protein sequences, and to analyze gene lists obtained from large-scale genomics experiments. In the past year, major improvements include a large expansion of classification information available in PANTHER, as well as significant enhancements to the analysis tools. Protein subfamily functional classifications have more than doubled due to progress of the Gene Ontology Phylogenetic Annotation Project. For human genes (as well as a few other organisms), PANTHER now also supports enrichment analysis using pathway classifications from the Reactome resource. The gene list enrichment tools include a new 'hierarchical view' of results, enabling users to leverage the structure of the classifications/ontologies; the tools also allow users to upload genetic variant data directly, rather than requiring prior conversion to a gene list. The updated coding single-nucleotide polymorphisms (SNP) scoring tool uses an improved algorithm. The hidden Markov model (HMM) search tools now use HMMER3, dramatically reducing search times and improving accuracy of E-value statistics. Finally, the PANTHER Tree-Attribute Viewer has been implemented in JavaScript, with new views for exploring protein sequence evolution. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Les structures de l'apprentissage en Roumanie: unite et diversite

NASA Astrophysics Data System (ADS)

Văideanu, George

1982-06-01

This analysis concerns structures of learning at the pre-university level. The concept of `learning' is used in a wide sense, including the assimilation not only of knowledge but also of know-how and attitudes. That is to say, learning has been analysed as intellectual — but also as moral, aesthetic and physical or sports — education. The author comments on the philosophy underlying the Report to the Club of Rome, No Limits to Learning. Three categories of learning structure are examined: formal, nonformal and informal. Other possibilities of grouping the structures are also indicated, including learning for society and learning for oneself. Various modalities of their articulation are presented, a distinction being made between those appropriate to school-level and those for scholarly research. Among the final conclusions and suggestions are two addressed to Unesco: a. an international round-table conference on the desirable evolution of learning; and b. the organisation of a network of experimental schools to present the desirable learning structures to educators, researchers and decision-makers.

Time-dependence of graph theory metrics in functional connectivity analysis

PubMed Central

Chiang, Sharon; Cassese, Alberto; Guindani, Michele; Vannucci, Marina; Yeh, Hsiang J.; Haneef, Zulfi; Stern, John M.

2016-01-01

Brain graphs provide a useful way to computationally model the network structure of the connectome, and this has led to increasing interest in the use of graph theory to quantitate and investigate the topological characteristics of the healthy brain and brain disorders on the network level. The majority of graph theory investigations of functional connectivity have relied on the assumption of temporal stationarity. However, recent evidence increasingly suggests that functional connectivity fluctuates over the length of the scan. In this study, we investigate the stationarity of brain network topology using a Bayesian hidden Markov model (HMM) approach that estimates the dynamic structure of graph theoretical measures of whole-brain functional connectivity. In addition to extracting the stationary distribution and transition probabilities of commonly employed graph theory measures, we propose two estimators of temporal stationarity: the S-index and N-index. These indexes can be used to quantify different aspects of the temporal stationarity of graph theory measures. We apply the method and proposed estimators to resting-state functional MRI data from healthy controls and patients with temporal lobe epilepsy. Our analysis shows that several graph theory measures, including small-world index, global integration measures, and betweenness centrality, may exhibit greater stationarity over time and therefore be more robust. Additionally, we demonstrate that accounting for subject-level differences in the level of temporal stationarity of network topology may increase discriminatory power in discriminating between disease states. Our results confirm and extend findings from other studies regarding the dynamic nature of functional connectivity, and suggest that using statistical models which explicitly account for the dynamic nature of functional connectivity in graph theory analyses may improve the sensitivity of investigations and consistency across investigations. PMID:26518632

Time-dependence of graph theory metrics in functional connectivity analysis.

PubMed

Chiang, Sharon; Cassese, Alberto; Guindani, Michele; Vannucci, Marina; Yeh, Hsiang J; Haneef, Zulfi; Stern, John M

2016-01-15

Brain graphs provide a useful way to computationally model the network structure of the connectome, and this has led to increasing interest in the use of graph theory to quantitate and investigate the topological characteristics of the healthy brain and brain disorders on the network level. The majority of graph theory investigations of functional connectivity have relied on the assumption of temporal stationarity. However, recent evidence increasingly suggests that functional connectivity fluctuates over the length of the scan. In this study, we investigate the stationarity of brain network topology using a Bayesian hidden Markov model (HMM) approach that estimates the dynamic structure of graph theoretical measures of whole-brain functional connectivity. In addition to extracting the stationary distribution and transition probabilities of commonly employed graph theory measures, we propose two estimators of temporal stationarity: the S-index and N-index. These indexes can be used to quantify different aspects of the temporal stationarity of graph theory measures. We apply the method and proposed estimators to resting-state functional MRI data from healthy controls and patients with temporal lobe epilepsy. Our analysis shows that several graph theory measures, including small-world index, global integration measures, and betweenness centrality, may exhibit greater stationarity over time and therefore be more robust. Additionally, we demonstrate that accounting for subject-level differences in the level of temporal stationarity of network topology may increase discriminatory power in discriminating between disease states. Our results confirm and extend findings from other studies regarding the dynamic nature of functional connectivity, and suggest that using statistical models which explicitly account for the dynamic nature of functional connectivity in graph theory analyses may improve the sensitivity of investigations and consistency across investigations. Copyright © 2015 Elsevier Inc. All rights reserved.

Fast and accurate inference of local ancestry in Latino populations

PubMed Central

Baran, Yael; Pasaniuc, Bogdan; Sankararaman, Sriram; Torgerson, Dara G.; Gignoux, Christopher; Eng, Celeste; Rodriguez-Cintron, William; Chapela, Rocio; Ford, Jean G.; Avila, Pedro C.; Rodriguez-Santana, Jose; Burchard, Esteban Gonzàlez; Halperin, Eran

2012-01-01

Motivation: It is becoming increasingly evident that the analysis of genotype data from recently admixed populations is providing important insights into medical genetics and population history. Such analyses have been used to identify novel disease loci, to understand recombination rate variation and to detect recent selection events. The utility of such studies crucially depends on accurate and unbiased estimation of the ancestry at every genomic locus in recently admixed populations. Although various methods have been proposed and shown to be extremely accurate in two-way admixtures (e.g. African Americans), only a few approaches have been proposed and thoroughly benchmarked on multi-way admixtures (e.g. Latino populations of the Americas). Results: To address these challenges we introduce here methods for local ancestry inference which leverage the structure of linkage disequilibrium in the ancestral population (LAMP-LD), and incorporate the constraint of Mendelian segregation when inferring local ancestry in nuclear family trios (LAMP-HAP). Our algorithms uniquely combine hidden Markov models (HMMs) of haplotype diversity within a novel window-based framework to achieve superior accuracy as compared with published methods. Further, unlike previous methods, the structure of our HMM does not depend on the number of reference haplotypes but on a fixed constant, and it is thereby capable of utilizing large datasets while remaining highly efficient and robust to over-fitting. Through simulations and analysis of real data from 489 nuclear trio families from the mainland US, Puerto Rico and Mexico, we demonstrate that our methods achieve superior accuracy compared with published methods for local ancestry inference in Latinos. Availability: http://lamp.icsi.berkeley.edu/lamp/lampld/ Contact: bpasaniu@hsph.harvard.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:22495753

Flexible Modeling of Latent Task Structures in Multitask Learning

DTIC Science & Technology

2012-06-26

Flexible Modeling of Latent Task Structures in Multitask Learning Alexandre Passos† apassos@cs.umass.edu Computer Science Department, University of...of Maryland, College Park, MD USA Abstract Multitask learning algorithms are typically designed assuming some fixed, a priori known latent structure...shared by all the tasks. However, it is usually unclear what type of latent task structure is the most ap- propriate for a given multitask learning prob

The Development of Logical Structures for E-Learning Evaluation

ERIC Educational Resources Information Center

Tudevdagva, Uranchimeg; Hardt, Wolfram; Dolgor, Jargalmaa

2013-01-01

This paper deals with development of logical structures for e-learning evaluation. Evaluation is a complex task into which many different groups of people are involved. As a rule these groups have different understanding and varying expectations on e-learning evaluation. Using logical structures for e-learning evaluation we can join the different…

A Project Focusing on Superintendents' Knowledge of Evidence-Based Practices of Structuring Time for Student Learning

ERIC Educational Resources Information Center

Lewis, Jared R.

2016-01-01

This report describes a problem based learning project focusing on superintendents' knowledge of evidence-based practices of structuring time for student learning. Current research findings offer evidence that structuring time for student learning is an important factor in student achievement. School district superintendents are challenged with…

Commitment-Based Learning of Hidden Linguistic Structures

ERIC Educational Resources Information Center

Akers, Crystal Gayle

2012-01-01

Learners must simultaneously learn a grammar and a lexicon from observed forms, yet some structures that the grammar and lexicon reference are unobservable in the acoustic signal. Moreover, these "hidden" structures interact: the grammar maps an underlying form to a particular interpretation. Learning one structure depends on learning…

Lesions of the fornix and anterior thalamic nuclei dissociate different aspects of hippocampal-dependent spatial learning: implications for the neural basis of scene learning.

PubMed

Aggleton, John P; Poirier, Guillaume L; Aggleton, Hugh S; Vann, Seralynne D; Pearce, John M

2009-06-01

The present study used 2 different discrimination tasks designed to isolate distinct components of visuospatial learning: structural learning and geometric learning. Structural learning refers to the ability to learn the precise combination of stimulus identity with stimulus location. Rats with anterior thalamic lesions and fornix lesions were unimpaired on a configural learning task in which the rats learned 3 concurrent mirror-image discriminations (structural learning). Indeed, both lesions led to facilitated learning. In contrast, anterior thalamic lesions impaired the geometric discrimination (e.g., swim to the corner with the short wall to the right of the long wall). Finally, both the fornix and anterior thalamic lesions severely impaired T-maze alternation, a task that taxes an array of spatial strategies including allocentric learning. This pattern of dissociations and double dissociations highlights how distinct classes of spatial learning rely on different systems, even though they may converge on the hippocampus. Consequently, the findings suggest that structural learning is heavily dependent on cortico-hippocampal interactions. In contrast, subcortical inputs (such as those from the anterior thalamus) contribute to geometric learning. Copyright (c) 2009 APA, all rights reserved.

Learning of pitch and time structures in an artificial grammar setting.

PubMed

Prince, Jon B; Stevens, Catherine J; Jones, Mari Riess; Tillmann, Barbara

2018-04-12

Despite the empirical evidence for the power of the cognitive capacity of implicit learning of structures and regularities in several modalities and materials, it remains controversial whether implicit learning extends to the learning of temporal structures and regularities. We investigated whether (a) an artificial grammar can be learned equally well when expressed in duration sequences as when expressed in pitch sequences, (b) learning of the artificial grammar in either duration or pitch (as the primary dimension) sequences can be influenced by the properties of the secondary dimension (invariant vs. randomized), and (c) learning can be boosted when the artificial grammar is expressed in both pitch and duration. After an exposure phase with grammatical sequences, learning in a subsequent test phase was assessed in a grammaticality judgment task. Participants in both the pitch and duration conditions showed incidental (not fully implicit) learning of the artificial grammar when the secondary dimension was invariant, but randomizing the pitch sequence prevented learning of the artificial grammar in duration sequences. Expressing the artificial grammar in both pitch and duration resulted in disproportionately better performance, suggesting an interaction between the learning of pitch and temporal structure. The findings are relevant to research investigating the learning of temporal structures and the learning of structures presented simultaneously in 2 dimensions (e.g., space and time, space and objects). By investigating learning, the findings provide further insight into the potential specificity of pitch and time processing, and their integrated versus independent processing, as previously debated in music cognition research. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Improving Fishing Pattern Detection from Satellite AIS Using Data Mining and Machine Learning.

PubMed

de Souza, Erico N; Boerder, Kristina; Matwin, Stan; Worm, Boris

2016-01-01

A key challenge in contemporary ecology and conservation is the accurate tracking of the spatial distribution of various human impacts, such as fishing. While coastal fisheries in national waters are closely monitored in some countries, existing maps of fishing effort elsewhere are fraught with uncertainty, especially in remote areas and the High Seas. Better understanding of the behavior of the global fishing fleets is required in order to prioritize and enforce fisheries management and conservation measures worldwide. Satellite-based Automatic Information Systems (S-AIS) are now commonly installed on most ocean-going vessels and have been proposed as a novel tool to explore the movements of fishing fleets in near real time. Here we present approaches to identify fishing activity from S-AIS data for three dominant fishing gear types: trawl, longline and purse seine. Using a large dataset containing worldwide fishing vessel tracks from 2011-2015, we developed three methods to detect and map fishing activities: for trawlers we produced a Hidden Markov Model (HMM) using vessel speed as observation variable. For longliners we have designed a Data Mining (DM) approach using an algorithm inspired from studies on animal movement. For purse seiners a multi-layered filtering strategy based on vessel speed and operation time was implemented. Validation against expert-labeled datasets showed average detection accuracies of 83% for trawler and longliner, and 97% for purse seiner. Our study represents the first comprehensive approach to detect and identify potential fishing behavior for three major gear types operating on a global scale. We hope that this work will enable new efforts to assess the spatial and temporal distribution of global fishing effort and make global fisheries activities transparent to ocean scientists, managers and the public.

Improving Fishing Pattern Detection from Satellite AIS Using Data Mining and Machine Learning

PubMed Central

Matwin, Stan; Worm, Boris

2016-01-01

A key challenge in contemporary ecology and conservation is the accurate tracking of the spatial distribution of various human impacts, such as fishing. While coastal fisheries in national waters are closely monitored in some countries, existing maps of fishing effort elsewhere are fraught with uncertainty, especially in remote areas and the High Seas. Better understanding of the behavior of the global fishing fleets is required in order to prioritize and enforce fisheries management and conservation measures worldwide. Satellite-based Automatic Information Systems (S-AIS) are now commonly installed on most ocean-going vessels and have been proposed as a novel tool to explore the movements of fishing fleets in near real time. Here we present approaches to identify fishing activity from S-AIS data for three dominant fishing gear types: trawl, longline and purse seine. Using a large dataset containing worldwide fishing vessel tracks from 2011–2015, we developed three methods to detect and map fishing activities: for trawlers we produced a Hidden Markov Model (HMM) using vessel speed as observation variable. For longliners we have designed a Data Mining (DM) approach using an algorithm inspired from studies on animal movement. For purse seiners a multi-layered filtering strategy based on vessel speed and operation time was implemented. Validation against expert-labeled datasets showed average detection accuracies of 83% for trawler and longliner, and 97% for purse seiner. Our study represents the first comprehensive approach to detect and identify potential fishing behavior for three major gear types operating on a global scale. We hope that this work will enable new efforts to assess the spatial and temporal distribution of global fishing effort and make global fisheries activities transparent to ocean scientists, managers and the public. PMID:27367425

Effects of structured versus non-structured learning on achievement and attitudes of fifth graders in a public aquarium

NASA Astrophysics Data System (ADS)

Kafka, Merryl Audrey

The investigator analyzed the main effect of a structured-learning experience in an informal setting, as well as interactions between the students' learning-style variations toward the element of structure and the imposed instructional conditions. The subjects consisted of 170 students enrolled in two public schools located in Brooklyn, New York. The students were predominantly a White multi-ethnic population consisting of 118 Caucasians, 25 Hispanics, 24 Asians, and 3 African-Americans. Three randomly assigned classes (n = 81) were provided trip sheets, which directed students on how to learn new information with written questions and directives. Three randomly assigned non-structured classes (n = 89) experienced the same exhibit in a free-form manner. Science-based criterion-referenced pre- and posttests were administered, in addition to Learning Style Inventories (Dunn, Dunn, & Price, 1996) and a modified Semantic Differential Scale (Pizzo, 1981), which was used to measure attitudinal levels. The non-structured group had access to similar content information in the form of exhibit graphics, but apparently they chose not to read it as carefully or engage in the information-seeking process as intensely as the students equipped with trip sheets. Analysis of covariance (ANCOVA) indicated that a structured-learning experience produced significantly higher science-achievement test scores than in a non-structured-learning experience (p = .0001). In addition, there was no single learning-style variation (preference, aversion, or no preference) to structure that produced significantly higher gains than another. Furthermore, attitudinal scores were not significantly different between structured and non-structured groups, as well as among homogeneous subsets of students with learning-style variations that matched, mismatched, or indicated no-preferenced positions on the element of structure. Hence, a moderate amount of structure resulted in academic gains without diminishing attitudinal scores. The fact that students' learning-style variations for sociological, design, and perceptual preferences were simultaneously accommodated in this setting may have contributed to the overall positive effects of this structure-based intervention. The diversified teaching resources of the exhibit and the sense of self-empowerment in a student-directed environment may have elevated students' attitudes regardless of their learning-style need for structure. The students' acceptance of a trip sheet that promoted the understanding of science concepts may have contributed to academic success.

The Relation of Story Structure to a Model of Conceptual Change in Science Learning

NASA Astrophysics Data System (ADS)

Klassen, Stephen

2010-03-01

Although various reasons have been proposed to explain the potential effectiveness of science stories to promote learning, no explicit relationship of stories to learning theory in science has been propounded. In this paper, two structurally analogous models are developed and compared: a structural model of stories and a temporal conceptual change model of learning. On the basis of the similarity of the models, as elaborated, it is proposed that the structure of science stories may promote a re-enactment of the learning process, and, thereby, such stories serve to encourage active learning through the generation of hypotheses and explanations. The practical implications of this theoretical analogy can be applied to the classroom in that the utilization of stories provides the opportunity for a type of re-enactment of the learning process that may encourage both engagement with the material and the development of long-term memory structures.

Design of Learning Spaces: Emotional and Cognitive Effects of Learning Environments in Relation to Child Development

ERIC Educational Resources Information Center

Arndt, Petra A.

2012-01-01

The design of learning spaces is rightly gaining more and more pedagogical attention, as they influence the learning climate and learning results in multiple ways. General structural characteristics influence the willingness to learn through emotional well-being and a sense of security. Specific structural characteristics influence cognitive…

Paradoxes of Social Networking in a Structured Web 2.0 Language Learning Community

ERIC Educational Resources Information Center

Loiseau, Mathieu; Zourou, Katerina

2012-01-01

This paper critically inquires into social networking as a set of mechanisms and associated practices developed in a structured Web 2.0 language learning community. This type of community can be roughly described as learning spaces featuring (more or less) structured language learning resources displaying at least some notions of language learning…

Identification of the sequence motif of glycoside hydrolase 13 family members

PubMed Central

Kumar, Vikash

2011-01-01

A bioinformatics analysis of sequences of enzymes of the glycoside hydrolase (GH) 13 family members such as α-amylase, cyclodextrin glycosyltransferase (CGTase), branching enzyme and cyclomaltodextrinase has been carried out in order to find out the sequence motifs that govern the reactions specificities of these enzymes by using hidden Markov model (HMM) profile. This analysis suggests the existence of such sequence motifs and residues of these motifs constituting the −1 to +3 catalytic subsites of the enzyme. Hence, by introducing mutations in the residues of these four subsites, one can change the reaction specificities of the enzymes. In general it has been observed that α -amylase sequence motif have low sequence conservation than rest of the motifs of the GH13 family members. PMID:21544166

Interactive projection for aerial dance using depth sensing camera

NASA Astrophysics Data System (ADS)

Dubnov, Tammuz; Seldess, Zachary; Dubnov, Shlomo

2014-02-01

This paper describes an interactive performance system for oor and Aerial Dance that controls visual and sonic aspects of the presentation via a depth sensing camera (MS Kinect). In order to detect, measure and track free movement in space, 3 degree of freedom (3-DOF) tracking in space (on the ground and in the air) is performed using IR markers. Gesture tracking and recognition is performed using a simpli ed HMM model that allows robust mapping of the actor's actions to graphics and sound. Additional visual e ects are achieved by segmentation of the actor body based on depth information, allowing projection of separate imagery on the performer and the backdrop. Artistic use of augmented reality performance relative to more traditional concepts of stage design and dramaturgy are discussed.

Syntactic Structure and Artificial Grammar Learning: The Learnability of Embedded Hierarchical Structures

ERIC Educational Resources Information Center

de Vries, Meinou H.; Monaghan, Padraic; Knecht, Stefan; Zwitserlood, Pienie

2008-01-01

Embedded hierarchical structures, such as "the rat the cat ate was brown", constitute a core generative property of a natural language theory. Several recent studies have reported learning of hierarchical embeddings in artificial grammar learning (AGL) tasks, and described the functional specificity of Broca's area for processing such structures.…

Effects of External Learning Aids on Learning with Ill-Structured Hypertext.

ERIC Educational Resources Information Center

Astleitner, Hermann

1997-01-01

Describes three experiments with high school and college students concerning learning with ill-structured hypertext; in each study, one different kind of external learning aid (memo pads, learning time, and teaching objectives) was manipulated and examined for its effect on intentional and incidental knowledge acquisition. Findings are discussed…

Peer-Assisted Learning in Mathematics: An Observational Study of Student Success

ERIC Educational Resources Information Center

Cheng, Dorothy; Walters, Matthew

2009-01-01

The Peer-Assisted Learning (PAL) program at the University of Minnesota has drawn from the best practices of Supplemental Instruction, Peer-Led Team Learning, Structured Learning Assistance, the Emerging Scholars Program, and other successful postsecondary peer cooperative learning models to establish guiding principles for structuring learning…

Adaptive structured dictionary learning for image fusion based on group-sparse-representation

NASA Astrophysics Data System (ADS)

Yang, Jiajie; Sun, Bin; Luo, Chengwei; Wu, Yuzhong; Xu, Limei

2018-04-01

Dictionary learning is the key process of sparse representation which is one of the most widely used image representation theories in image fusion. The existing dictionary learning method does not use the group structure information and the sparse coefficients well. In this paper, we propose a new adaptive structured dictionary learning algorithm and a l1-norm maximum fusion rule that innovatively utilizes grouped sparse coefficients to merge the images. In the dictionary learning algorithm, we do not need prior knowledge about any group structure of the dictionary. By using the characteristics of the dictionary in expressing the signal, our algorithm can automatically find the desired potential structure information that hidden in the dictionary. The fusion rule takes the physical meaning of the group structure dictionary, and makes activity-level judgement on the structure information when the images are being merged. Therefore, the fused image can retain more significant information. Comparisons have been made with several state-of-the-art dictionary learning methods and fusion rules. The experimental results demonstrate that, the dictionary learning algorithm and the fusion rule both outperform others in terms of several objective evaluation metrics.

Implicit transfer of reversed temporal structure in visuomotor sequence learning.

PubMed

Tanaka, Kanji; Watanabe, Katsumi

2014-04-01

Some spatio-temporal structures are easier to transfer implicitly in sequential learning. In this study, we investigated whether the consistent reversal of triads of learned components would support the implicit transfer of their temporal structure in visuomotor sequence learning. A triad comprised three sequential button presses ([1][2][3]) and seven consecutive triads comprised a sequence. Participants learned sequences by trial and error, until they could complete it 20 times without error. Then, they learned another sequence, in which each triad was reversed ([3][2][1]), partially reversed ([2][1][3]), or switched so as not to overlap with the other conditions ([2][3][1] or [3][1][2]). Even when the participants did not notice the alternation rule, the consistent reversal of the temporal structure of each triad led to better implicit transfer; this was confirmed in a subsequent experiment. These results suggest that the implicit transfer of the temporal structure of a learned sequence can be influenced by both the structure and consistency of the change. Copyright © 2013 Cognitive Science Society, Inc.

Causal Learning with Local Computations

ERIC Educational Resources Information Center

Fernbach, Philip M.; Sloman, Steven A.

2009-01-01

The authors proposed and tested a psychological theory of causal structure learning based on local computations. Local computations simplify complex learning problems via cues available on individual trials to update a single causal structure hypothesis. Structural inferences from local computations make minimal demands on memory, require…

Comparative analysis of machine learning methods in ligand-based virtual screening of large compound libraries.

PubMed

Ma, Xiao H; Jia, Jia; Zhu, Feng; Xue, Ying; Li, Ze R; Chen, Yu Z

2009-05-01

Machine learning methods have been explored as ligand-based virtual screening tools for facilitating drug lead discovery. These methods predict compounds of specific pharmacodynamic, pharmacokinetic or toxicological properties based on their structure-derived structural and physicochemical properties. Increasing attention has been directed at these methods because of their capability in predicting compounds of diverse structures and complex structure-activity relationships without requiring the knowledge of target 3D structure. This article reviews current progresses in using machine learning methods for virtual screening of pharmacodynamically active compounds from large compound libraries, and analyzes and compares the reported performances of machine learning tools with those of structure-based and other ligand-based (such as pharmacophore and clustering) virtual screening methods. The feasibility to improve the performance of machine learning methods in screening large libraries is discussed.

Structural Equation Modeling towards Online Learning Readiness, Academic Motivations, and Perceived Learning

ERIC Educational Resources Information Center

Horzum, Mehmet Baris; Kaymak, Zeliha Demir; Gungoren, Ozlem Canan

2015-01-01

The relationship between online learning readiness, academic motivations, and perceived learning was investigated via structural equation modeling in the research. The population of the research consisted of 750 students who studied using the online learning programs of Sakarya University. 420 of the students who volunteered for the research and…

Applying Adaptive Swarm Intelligence Technology with Structuration in Web-Based Collaborative Learning

ERIC Educational Resources Information Center

Huang, Yueh-Min; Liu, Chien-Hung

2009-01-01

One of the key challenges in the promotion of web-based learning is the development of effective collaborative learning environments. We posit that the structuration process strongly influences the effectiveness of technology used in web-based collaborative learning activities. In this paper, we propose an ant swarm collaborative learning (ASCL)…

Relationship between Online Learning Readiness and Structure and Interaction of Online Learning Students

ERIC Educational Resources Information Center

Demir Kaymak, Zeliha; Horzum, Mehmet Baris

2013-01-01

Current study tried to determine whether a relationship exists between readiness levels of the online learning students for online learning and the perceived structure and interaction in online learning environments. In the study, cross sectional survey model was used. The study was conducted with 320 voluntary students studying online learning…

Motivated Learning with Digital Learning Tasks: What about Autonomy and Structure?

ERIC Educational Resources Information Center

van Loon, Anne-Marieke; Ros, Anje; Martens, Rob

2012-01-01

In the present study, the ways in which digital learning tasks contribute to students' intrinsic motivation and learning outcomes were examined. In particular, this study explored the relative contributions of autonomy support and the provision of structure in digital learning tasks. Participants were 320 fifth- and sixth-grade students from eight…

Effect of glycation on α-crystallin structure and chaperone-like function

PubMed Central

Kumar, P. Anil; Kumar, M. Satish; Reddy, G. Bhanuprakash

2007-01-01

The chaperone-like activity of α-crystallin is considered to play an important role in the maintenance of the transparency of the eye lens. However, in the case of aging and in diabetes, the chaperone function of α-crystallin is compromized, resulting in cataract formation. Several post-translational modifications, including non-enzymatic glycation, have been shown to affect the chaperone function of α-crystallin in aging and in diabetes. A variety of agents have been identified as the predominant sources for the formation of AGEs (advanced glycation end-products) in various tissues, including the lens. Nevertheless, glycation of α-crystallin with various sugars has resulted in divergent results. In the present in vitro study, we have investigated the effect of glucose, fructose, G6P (glucose 6-phosphate) and MGO (methylglyoxal), which represent the major classes of glycating agents, on the structure and chaperone function of α-crystallin. Modification of α-crystallin with all four agents resulted in the formation of glycated protein, increased AGE fluorescence, protein cross-linking and HMM (high-molecular-mass) aggregation. Interestingly, these glycation-related profiles were found to vary with different glycating agents. For instance, CML [Nϵ-(carboxymethyl)lysine] was the predominant AGE formed upon glycation of α-crystallin with these agents. Although fructose and MGO caused significant conformational changes, there were no significant structural perturbations with glucose and G6P. With the exception of MGO modification, glycation with other sugars resulted in decreased chaperone activity in aggregation assays. However, modification with all four sugars led to the loss of chaperone activity as assessed using an enzyme inactivation assay. Glycation-induced loss of α-crystallin chaperone activity was associated with decreased hydrophobicity. Furthermore, α-crystallin isolated from glycated TSP (total lens soluble protein) had also increased AGE fluorescence, CML formation and diminished chaperone activity. These results indicate the susceptibility of α-crystallin to non-enzymatic glycation by various sugars and their derivatives, whose levels are elevated in diabetes. We also describes the effects of glycation on the structure and chaperone-like activity of α-crystallin. PMID:17696877

Automatic classification of animal vocalizations

NASA Astrophysics Data System (ADS)

Clemins, Patrick J.

2005-11-01

Bioacoustics, the study of animal vocalizations, has begun to use increasingly sophisticated analysis techniques in recent years. Some common tasks in bioacoustics are repertoire determination, call detection, individual identification, stress detection, and behavior correlation. Each research study, however, uses a wide variety of different measured variables, called features, and classification systems to accomplish these tasks. The well-established field of human speech processing has developed a number of different techniques to perform many of the aforementioned bioacoustics tasks. Melfrequency cepstral coefficients (MFCCs) and perceptual linear prediction (PLP) coefficients are two popular feature sets. The hidden Markov model (HMM), a statistical model similar to a finite autonoma machine, is the most commonly used supervised classification model and is capable of modeling both temporal and spectral variations. This research designs a framework that applies models from human speech processing for bioacoustic analysis tasks. The development of the generalized perceptual linear prediction (gPLP) feature extraction model is one of the more important novel contributions of the framework. Perceptual information from the species under study can be incorporated into the gPLP feature extraction model to represent the vocalizations as the animals might perceive them. By including this perceptual information and modifying parameters of the HMM classification system, this framework can be applied to a wide range of species. The effectiveness of the framework is shown by analyzing African elephant and beluga whale vocalizations. The features extracted from the African elephant data are used as input to a supervised classification system and compared to results from traditional statistical tests. The gPLP features extracted from the beluga whale data are used in an unsupervised classification system and the results are compared to labels assigned by experts. The development of a framework from which to build animal vocalization classifiers will provide bioacoustics researchers with a consistent platform to analyze and classify vocalizations. A common framework will also allow studies to compare results across species and institutions. In addition, the use of automated classification techniques can speed analysis and uncover behavioral correlations not readily apparent using traditional techniques.

Respiratory disease, behavior, and survival of mountain goat kids

USGS Publications Warehouse

Blanchong, Julie A.; Anderson, Christopher A.; Clark, Nicholas J.; Klaver, Robert W.; Plummer, Paul J.; Cox, Mike; Mcadoo, Caleb; Wolff, Peregrine L.

2018-01-01

Bacterial pneumonia is a threat to bighorn sheep (Ovis canadensis) populations. Bighorn sheep in the East Humboldt Mountain Range (EHR), Nevada, USA, experienced a pneumonia epizootic in 2009–2010. Testing of mountain goats (Oreamnos americanus) that were captured or found dead on this range during and after the epizootic detected bacteria commonly associated with bighorn sheep pneumonia die‐offs. Additionally, in years subsequent to the bighorn sheep epizootic, the mountain goat population had low kid:adult ratios, a common outcome for bighorn sheep populations that have experienced a pneumonia epizootic. We hypothesized that pneumonia was present and negatively affecting mountain goat kids in the EHR. From June–August 2013–2015, we attempted to observe mountain goat kids with marked adult females in the EHR at least once per week to document signs of respiratory disease; identify associations between respiratory disease, activity levels, and subsequent disappearance (i.e., death); and estimate weekly survival. Each time we observed a kid with a marked adult female, we recorded any signs of respiratory disease and collected behavior data that we fit to a 3‐state discrete hidden Markov model (HMM) to predict a kid's state (active vs. sedentary) and its probability of disappearing. We first observed clinical signs of respiratory disease in kids in late July–early August each summer. We observed 8 of 31 kids with marked adult females with signs of respiratory disease on 13 occasions. On 11 of these occasions, the HMM predicted that kids were in the sedentary state, which was associated with increased probability of subsequent death. We estimated overall probability of kid survival from June–August to be 0.19 (95% CI = 0.08–0.38), which was lower than has been reported in other mountain goat populations. We concluded that respiratory disease was present in the mountain goat kids in the EHR and negatively affected their activity levels and survival. Our results raise concerns about potential effects of pneumonia to mountain goat populations and the potential for disease transmission between mountain goats and bighorn sheep where the species are sympatric. 

Effector prediction in host-pathogen interaction based on a Markov model of a ubiquitous EPIYA motif

PubMed Central

2010-01-01

Background Effector secretion is a common strategy of pathogen in mediating host-pathogen interaction. Eight EPIYA-motif containing effectors have recently been discovered in six pathogens. Once these effectors enter host cells through type III/IV secretion systems (T3SS/T4SS), tyrosine in the EPIYA motif is phosphorylated, which triggers effectors binding other proteins to manipulate host-cell functions. The objectives of this study are to evaluate the distribution pattern of EPIYA motif in broad biological species, to predict potential effectors with EPIYA motif, and to suggest roles and biological functions of potential effectors in host-pathogen interactions. Results A hidden Markov model (HMM) of five amino acids was built for the EPIYA-motif based on the eight known effectors. Using this HMM to search the non-redundant protein database containing 9,216,047 sequences, we obtained 107,231 sequences with at least one EPIYA motif occurrence and 3115 sequences with multiple repeats of the EPIYA motif. Although the EPIYA motif exists among broad species, it is significantly over-represented in some particular groups of species. For those proteins containing at least four copies of EPIYA motif, most of them are from intracellular bacteria, extracellular bacteria with T3SS or T4SS or intracellular protozoan parasites. By combining the EPIYA motif and the adjacent SH2 binding motifs (KK, R4, Tarp and Tir), we built HMMs of nine amino acids and predicted many potential effectors in bacteria and protista by the HMMs. Some potential effectors for pathogens (such as Lawsonia intracellularis, Plasmodium falciparum and Leishmania major) are suggested. Conclusions Our study indicates that the EPIYA motif may be a ubiquitous functional site for effectors that play an important pathogenicity role in mediating host-pathogen interactions. We suggest that some intracellular protozoan parasites could secrete EPIYA-motif containing effectors through secretion systems similar to the T3SS/T4SS in bacteria. Our predicted effectors provide useful hypotheses for further studies. PMID:21143776

Enhancing Student Experiential Learning with Structured Interviews

ERIC Educational Resources Information Center

Cornell, Robert M.; Johnson, Carol B.; Schwartz, William C., Jr.

2013-01-01

Learning through experience can be rewarding but intimidating. To maximize the benefits of experiential learning assignments, students need to have confidence in their abilities. The authors report how a structured-interview instrument effectively facilitated experiential learning for accounting students without extensive content-specific…

The Experimental Research on E-Learning Instructional Design Model Based on Cognitive Flexibility Theory

NASA Astrophysics Data System (ADS)

Cao, Xianzhong; Wang, Feng; Zheng, Zhongmei

The paper reports an educational experiment on the e-Learning instructional design model based on Cognitive Flexibility Theory, the experiment were made to explore the feasibility and effectiveness of the model in promoting the learning quality in ill-structured domain. The study performed the experiment on two groups of students: one group learned through the system designed by the model and the other learned by the traditional method. The results of the experiment indicate that the e-Learning designed through the model is helpful to promote the intrinsic motivation, learning quality in ill-structured domains, ability to resolve ill-structured problem and creative thinking ability of the students.

Statistical Machine Learning for Structured and High Dimensional Data

DTIC Science & Technology

2014-09-17

AFRL-OSR-VA-TR-2014-0234 STATISTICAL MACHINE LEARNING FOR STRUCTURED AND HIGH DIMENSIONAL DATA Larry Wasserman CARNEGIE MELLON UNIVERSITY Final...Re . 8-98) v Prescribed by ANSI Std. Z39.18 14-06-2014 Final Dec 2009 - Aug 2014 Statistical Machine Learning for Structured and High Dimensional...area of resource-constrained statistical estimation. machine learning , high-dimensional statistics U U U UU John Lafferty 773-702-3813 > Research under

Cultural Markov blankets? Mind the other minds gap!. Comment on "Answering Schrödinger's question: A free-energy formulation" by Maxwell James Désormeau Ramstead et al.

NASA Astrophysics Data System (ADS)

Veissière, Samuel

2018-03-01

Ramstead et al. have pulled an impressive feat. By combining recent developments in evolutionary systems theory (EST), machine learning, and theoretical biology, they seek to apply the free-energy principle (FEP) to tackle one of the most intractable questions in the physics of life: why and how do living systems resist the second law of thermodynamics and maintain themselves in a state of bounded organization? The authors expand on a formal model of neuronal self-organization to articulate a meta-theory of perception, action, and biobehaviour that they extend from the human brain and mind to body and society. They call this model "variational neuroethology" [1]. The basic idea is simple and elegant: living systems self-organize optimally by resisting internal entropy; that is, by minimizing free-energy. The model draws on, and significantly expands on Bayesian predictive-processing (PP) theories of cognition, according to which the brain generates statistical predictions of the environment based on prior learning, and guides behaviour by working optimally to minimise prediction errors. In the neuroethology account, free energy is understood as "a function of probabilistic beliefs" encoded in an organism's internal states about external states of the world. The model thus rejoins 'enactivist' and 'affordances' accounts in phenomenology and ecological psychology, in which 'reality' for a living organism is understood as perspective-dependent, and constructed from an agent's prior dispositions ("probabilistic beliefs" in Bayesian terms). In ecological terms, an organism operates in a niche within what its dispositions in relation to features of the environment 'afford'. Ramstead et al. borrow the concept of Markov Blanket from mathematics to describe the processing of internal states and beliefs through which an organism perceives its environment. In machine learning, a Markov Blank is a learning algorithm consisting of a network of nested 'parent' and 'children' nodes for hierarchical information processing. Ramstead et al. take up this model to describe the perceptive 'veil' through which human sensory states are coupled to affordances of the broader environment. Building on the recently formulated cultural affordances paradigm, the authors extend their model to a meta-theory of the human niche, in which "cultural ensembles minimise free energy by enculturing their members so that they share common sets of precision-weighting priors". Ramstead et al. propose to enrich the cultural affordances account by bringing in the hierarchical mechanistic mind (HMM) model, which assumes the free-energy principle as a general mechanism underpinning cognitive function on evolutionary, developmental, and real-time scales. They concede, however, that ways of further integrating the HMM with cultural affordances remain an open question. As a cognitive anthropologist and co-author of the first Cultural Affordances article [2], I am happy to provide the outline of an answer. For humans, affordances are mediated through recursive loops between natural features of the environment and human conventions. A chair, for example, affords sitting for bipedal agents. This is 'natural' enough. But for humans, chairs afford sitting and not-sitting in myriad context and status-specific ways. A throne affords not-sitting for all but the monarch. In the absence of the monarch, it may afford transgressive sitting for the most daring. How do these conventional affordances come to hold with such precision? In the original model, we defined culture as collectively patterned and mutually reinforced behaviour mediated by largely implicit expectations about what one expects others to also expect - and to expect of one by extension. Environmental cues may act as triggers of affordances, but joint meta-expectations do all the mediating work. Meaning and affordances in the environment of the Homo Sapiens niche, are mostly (if not exclusively) picked up through the 'veil' of what one expects others to expect. The Markov Blanket in the human niche (the cultural Markov Blanket), thus, serves as a buffer to exploit statistical regularities in human psychology at least as much, if not more than in external states of the world. Human internal states about external states, in other words, are mediated by expectations about other humans' internal states. The nestedness of these inferences should be primarily conceptualized at the level of recursive mindreading - or inferences about other humans' internal states (about both internal and external states), dispositions, anticipations, and propositional attitudes. In order to function optimally and minimise cognitive energy in any given context, I have to know that you [the context-relevant other, actual or generalized] know that I know that you know that I know, etc. how to behave in that context. Navigating social life and cultural affordances requires the smooth acquisition, processing, and constant updating of infinitely recursive inferences about many specific, generalized, and hypothetical other minds. It might be useful to specify, thus, that the cultural Markov Blanket is one that mediates world-agent perception and action through the veil of Other Minds.

Robust Learning of High-dimensional Biological Networks with Bayesian Networks

NASA Astrophysics Data System (ADS)

Nägele, Andreas; Dejori, Mathäus; Stetter, Martin

Structure learning of Bayesian networks applied to gene expression data has become a potentially useful method to estimate interactions between genes. However, the NP-hardness of Bayesian network structure learning renders the reconstruction of the full genetic network with thousands of genes unfeasible. Consequently, the maximal network size is usually restricted dramatically to a small set of genes (corresponding with variables in the Bayesian network). Although this feature reduction step makes structure learning computationally tractable, on the downside, the learned structure might be adversely affected due to the introduction of missing genes. Additionally, gene expression data are usually very sparse with respect to the number of samples, i.e., the number of genes is much greater than the number of different observations. Given these problems, learning robust network features from microarray data is a challenging task. This chapter presents several approaches tackling the robustness issue in order to obtain a more reliable estimation of learned network features.

Domain-specific learning of grammatical structure in musical and phonological sequences.

PubMed

Bly, Benjamin Martin; Carrión, Ricardo E; Rasch, Björn

2009-01-01

Artificial grammar learning depends on acquisition of abstract structural representations rather than domain-specific representational constraints, or so many studies tell us. Using an artificial grammar task, we compared learning performance in two stimulus domains in which respondents have differing tacit prior knowledge. We found that despite grammatically identical sequence structures, learning was better for harmonically related chord sequences than for letter name sequences or harmonically unrelated chord sequences. We also found transfer effects within the musical and letter name tasks, but not across the domains. We conclude that knowledge acquired in implicit learning depends not only on abstract features of structured stimuli, but that the learning of regularities is in some respects domain-specific and strongly linked to particular features of the stimulus domain.

Learning Organisations: A Literature Review and Critique

DTIC Science & Technology

2014-01-01

autocratic, laissez - faire and democratic work-group principles attributed to Lewin, provided evidence that people would learn and self-manage in an...each with their own particular emphasis on learning, leadership behaviours and organisational structure. A Learning Organisation’s salient...the organisational and structural factors that affect learning. These include the importance specific leadership actions or practices, the utility of

The Use of a Mobile Learning Management System at an Online University and Its Effect on Learning Satisfaction and Achievement

ERIC Educational Resources Information Center

Shin, Won Sug; Kang, Minseok

2015-01-01

This study investigates online students' acceptance of mobile learning and its influence on learning achievement using an information system success and extended technology acceptance model (TAM). Structural equation modeling was used to test the structure of individual, social, and systemic factors influencing mobile learning's acceptance, and…

Learning in Structured Connectionist Networks

DTIC Science & Technology

1988-04-01

the structure is too rigid and learning too difficult for cognitive modeling. Two algorithms for learning simple, feature-based concept descriptions...and learning too difficult for cognitive model- ing. Two algorithms for learning simple, feature-based concept descriptions were also implemented. The...Term Goals Recent progress in connectionist research has been encouraging; networks have success- fully modeled human performance for various cognitive

Non-Gaussian Methods for Causal Structure Learning.

PubMed

Shimizu, Shohei

2018-05-22

Causal structure learning is one of the most exciting new topics in the fields of machine learning and statistics. In many empirical sciences including prevention science, the causal mechanisms underlying various phenomena need to be studied. Nevertheless, in many cases, classical methods for causal structure learning are not capable of estimating the causal structure of variables. This is because it explicitly or implicitly assumes Gaussianity of data and typically utilizes only the covariance structure. In many applications, however, non-Gaussian data are often obtained, which means that more information may be contained in the data distribution than the covariance matrix is capable of containing. Thus, many new methods have recently been proposed for using the non-Gaussian structure of data and inferring the causal structure of variables. This paper introduces prevention scientists to such causal structure learning methods, particularly those based on the linear, non-Gaussian, acyclic model known as LiNGAM. These non-Gaussian data analysis tools can fully estimate the underlying causal structures of variables under assumptions even in the presence of unobserved common causes. This feature is in contrast to other approaches. A simulated example is also provided.

Fragile X Mental Retardation Protein and Dendritic Local Translation of the Alpha Subunit of the Calcium/Calmodulin-Dependent Kinase II Messenger RNA Are Required for the Structural Plasticity Underlying Olfactory Learning.

PubMed

Daroles, Laura; Gribaudo, Simona; Doulazmi, Mohamed; Scotto-Lomassese, Sophie; Dubacq, Caroline; Mandairon, Nathalie; Greer, Charles August; Didier, Anne; Trembleau, Alain; Caillé, Isabelle

2016-07-15

In the adult brain, structural plasticity allowing gain or loss of synapses remodels circuits to support learning. In fragile X syndrome, the absence of fragile X mental retardation protein (FMRP) leads to defects in plasticity and learning deficits. FMRP is a master regulator of local translation but its implication in learning-induced structural plasticity is unknown. Using an olfactory learning task requiring adult-born olfactory bulb neurons and cell-specific ablation of FMRP, we investigated whether learning shapes adult-born neuron morphology during their synaptic integration and its dependence on FMRP. We used alpha subunit of the calcium/calmodulin-dependent kinase II (αCaMKII) mutant mice with altered dendritic localization of αCaMKII messenger RNA, as well as a reporter of αCaMKII local translation to investigate the role of this FMRP messenger RNA target in learning-dependent structural plasticity. Learning induces profound changes in dendritic architecture and spine morphology of adult-born neurons that are prevented by ablation of FMRP in adult-born neurons and rescued by an metabotropic glutamate receptor 5 antagonist. Moreover, dendritically translated αCaMKII is necessary for learning and associated structural modifications and learning triggers an FMRP-dependent increase of αCaMKII dendritic translation in adult-born neurons. Our results strongly suggest that FMRP mediates structural plasticity of olfactory bulb adult-born neurons to support olfactory learning through αCaMKII local translation. This reveals a new role for FMRP-regulated dendritic local translation in learning-induced structural plasticity. This might be of clinical relevance for the understanding of critical periods disruption in autism spectrum disorder patients, among which fragile X syndrome is the primary monogenic cause. Copyright © 2016 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Structuring Cooperative Learning in Teaching English Pronunciation

ERIC Educational Resources Information Center

Chen, Hsuan-Yu; Goswami, Jaya S.

2011-01-01

Classrooms incorporating Cooperative Learning (CL) structures facilitate a supportive learning environment for English Language Learners (ELLs). Accurate pronunciation by ELLs is important for communication, and also benefits academic achievement. The known benefits of CL for ELLs make it a desirable learning environment to teach pronunciation…

A sparse structure learning algorithm for Gaussian Bayesian Network identification from high-dimensional data.

PubMed

Huang, Shuai; Li, Jing; Ye, Jieping; Fleisher, Adam; Chen, Kewei; Wu, Teresa; Reiman, Eric

2013-06-01

Structure learning of Bayesian Networks (BNs) is an important topic in machine learning. Driven by modern applications in genetics and brain sciences, accurate and efficient learning of large-scale BN structures from high-dimensional data becomes a challenging problem. To tackle this challenge, we propose a Sparse Bayesian Network (SBN) structure learning algorithm that employs a novel formulation involving one L1-norm penalty term to impose sparsity and another penalty term to ensure that the learned BN is a Directed Acyclic Graph--a required property of BNs. Through both theoretical analysis and extensive experiments on 11 moderate and large benchmark networks with various sample sizes, we show that SBN leads to improved learning accuracy, scalability, and efficiency as compared with 10 existing popular BN learning algorithms. We apply SBN to a real-world application of brain connectivity modeling for Alzheimer's disease (AD) and reveal findings that could lead to advancements in AD research.

A Sparse Structure Learning Algorithm for Gaussian Bayesian Network Identification from High-Dimensional Data

PubMed Central

Huang, Shuai; Li, Jing; Ye, Jieping; Fleisher, Adam; Chen, Kewei; Wu, Teresa; Reiman, Eric

2014-01-01

Structure learning of Bayesian Networks (BNs) is an important topic in machine learning. Driven by modern applications in genetics and brain sciences, accurate and efficient learning of large-scale BN structures from high-dimensional data becomes a challenging problem. To tackle this challenge, we propose a Sparse Bayesian Network (SBN) structure learning algorithm that employs a novel formulation involving one L1-norm penalty term to impose sparsity and another penalty term to ensure that the learned BN is a Directed Acyclic Graph (DAG)—a required property of BNs. Through both theoretical analysis and extensive experiments on 11 moderate and large benchmark networks with various sample sizes, we show that SBN leads to improved learning accuracy, scalability, and efficiency as compared with 10 existing popular BN learning algorithms. We apply SBN to a real-world application of brain connectivity modeling for Alzheimer’s disease (AD) and reveal findings that could lead to advancements in AD research. PMID:22665720

Influence of syllable structure on L2 auditory word learning.

PubMed

Hamada, Megumi; Goya, Hideki

2015-04-01

This study investigated the role of syllable structure in L2 auditory word learning. Based on research on cross-linguistic variation of speech perception and lexical memory, it was hypothesized that Japanese L1 learners of English would learn English words with an open-syllable structure without consonant clusters better than words with a closed-syllable structure and consonant clusters. Two groups of college students (Japanese group, N = 22; and native speakers of English, N = 21) learned paired English pseudowords and pictures. The pseudoword types differed in terms of the syllable structure and consonant clusters (congruent vs. incongruent) and the position of consonant clusters (coda vs. onset). Recall accuracy was higher for the pseudowords in the congruent type and the pseudowords with the coda-consonant clusters. The syllable structure effect was obtained from both participant groups, disconfirming the hypothesized cross-linguistic influence on L2 auditory word learning.

Implicit Learning of Recursive Context-Free Grammars

PubMed Central

Rohrmeier, Martin; Fu, Qiufang; Dienes, Zoltan

2012-01-01

Context-free grammars are fundamental for the description of linguistic syntax. However, most artificial grammar learning experiments have explored learning of simpler finite-state grammars, while studies exploring context-free grammars have not assessed awareness and implicitness. This paper explores the implicit learning of context-free grammars employing features of hierarchical organization, recursive embedding and long-distance dependencies. The grammars also featured the distinction between left- and right-branching structures, as well as between centre- and tail-embedding, both distinctions found in natural languages. People acquired unconscious knowledge of relations between grammatical classes even for dependencies over long distances, in ways that went beyond learning simpler relations (e.g. n-grams) between individual words. The structural distinctions drawn from linguistics also proved important as performance was greater for tail-embedding than centre-embedding structures. The results suggest the plausibility of implicit learning of complex context-free structures, which model some features of natural languages. They support the relevance of artificial grammar learning for probing mechanisms of language learning and challenge existing theories and computational models of implicit learning. PMID:23094021

Self-Learning through Programmed Learning in Distance Mode.

ERIC Educational Resources Information Center

Rao, D. Prakasa; Reddy, B. Sudhakar

2002-01-01

Presents the characteristics and development of self-learning material (SLM) in distance education. Discusses teaching with programmed learning; structure of SLM; and how SLM helps in self-study. Discusses the advantages of print materials as accompanying programmed instruction, because they are portable, well-structured, compact, and easily…

Relationship Governance and Learning in Partnerships

ERIC Educational Resources Information Center

Kohtamaki, Marko

2010-01-01

Purpose: Relationship learning is a topic of considerable importance for industrial networks, yet a lack of empirical research on the impact of relationship governance structures on relationship learning remains. The purpose of this paper is to analyze the impact of relationship governance structures on learning in partnerships.…

Hierarchically organized behavior and its neural foundations: A reinforcement-learning perspective

PubMed Central

Botvinick, Matthew M.; Niv, Yael; Barto, Andrew C.

2009-01-01

Research on human and animal behavior has long emphasized its hierarchical structure — the divisibility of ongoing behavior into discrete tasks, which are comprised of subtask sequences, which in turn are built of simple actions. The hierarchical structure of behavior has also been of enduring interest within neuroscience, where it has been widely considered to reflect prefrontal cortical functions. In this paper, we reexamine behavioral hierarchy and its neural substrates from the point of view of recent developments in computational reinforcement learning. Specifically, we consider a set of approaches known collectively as hierarchical reinforcement learning, which extend the reinforcement learning paradigm by allowing the learning agent to aggregate actions into reusable subroutines or skills. A close look at the components of hierarchical reinforcement learning suggests how they might map onto neural structures, in particular regions within the dorsolateral and orbital prefrontal cortex. It also suggests specific ways in which hierarchical reinforcement learning might provide a complement to existing psychological models of hierarchically structured behavior. A particularly important question that hierarchical reinforcement learning brings to the fore is that of how learning identifies new action routines that are likely to provide useful building blocks in solving a wide range of future problems. Here and at many other points, hierarchical reinforcement learning offers an appealing framework for investigating the computational and neural underpinnings of hierarchically structured behavior. PMID:18926527

Constructs of Student-Centered Online Learning on Learning Satisfaction of a Diverse Online Student Body: A Structural Equation Modeling Approach

ERIC Educational Resources Information Center

Ke, Fengfeng; Kwak, Dean

2013-01-01

The present study investigated the relationships between constructs of web-based student-centered learning and the learning satisfaction of a diverse online student body. Hypotheses on the constructs of student-centered learning were tested using structural equation modeling. The results indicated that five key constructs of student-centered…

High School Students' Epistemological Beliefs, Conceptions of Learning, and Self-Efficacy for Learning Biology: A Study of Their Structural Models

ERIC Educational Resources Information Center

Sadi, Özlem; Dagyar, Miray

2015-01-01

The current work reveals the data of the study which examines the relationships among epistemological beliefs, conceptions of learning, and self-efficacy for biology learning with the help of the Structural Equation Modeling. Three questionnaires, the Epistemological Beliefs, the Conceptions of Learning Biology and the Self-efficacy for Learning…

EnzDP: improved enzyme annotation for metabolic network reconstruction based on domain composition profiles.

PubMed

Nguyen, Nam-Ninh; Srihari, Sriganesh; Leong, Hon Wai; Chong, Ket-Fah

2015-10-01

Determining the entire complement of enzymes and their enzymatic functions is a fundamental step for reconstructing the metabolic network of cells. High quality enzyme annotation helps in enhancing metabolic networks reconstructed from the genome, especially by reducing gaps and increasing the enzyme coverage. Currently, structure-based and network-based approaches can only cover a limited number of enzyme families, and the accuracy of homology-based approaches can be further improved. Bottom-up homology-based approach improves the coverage by rebuilding Hidden Markov Model (HMM) profiles for all known enzymes. However, its clustering procedure relies firmly on BLAST similarity score, ignoring protein domains/patterns, and is sensitive to changes in cut-off thresholds. Here, we use functional domain architecture to score the association between domain families and enzyme families (Domain-Enzyme Association Scoring, DEAS). The DEAS score is used to calculate the similarity between proteins, which is then used in clustering procedure, instead of using sequence similarity score. We improve the enzyme annotation protocol using a stringent classification procedure, and by choosing optimal threshold settings and checking for active sites. Our analysis shows that our stringent protocol EnzDP can cover up to 90% of enzyme families available in Swiss-Prot. It achieves a high accuracy of 94.5% based on five-fold cross-validation. EnzDP outperforms existing methods across several testing scenarios. Thus, EnzDP serves as a reliable automated tool for enzyme annotation and metabolic network reconstruction. Available at: www.comp.nus.edu.sg/~nguyennn/EnzDP .

Joint Source-Channel Decoding of Variable-Length Codes with Soft Information: A Survey

NASA Astrophysics Data System (ADS)

Guillemot, Christine; Siohan, Pierre

2005-12-01

Multimedia transmission over time-varying wireless channels presents a number of challenges beyond existing capabilities conceived so far for third-generation networks. Efficient quality-of-service (QoS) provisioning for multimedia on these channels may in particular require a loosening and a rethinking of the layer separation principle. In that context, joint source-channel decoding (JSCD) strategies have gained attention as viable alternatives to separate decoding of source and channel codes. A statistical framework based on hidden Markov models (HMM) capturing dependencies between the source and channel coding components sets the foundation for optimal design of techniques of joint decoding of source and channel codes. The problem has been largely addressed in the research community, by considering both fixed-length codes (FLC) and variable-length source codes (VLC) widely used in compression standards. Joint source-channel decoding of VLC raises specific difficulties due to the fact that the segmentation of the received bitstream into source symbols is random. This paper makes a survey of recent theoretical and practical advances in the area of JSCD with soft information of VLC-encoded sources. It first describes the main paths followed for designing efficient estimators for VLC-encoded sources, the key component of the JSCD iterative structure. It then presents the main issues involved in the application of the turbo principle to JSCD of VLC-encoded sources as well as the main approaches to source-controlled channel decoding. This survey terminates by performance illustrations with real image and video decoding systems.

Speech endpoint detection with non-language speech sounds for generic speech processing applications

NASA Astrophysics Data System (ADS)

McClain, Matthew; Romanowski, Brian

2009-05-01

Non-language speech sounds (NLSS) are sounds produced by humans that do not carry linguistic information. Examples of these sounds are coughs, clicks, breaths, and filled pauses such as "uh" and "um" in English. NLSS are prominent in conversational speech, but can be a significant source of errors in speech processing applications. Traditionally, these sounds are ignored by speech endpoint detection algorithms, where speech regions are identified in the audio signal prior to processing. The ability to filter NLSS as a pre-processing step can significantly enhance the performance of many speech processing applications, such as speaker identification, language identification, and automatic speech recognition. In order to be used in all such applications, NLSS detection must be performed without the use of language models that provide knowledge of the phonology and lexical structure of speech. This is especially relevant to situations where the languages used in the audio are not known apriori. We present the results of preliminary experiments using data from American and British English speakers, in which segments of audio are classified as language speech sounds (LSS) or NLSS using a set of acoustic features designed for language-agnostic NLSS detection and a hidden-Markov model (HMM) to model speech generation. The results of these experiments indicate that the features and model used are capable of detection certain types of NLSS, such as breaths and clicks, while detection of other types of NLSS such as filled pauses will require future research.

Distribution and prediction of catalytic domains in 2-oxoglutarate dependent dioxygenases

PubMed Central

2012-01-01

Background The 2-oxoglutarate dependent superfamily is a diverse group of non-haem dioxygenases, and is present in prokaryotes, eukaryotes, and archaea. The enzymes differ in substrate preference and reaction chemistry, a factor that precludes their classification by homology studies and electronic annotation schemes alone. In this work, I propose and explore the rationale of using substrates to classify structurally similar alpha-ketoglutarate dependent enzymes. Findings Differential catalysis in phylogenetic clades of 2-OG dependent enzymes, is determined by the interactions of a subset of active-site amino acids. Identifying these with existing computational methods is challenging and not feasible for all proteins. A clustering protocol based on validated mechanisms of catalysis of known molecules, in tandem with group specific hidden markov model profiles is able to differentiate and sequester these enzymes. Access to this repository is by a web server that compares user defined unknown sequences to these pre-defined profiles and outputs a list of predicted catalytic domains. The server is free and is accessible at the following URL ( http://comp-biol.theacms.in/H2OGpred.html). Conclusions The proposed stratification is a novel attempt at classifying and predicting 2-oxoglutarate dependent function. In addition, the server will provide researchers with a tool to compare their data to a comprehensive list of HMM profiles of catalytic domains. This work, will aid efforts by investigators to screen and characterize putative 2-OG dependent sequences. The profile database will be updated at regular intervals. PMID:22862831

Cognitive Control over Learning: Creating, Clustering, and Generalizing Task-Set Structure

ERIC Educational Resources Information Center

Collins, Anne G. E.; Frank, Michael J.

2013-01-01

Learning and executive functions such as task-switching share common neural substrates, notably prefrontal cortex and basal ganglia. Understanding how they interact requires studying how cognitive control facilitates learning but also how learning provides the (potentially hidden) structure, such as abstract rules or task-sets, needed for…

Collaborative Structures in a Graduate Program

ERIC Educational Resources Information Center

Otty, Robyn; Milton, Lauren

2016-01-01

This chapter describes the Centralized Service Learning Model (CSLM), a collaborative-teaching structure that connects two separate courses with one service-learning project. We discuss the lessons learned from applying the CSLM in our courses.

Learning the 3-D structure of objects from 2-D views depends on shape, not format

PubMed Central

Tian, Moqian; Yamins, Daniel; Grill-Spector, Kalanit

2016-01-01

Humans can learn to recognize new objects just from observing example views. However, it is unknown what structural information enables this learning. To address this question, we manipulated the amount of structural information given to subjects during unsupervised learning by varying the format of the trained views. We then tested how format affected participants' ability to discriminate similar objects across views that were rotated 90° apart. We found that, after training, participants' performance increased and generalized to new views in the same format. Surprisingly, the improvement was similar across line drawings, shape from shading, and shape from shading + stereo even though the latter two formats provide richer depth information compared to line drawings. In contrast, participants' improvement was significantly lower when training used silhouettes, suggesting that silhouettes do not have enough information to generate a robust 3-D structure. To test whether the learned object representations were format-specific or format-invariant, we examined if learning novel objects from example views transfers across formats. We found that learning objects from example line drawings transferred to shape from shading and vice versa. These results have important implications for theories of object recognition because they suggest that (a) learning the 3-D structure of objects does not require rich structural cues during training as long as shape information of internal and external features is provided and (b) learning generates shape-based object representations independent of the training format. PMID:27153196

The Structural Underpinnings of Policy Learning: A Classroom Policy Simulation

NASA Astrophysics Data System (ADS)

Bird, Stephen

This paper investigates the relationship between the centrality of individual actors in a social network structure and their policy learning performance. In a dynamic comparable to real-world policy networks, results from a classroom simulation demonstrate a strong relationship between centrality in social learning networks and grade performance. Previous research indicates that social network centrality should have a positive effect on learning in other contexts and this link is tested in a policy learning context. Second, the distinction between collaborative learning versus information diffusion processes in policy learning is examined. Third, frequency of interaction is analyzed to determine whether consistent, frequent tics have a greater impact on the learning process. Finally, the data arc analyzed to determine if the benefits of centrality have limitations or thresholds when benefits no longer accrue. These results demonstrate the importance of network structure, and support a collaborative conceptualization of the policy learning process.

Time Series Expression Analyses Using RNA-seq: A Statistical Approach

PubMed Central

Oh, Sunghee; Song, Seongho; Grabowski, Gregory; Zhao, Hongyu; Noonan, James P.

2013-01-01

RNA-seq is becoming the de facto standard approach for transcriptome analysis with ever-reducing cost. It has considerable advantages over conventional technologies (microarrays) because it allows for direct identification and quantification of transcripts. Many time series RNA-seq datasets have been collected to study the dynamic regulations of transcripts. However, statistically rigorous and computationally efficient methods are needed to explore the time-dependent changes of gene expression in biological systems. These methods should explicitly account for the dependencies of expression patterns across time points. Here, we discuss several methods that can be applied to model timecourse RNA-seq data, including statistical evolutionary trajectory index (SETI), autoregressive time-lagged regression (AR(1)), and hidden Markov model (HMM) approaches. We use three real datasets and simulation studies to demonstrate the utility of these dynamic methods in temporal analysis. PMID:23586021

Computer simulation of the activity of the elderly person living independently in a Health Smart Home.

PubMed

Noury, N; Hadidi, T

2012-12-01

We propose a simulator of human activities collected with presence sensors in our experimental Health Smart Home "Habitat Intelligent pour la Sante (HIS)". We recorded 1492 days of data on several experimental HIS during the French national project "AILISA". On these real data, we built a mathematical model of the behavior of the data series, based on "Hidden Markov Models" (HMM). The model is then played on a computer to produce simulated data series with added flexibility to adjust the parameters in various scenarios. We also tested several methods to measure the similarity between our real and simulated data. Our simulator can produce large data base which can be further used to evaluate the algorithms to raise an alarm in case of loss in autonomy. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Time series expression analyses using RNA-seq: a statistical approach.

PubMed

Oh, Sunghee; Song, Seongho; Grabowski, Gregory; Zhao, Hongyu; Noonan, James P

2013-01-01

RNA-seq is becoming the de facto standard approach for transcriptome analysis with ever-reducing cost. It has considerable advantages over conventional technologies (microarrays) because it allows for direct identification and quantification of transcripts. Many time series RNA-seq datasets have been collected to study the dynamic regulations of transcripts. However, statistically rigorous and computationally efficient methods are needed to explore the time-dependent changes of gene expression in biological systems. These methods should explicitly account for the dependencies of expression patterns across time points. Here, we discuss several methods that can be applied to model timecourse RNA-seq data, including statistical evolutionary trajectory index (SETI), autoregressive time-lagged regression (AR(1)), and hidden Markov model (HMM) approaches. We use three real datasets and simulation studies to demonstrate the utility of these dynamic methods in temporal analysis.

The extraction and integration framework: a two-process account of statistical learning.

PubMed

Thiessen, Erik D; Kronstein, Alexandra T; Hufnagle, Daniel G

2013-07-01

The term statistical learning in infancy research originally referred to sensitivity to transitional probabilities. Subsequent research has demonstrated that statistical learning contributes to infant development in a wide array of domains. The range of statistical learning phenomena necessitates a broader view of the processes underlying statistical learning. Learners are sensitive to a much wider range of statistical information than the conditional relations indexed by transitional probabilities, including distributional and cue-based statistics. We propose a novel framework that unifies learning about all of these kinds of statistical structure. From our perspective, learning about conditional relations outputs discrete representations (such as words). Integration across these discrete representations yields sensitivity to cues and distributional information. To achieve sensitivity to all of these kinds of statistical structure, our framework combines processes that extract segments of the input with processes that compare across these extracted items. In this framework, the items extracted from the input serve as exemplars in long-term memory. The similarity structure of those exemplars in long-term memory leads to the discovery of cues and categorical structure, which guides subsequent extraction. The extraction and integration framework provides a way to explain sensitivity to both conditional statistical structure (such as transitional probabilities) and distributional statistical structure (such as item frequency and variability), and also a framework for thinking about how these different aspects of statistical learning influence each other. 2013 APA, all rights reserved

Learning about the internal structure of categories through classification and feature inference.

PubMed

Jee, Benjamin D; Wiley, Jennifer

2014-01-01

Previous research on category learning has found that classification tasks produce representations that are skewed toward diagnostic feature dimensions, whereas feature inference tasks lead to richer representations of within-category structure. Yet, prior studies often measure category knowledge through tasks that involve identifying only the typical features of a category. This neglects an important aspect of a category's internal structure: how typical and atypical features are distributed within a category. The present experiments tested the hypothesis that inference learning results in richer knowledge of internal category structure than classification learning. We introduced several new measures to probe learners' representations of within-category structure. Experiment 1 found that participants in the inference condition learned and used a wider range of feature dimensions than classification learners. Classification learners, however, were more sensitive to the presence of atypical features within categories. Experiment 2 provided converging evidence that classification learners were more likely to incorporate atypical features into their representations. Inference learners were less likely to encode atypical category features, even in a "partial inference" condition that focused learners' attention on the feature dimensions relevant to classification. Overall, these results are contrary to the hypothesis that inference learning produces superior knowledge of within-category structure. Although inference learning promoted representations that included a broad range of category-typical features, classification learning promoted greater sensitivity to the distribution of typical and atypical features within categories.

Attentional Bias in Human Category Learning: The Case of Deep Learning.

PubMed

Hanson, Catherine; Caglar, Leyla Roskan; Hanson, Stephen José

2018-01-01

Category learning performance is influenced by both the nature of the category's structure and the way category features are processed during learning. Shepard (1964, 1987) showed that stimuli can have structures with features that are statistically uncorrelated (separable) or statistically correlated (integral) within categories. Humans find it much easier to learn categories having separable features, especially when attention to only a subset of relevant features is required, and harder to learn categories having integral features, which require consideration of all of the available features and integration of all the relevant category features satisfying the category rule (Garner, 1974). In contrast to humans, a single hidden layer backpropagation (BP) neural network has been shown to learn both separable and integral categories equally easily, independent of the category rule (Kruschke, 1993). This "failure" to replicate human category performance appeared to be strong evidence that connectionist networks were incapable of modeling human attentional bias. We tested the presumed limitations of attentional bias in networks in two ways: (1) by having networks learn categories with exemplars that have high feature complexity in contrast to the low dimensional stimuli previously used, and (2) by investigating whether a Deep Learning (DL) network, which has demonstrated humanlike performance in many different kinds of tasks (language translation, autonomous driving, etc.), would display human-like attentional bias during category learning. We were able to show a number of interesting results. First, we replicated the failure of BP to differentially process integral and separable category structures when low dimensional stimuli are used (Garner, 1974; Kruschke, 1993). Second, we show that using the same low dimensional stimuli, Deep Learning (DL), unlike BP but similar to humans, learns separable category structures more quickly than integral category structures. Third, we show that even BP can exhibit human like learning differences between integral and separable category structures when high dimensional stimuli (face exemplars) are used. We conclude, after visualizing the hidden unit representations, that DL appears to extend initial learning due to feature development thereby reducing destructive feature competition by incrementally refining feature detectors throughout later layers until a tipping point (in terms of error) is reached resulting in rapid asymptotic learning.

Influence of Syllable Structure on L2 Auditory Word Learning

ERIC Educational Resources Information Center

Hamada, Megumi; Goya, Hideki

2015-01-01

This study investigated the role of syllable structure in L2 auditory word learning. Based on research on cross-linguistic variation of speech perception and lexical memory, it was hypothesized that Japanese L1 learners of English would learn English words with an open-syllable structure without consonant clusters better than words with a…

An Individual or a Group Grade: Exploring Reward Structures and Motivation for Learning

ERIC Educational Resources Information Center

Collins, C. S.

2012-01-01

From a student perspective, grades are a central part in the educational experience. In an effort to learn more about student motivation for learning and grades, this study was designed to examine student reactions to the opportunity to choose between the traditional individual grading structure and a group grading structure where all students…

Concept Map Structure, Gender and Teaching Methods: An Investigation of Students' Science Learning

ERIC Educational Resources Information Center

Gerstner, Sabine; Bogner, Franz X.

2009-01-01

Background: This study deals with the application of concept mapping to the teaching and learning of a science topic with secondary school students in Germany. Purpose: The main research questions were: (1) Do different teaching approaches affect concept map structure or students' learning success? (2) Is the structure of concept maps influenced…

Wavefront cellular learning automata.

PubMed

Moradabadi, Behnaz; Meybodi, Mohammad Reza

2018-02-01

This paper proposes a new cellular learning automaton, called a wavefront cellular learning automaton (WCLA). The proposed WCLA has a set of learning automata mapped to a connected structure and uses this structure to propagate the state changes of the learning automata over the structure using waves. In the WCLA, after one learning automaton chooses its action, if this chosen action is different from the previous action, it can send a wave to its neighbors and activate them. Each neighbor receiving the wave is activated and must choose a new action. This structure for the WCLA is necessary in many dynamic areas such as social networks, computer networks, grid computing, and web mining. In this paper, we introduce the WCLA framework as an optimization tool with diffusion capability, study its behavior over time using ordinary differential equation solutions, and present its accuracy using expediency analysis. To show the superiority of the proposed WCLA, we compare the proposed method with some other types of cellular learning automata using two benchmark problems.

Wavefront cellular learning automata

NASA Astrophysics Data System (ADS)

Moradabadi, Behnaz; Meybodi, Mohammad Reza

2018-02-01

This paper proposes a new cellular learning automaton, called a wavefront cellular learning automaton (WCLA). The proposed WCLA has a set of learning automata mapped to a connected structure and uses this structure to propagate the state changes of the learning automata over the structure using waves. In the WCLA, after one learning automaton chooses its action, if this chosen action is different from the previous action, it can send a wave to its neighbors and activate them. Each neighbor receiving the wave is activated and must choose a new action. This structure for the WCLA is necessary in many dynamic areas such as social networks, computer networks, grid computing, and web mining. In this paper, we introduce the WCLA framework as an optimization tool with diffusion capability, study its behavior over time using ordinary differential equation solutions, and present its accuracy using expediency analysis. To show the superiority of the proposed WCLA, we compare the proposed method with some other types of cellular learning automata using two benchmark problems.

Structural Priming as Learning: Evidence from Mandarin-Learning 5-Year-Olds

ERIC Educational Resources Information Center

Hsu, Dong-Bo

2014-01-01

Three experiments on structural priming in Mandarin-speaking 5-year-olds were conducted to test the priming as implicit learning hypothesis. It describes a learning mechanism that acts on a shared abstract syntactic representation in response to linguistic input using an equi-biased Mandarin SVO-"ba" alternation. The first two…

A Comparison of Organizational Structure and Pedagogical Approach: Online versus Face-to-Face

ERIC Educational Resources Information Center

McFarlane, Donovan A.

2011-01-01

This paper examines online versus face-to-face organizational structure and pedagogy in terms of education and the teaching and learning process. The author distinguishes several important terms related to distance/online/e-learning, virtual learning and brick-and-mortar learning interactions and concepts such as asynchronous and synchronous…

Vocabulary Learning on the Internet: Using a Structured Think-Aloud Procedure

ERIC Educational Resources Information Center

Ebner, Rachel J.; Ehri, Linnea C.

2013-01-01

Using the Internet as a learning tool has great promise, but also poses significant challenges. Theories and research confirm the importance of students' engagement in self-regulated learning processes for effective Internet learning. In this article the Authors describe a structured think-aloud procedure intended to support students'…

Evaluation of Two Teaching Programs Based on Structural Learning Principles.

ERIC Educational Resources Information Center

Haussler, Peter

1978-01-01

Structural learning theory and the Rasch model measured learning gain, retention, and transfer in 1,037 students, grades 7-10. Students learned nine functional relationships with either spontaneous or synthetic algorithms. The Rasch model gave the better description of the data. The hypothesis that the synthetic method was superior was refuted.…

Learning to Cook: Production Learning Environment in Kitchens

ERIC Educational Resources Information Center

James, Susan

2006-01-01

Learning in workplaces is neither ad hoc nor informal. Such labels are a misnomer and do not do justice to the highly-structured nature and complexity of many workplaces where learning takes place. This article discusses the organisational and structural framework developed from a three-year doctoral study into how apprentice chefs construct their…

Collective Learning and Path Plasticity as Means to Regional Economic Resilience: The Case of Stuttgart

ERIC Educational Resources Information Center

Wink, Rüdiger; Kirchner, Laura; Koch, Florian; Speda, Daniel

2015-01-01

This paper links two strands of literature (collective learning and resilience) by looking at experiences with collective learning as precondition of regional economic resilience. Based on a qualitative empirical study, the emergence of collective learning structures in the Stuttgart region after a macroeconomic and structural crisis at the…

Cognitive control over learning: Creating, clustering and generalizing task-set structure

PubMed Central

Collins, Anne G.E.; Frank, Michael J.

2013-01-01

Executive functions and learning share common neural substrates essential for their expression, notably in prefrontal cortex and basal ganglia. Understanding how they interact requires studying how cognitive control facilitates learning, but also how learning provides the (potentially hidden) structure, such as abstract rules or task-sets, needed for cognitive control. We investigate this question from three complementary angles. First, we develop a new computational “C-TS” (context-task-set) model inspired by non-parametric Bayesian methods, specifying how the learner might infer hidden structure and decide whether to re-use that structure in new situations, or to create new structure. Second, we develop a neurobiologically explicit model to assess potential mechanisms of such interactive structured learning in multiple circuits linking frontal cortex and basal ganglia. We systematically explore the link betweens these levels of modeling across multiple task demands. We find that the network provides an approximate implementation of high level C-TS computations, where manipulations of specific neural mechanisms are well captured by variations in distinct C-TS parameters. Third, this synergism across models yields strong predictions about the nature of human optimal and suboptimal choices and response times during learning. In particular, the models suggest that participants spontaneously build task-set structure into a learning problem when not cued to do so, which predicts positive and negative transfer in subsequent generalization tests. We provide evidence for these predictions in two experiments and show that the C-TS model provides a good quantitative fit to human sequences of choices in this task. These findings implicate a strong tendency to interactively engage cognitive control and learning, resulting in structured abstract representations that afford generalization opportunities, and thus potentially long-term rather than short-term optimality. PMID:23356780

Differently Structured Advance Organizers Lead to Different Initial Schemata and Learning Outcomes

ERIC Educational Resources Information Center

Gurlitt, Johannes; Dummel, Sebastian; Schuster, Silvia; Nuckles, Matthias

2012-01-01

Does the specific structure of advance organizers influence learning outcomes? In the first experiment, 48 psychology students were randomly assigned to three differently structured advance organizers: a well-structured, a well-structured and key-concept emphasizing, and a less structured advance organizer. These were followed by a sorting task, a…

Evolution of individual versus social learning on social networks

PubMed Central

Tamura, Kohei; Kobayashi, Yutaka; Ihara, Yasuo

2015-01-01

A number of studies have investigated the roles played by individual and social learning in cultural phenomena and the relative advantages of the two learning strategies in variable environments. Because social learning involves the acquisition of behaviours from others, its utility depends on the availability of ‘cultural models’ exhibiting adaptive behaviours. This indicates that social networks play an essential role in the evolution of learning. However, possible effects of social structure on the evolution of learning have not been fully explored. Here, we develop a mathematical model to explore the evolutionary dynamics of learning strategies on social networks. We first derive the condition under which social learners (SLs) are selectively favoured over individual learners in a broad range of social network. We then obtain an analytical approximation of the long-term average frequency of SLs in homogeneous networks, from which we specify the condition, in terms of three relatedness measures, for social structure to facilitate the long-term evolution of social learning. Finally, we evaluate our approximation by Monte Carlo simulations in complete graphs, regular random graphs and scale-free networks. We formally show that whether social structure favours the evolution of social learning is determined by the relative magnitudes of two effects of social structure: localization in competition, by which competition between learning strategies is evaded, and localization in cultural transmission, which slows down the spread of adaptive traits. In addition, our estimates of the relatedness measures suggest that social structure disfavours the evolution of social learning when selection is weak. PMID:25631568

Evolution of individual versus social learning on social networks.

PubMed

Tamura, Kohei; Kobayashi, Yutaka; Ihara, Yasuo

2015-03-06

A number of studies have investigated the roles played by individual and social learning in cultural phenomena and the relative advantages of the two learning strategies in variable environments. Because social learning involves the acquisition of behaviours from others, its utility depends on the availability of 'cultural models' exhibiting adaptive behaviours. This indicates that social networks play an essential role in the evolution of learning. However, possible effects of social structure on the evolution of learning have not been fully explored. Here, we develop a mathematical model to explore the evolutionary dynamics of learning strategies on social networks. We first derive the condition under which social learners (SLs) are selectively favoured over individual learners in a broad range of social network. We then obtain an analytical approximation of the long-term average frequency of SLs in homogeneous networks, from which we specify the condition, in terms of three relatedness measures, for social structure to facilitate the long-term evolution of social learning. Finally, we evaluate our approximation by Monte Carlo simulations in complete graphs, regular random graphs and scale-free networks. We formally show that whether social structure favours the evolution of social learning is determined by the relative magnitudes of two effects of social structure: localization in competition, by which competition between learning strategies is evaded, and localization in cultural transmission, which slows down the spread of adaptive traits. In addition, our estimates of the relatedness measures suggest that social structure disfavours the evolution of social learning when selection is weak. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Self-Taught Low-Rank Coding for Visual Learning.

PubMed

Li, Sheng; Li, Kang; Fu, Yun

2018-03-01

The lack of labeled data presents a common challenge in many computer vision and machine learning tasks. Semisupervised learning and transfer learning methods have been developed to tackle this challenge by utilizing auxiliary samples from the same domain or from a different domain, respectively. Self-taught learning, which is a special type of transfer learning, has fewer restrictions on the choice of auxiliary data. It has shown promising performance in visual learning. However, existing self-taught learning methods usually ignore the structure information in data. In this paper, we focus on building a self-taught coding framework, which can effectively utilize the rich low-level pattern information abstracted from the auxiliary domain, in order to characterize the high-level structural information in the target domain. By leveraging a high quality dictionary learned across auxiliary and target domains, the proposed approach learns expressive codings for the samples in the target domain. Since many types of visual data have been proven to contain subspace structures, a low-rank constraint is introduced into the coding objective to better characterize the structure of the given target set. The proposed representation learning framework is called self-taught low-rank (S-Low) coding, which can be formulated as a nonconvex rank-minimization and dictionary learning problem. We devise an efficient majorization-minimization augmented Lagrange multiplier algorithm to solve it. Based on the proposed S-Low coding mechanism, both unsupervised and supervised visual learning algorithms are derived. Extensive experiments on five benchmark data sets demonstrate the effectiveness of our approach.

Machine learning for autonomous crystal structure identification.

PubMed

Reinhart, Wesley F; Long, Andrew W; Howard, Michael P; Ferguson, Andrew L; Panagiotopoulos, Athanassios Z

2017-07-21

We present a machine learning technique to discover and distinguish relevant ordered structures from molecular simulation snapshots or particle tracking data. Unlike other popular methods for structural identification, our technique requires no a priori description of the target structures. Instead, we use nonlinear manifold learning to infer structural relationships between particles according to the topology of their local environment. This graph-based approach yields unbiased structural information which allows us to quantify the crystalline character of particles near defects, grain boundaries, and interfaces. We demonstrate the method by classifying particles in a simulation of colloidal crystallization, and show that our method identifies structural features that are missed by standard techniques.

Structure identification in fuzzy inference using reinforcement learning

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.; Khedkar, Pratap

1993-01-01

In our previous work on the GARIC architecture, we have shown that the system can start with surface structure of the knowledge base (i.e., the linguistic expression of the rules) and learn the deep structure (i.e., the fuzzy membership functions of the labels used in the rules) by using reinforcement learning. Assuming the surface structure, GARIC refines the fuzzy membership functions used in the consequents of the rules using a gradient descent procedure. This hybrid fuzzy logic and reinforcement learning approach can learn to balance a cart-pole system and to backup a truck to its docking location after a few trials. In this paper, we discuss how to do structure identification using reinforcement learning in fuzzy inference systems. This involves identifying both surface as well as deep structure of the knowledge base. The term set of fuzzy linguistic labels used in describing the values of each control variable must be derived. In this process, splitting a label refers to creating new labels which are more granular than the original label and merging two labels creates a more general label. Splitting and merging of labels directly transform the structure of the action selection network used in GARIC by increasing or decreasing the number of hidden layer nodes.

Learning predictive statistics from temporal sequences: Dynamics and strategies

PubMed Central

Wang, Rui; Shen, Yuan; Tino, Peter; Welchman, Andrew E.; Kourtzi, Zoe

2017-01-01

Human behavior is guided by our expectations about the future. Often, we make predictions by monitoring how event sequences unfold, even though such sequences may appear incomprehensible. Event structures in the natural environment typically vary in complexity, from simple repetition to complex probabilistic combinations. How do we learn these structures? Here we investigate the dynamics of structure learning by tracking human responses to temporal sequences that change in structure unbeknownst to the participants. Participants were asked to predict the upcoming item following a probabilistic sequence of symbols. Using a Markov process, we created a family of sequences, from simple frequency statistics (e.g., some symbols are more probable than others) to context-based statistics (e.g., symbol probability is contingent on preceding symbols). We demonstrate the dynamics with which individuals adapt to changes in the environment's statistics—that is, they extract the behaviorally relevant structures to make predictions about upcoming events. Further, we show that this structure learning relates to individual decision strategy; faster learning of complex structures relates to selection of the most probable outcome in a given context (maximizing) rather than matching of the exact sequence statistics. Our findings provide evidence for alternate routes to learning of behaviorally relevant statistics that facilitate our ability to predict future events in variable environments. PMID:28973111

Learning predictive statistics from temporal sequences: Dynamics and strategies.

PubMed

Wang, Rui; Shen, Yuan; Tino, Peter; Welchman, Andrew E; Kourtzi, Zoe

2017-10-01

Human behavior is guided by our expectations about the future. Often, we make predictions by monitoring how event sequences unfold, even though such sequences may appear incomprehensible. Event structures in the natural environment typically vary in complexity, from simple repetition to complex probabilistic combinations. How do we learn these structures? Here we investigate the dynamics of structure learning by tracking human responses to temporal sequences that change in structure unbeknownst to the participants. Participants were asked to predict the upcoming item following a probabilistic sequence of symbols. Using a Markov process, we created a family of sequences, from simple frequency statistics (e.g., some symbols are more probable than others) to context-based statistics (e.g., symbol probability is contingent on preceding symbols). We demonstrate the dynamics with which individuals adapt to changes in the environment's statistics-that is, they extract the behaviorally relevant structures to make predictions about upcoming events. Further, we show that this structure learning relates to individual decision strategy; faster learning of complex structures relates to selection of the most probable outcome in a given context (maximizing) rather than matching of the exact sequence statistics. Our findings provide evidence for alternate routes to learning of behaviorally relevant statistics that facilitate our ability to predict future events in variable environments.

An Analysis of Density and Degree-Centrality According to the Social Networking Structure Formed in an Online Learning Environment

ERIC Educational Resources Information Center

Ergün, Esin; Usluel, Yasemin Koçak

2016-01-01

In this study, we assessed the communication structure in an educational online learning environment using social network analysis (SNA). The communication structure was examined with respect to time, and instructor's participation. The course was implemented using ELGG, a network learning environment, blended with face-to-face sessions over a…

From surprise to cognition: Some effects of the structure of C.A.L. simulation programs on the cognitive and scientific activities of young adults

NASA Astrophysics Data System (ADS)

Dicker, R. J.

The main objective of this thesis is to describe the effect on cognition of the structure of CAL simulation programs used, in science teaching. Four programs simulating a pond ecosystem were written so as to present a simulation model and to assist in cognition in different ways. Various clinically detailed methods of describing learning were developed and tried including concept maps which were found to be sammative rather than formative descriptions of learning, and to be ambiguous) and hierarchical structures (which were found to be difficult to produce). Fran these concept maps and hierarchical structures I developed my Interaction Model of Learning which can be used to describe the chronological events concerned with cognition. Using the Interaction Model, the nature of cognition and the effect that CAL program structure has on this process is described. Various scenarios are presented as a means of showing the possible effects of program structure on learning. Four forms of concept learning activity and their relationship to learning valid and alternative conceptions are described. The findings from the study are particularly related to the work of Driver (1983), Marton (1976) and Entwistle (1981).

Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture.

PubMed

Chen, C L Philip; Liu, Zhulin

2018-01-01

Broad Learning System (BLS) that aims to offer an alternative way of learning in deep structure is proposed in this paper. Deep structure and learning suffer from a time-consuming training process because of a large number of connecting parameters in filters and layers. Moreover, it encounters a complete retraining process if the structure is not sufficient to model the system. The BLS is established in the form of a flat network, where the original inputs are transferred and placed as "mapped features" in feature nodes and the structure is expanded in wide sense in the "enhancement nodes." The incremental learning algorithms are developed for fast remodeling in broad expansion without a retraining process if the network deems to be expanded. Two incremental learning algorithms are given for both the increment of the feature nodes (or filters in deep structure) and the increment of the enhancement nodes. The designed model and algorithms are very versatile for selecting a model rapidly. In addition, another incremental learning is developed for a system that has been modeled encounters a new incoming input. Specifically, the system can be remodeled in an incremental way without the entire retraining from the beginning. Satisfactory result for model reduction using singular value decomposition is conducted to simplify the final structure. Compared with existing deep neural networks, experimental results on the Modified National Institute of Standards and Technology database and NYU NORB object recognition dataset benchmark data demonstrate the effectiveness of the proposed BLS.

Influence of Learning Styles on Social Structures in Online Learning Environments

ERIC Educational Resources Information Center

Cela, Karina; Sicilia, Miguel-Ángel; Sánchez-Alonso, Salvador

2016-01-01

In e-learning settings, the interactions of students with one another, with the course content and with the instructors generate a considerable amount of information that may be useful for understanding how people learn online. The objective of the present research was to use social network analysis to explore the social structure of an e-learning…

A 5E Learning Cycle Approach-Based, Multimedia-Supplemented Instructional Unit for Structured Query Language

ERIC Educational Resources Information Center

Piyayodilokchai, Hongsiri; Panjaburee, Patcharin; Laosinchai, Parames; Ketpichainarong, Watcharee; Ruenwongsa, Pintip

2013-01-01

With the benefit of multimedia and the learning cycle approach in promoting effective active learning, this paper proposed a learning cycle approach-based, multimedia-supplemented instructional unit for Structured Query Language (SQL) for second-year undergraduate students with the aim of enhancing their basic knowledge of SQL and ability to apply…

General cognitive principles for learning structure in time and space.

PubMed

Goldstein, Michael H; Waterfall, Heidi R; Lotem, Arnon; Halpern, Joseph Y; Schwade, Jennifer A; Onnis, Luca; Edelman, Shimon

2010-06-01

How are hierarchically structured sequences of objects, events or actions learned from experience and represented in the brain? When several streams of regularities present themselves, which will be learned and which ignored? Can statistical regularities take effect on their own, or are additional factors such as behavioral outcomes expected to influence statistical learning? Answers to these questions are starting to emerge through a convergence of findings from naturalistic observations, behavioral experiments, neurobiological studies, and computational analyses and simulations. We propose that a small set of principles are at work in every situation that involves learning of structure from patterns of experience and outline a general framework that accounts for such learning. (c) 2010 Elsevier Ltd. All rights reserved.

Monitoring of Students' Interaction in Online Learning Settings by Structural Network Analysis and Indicators.

PubMed

Ammenwerth, Elske; Hackl, Werner O

2017-01-01

Learning as a constructive process works best in interaction with other learners. Support of social interaction processes is a particular challenge within online learning settings due to the spatial and temporal distribution of participants. It should thus be carefully monitored. We present structural network analysis and related indicators to analyse and visualize interaction patterns of participants in online learning settings. We validate this approach in two online courses and show how the visualization helps to monitor interaction and to identify activity profiles of learners. Structural network analysis is a feasible approach for an analysis of the intensity and direction of interaction in online learning settings.

Structured sparse linear graph embedding.

PubMed

Wang, Haixian

2012-03-01

Subspace learning is a core issue in pattern recognition and machine learning. Linear graph embedding (LGE) is a general framework for subspace learning. In this paper, we propose a structured sparse extension to LGE (SSLGE) by introducing a structured sparsity-inducing norm into LGE. Specifically, SSLGE casts the projection bases learning into a regression-type optimization problem, and then the structured sparsity regularization is applied to the regression coefficients. The regularization selects a subset of features and meanwhile encodes high-order information reflecting a priori structure information of the data. The SSLGE technique provides a unified framework for discovering structured sparse subspace. Computationally, by using a variational equality and the Procrustes transformation, SSLGE is efficiently solved with closed-form updates. Experimental results on face image show the effectiveness of the proposed method. Copyright © 2011 Elsevier Ltd. All rights reserved.

Structured Learning Teams: Reimagining Student Group Work

ERIC Educational Resources Information Center

Lendvay, Gregory C.

2014-01-01

Even in a standards-based curriculum, teachers can apply constructivist practices such as structured learning teams. In this environment, students become invested in the learning aims, triggering the desire in students to awaken, get information, interpret, remix, share, and design scenarios.

A qualitative inquiry into the effects of visualization on high school chemistry students' learning process of molecular structure

NASA Astrophysics Data System (ADS)

Deratzou, Susan

This research studies the process of high school chemistry students visualizing chemical structures and its role in learning chemical bonding and molecular structure. Minimal research exists with high school chemistry students and more research is necessary (Gabel & Sherwood, 1980; Seddon & Moore, 1986; Seddon, Tariq, & Dos Santos Veiga, 1984). Using visualization tests (Ekstrom, French, Harman, & Dermen, 1990a), a learning style inventory (Brown & Cooper, 1999), and observations through a case study design, this study found visual learners performed better, but needed more practice and training. Statistically, all five pre- and post-test visualization test comparisons were highly significant in the two-tailed t-test (p > .01). The research findings are: (1) Students who tested high in the Visual (Language and/or Numerical) and Tactile Learning Styles (and Social Learning) had an advantage. Students who learned the chemistry concepts more effectively were better at visualizing structures and using molecular models to enhance their knowledge. (2) Students showed improvement in learning after visualization practice. Training in visualization would improve students' visualization abilities and provide them with a way to think about these concepts. (3) Conceptualization of concepts indicated that visualizing ability was critical and that it could be acquired. Support for this finding was provided by pre- and post-Visualization Test data with a highly significant t-test. (4) Various molecular animation programs and websites were found to be effective. (5) Visualization and modeling of structures encompassed both two- and three-dimensional space. The Visualization Test findings suggested that the students performed better with basic rotation of structures as compared to two- and three-dimensional objects. (6) Data from observations suggest that teaching style was an important factor in student learning of molecular structure. (7) Students did learn the chemistry concepts. Based on the Visualization Test results, which showed that most of the students performed better on the post-test, the visualization experience and the abstract nature of the content allowed them to transfer some of their chemical understanding and practice to non-chemical structures. Finally, implications for teaching of chemistry, students learning chemistry, curriculum, and research for the field of chemical education were discussed.

Permaculture in higher education: Teaching sustainability through action learning

NASA Astrophysics Data System (ADS)

Battisti, Bryce Thomas

This is a case study of the use of Action Learning (AL) theory to teach and confer degrees in Permaculture and other forms of sustainability at the newly formed Gaia University International (GUI). In Chapter Two I argue that GUI, as an institution of higher learning, is organized to provide support for learning. The goal of the university structure is to provide students, called Associates, with a vehicle for accumulation of credit towards a bachelor's degree. This organizational structure is necessary, but insufficient for AL because Associates need more than an organization to provide and coordinate their degree programs. In other words, just because the network of university structures are organized in ways that make AL possible and convenient, it does not necessarily follow that Action Learning will occur for any individual Associate. The support structures within GUI's degrees are discussed in Chapter Three. To a greater or lesser degree GUI provides support for personal learning among Associates as advisors and advisees with the goal of helping Associates complete and document the outcomes of world-change projects. The support structures are necessary, but not sufficient for AL because the personal learning process occurring for each Associate requires transformative reflection. Additionally, because Associates' attrition rate is very high, many Associates do not remain enrolled in GUI long enough to benefit from the support structures. At the simplest organizational level I discuss the reflection process conducted in the patterned interactions of assigned learning groups called Guilds (Chapter Four). These groups of Associates work to provide each other with the best possible environment for personal learning through reflection. As its Associates experience transformative reflection, GUI is able to help elevate the quality of world-change efforts in the Permaculture community. Provided the organizational and support structures are in place, this reflection process is both necessary and sufficient for AL. By this I mean that if transformative reflection is occurring in Guild meetings, and is supported by a system of advisors, reviewers and support people within a university organized to give credit for Action Learning, then Action Learning will occur for individual Associates.

A Construct-Modeling Approach to Develop a Learning Progression of How Students Understand the Structure of Matter

ERIC Educational Resources Information Center

Morell, Linda; Collier, Tina; Black, Paul; Wilson, Mark

2017-01-01

This paper builds on the current literature base about learning progressions in science to address the question, "What is the nature of the learning progression in the content domain of the structure of matter?" We introduce a learning progression in response to that question and illustrate a methodology, the Construct Modeling (Wilson,…

Building Capacity in Understanding Foundational Biology Concepts: A K-12 Learning Progression in Genetics Informed by Research on Children's Thinking and Learning

ERIC Educational Resources Information Center

Elmesky, Rowhea

2013-01-01

This article describes the substance, structure, and rationale of a learning progression in genetics spanning kindergarten through twelfth grade (K-12). The learning progression is designed to build a foundation towards understanding protein structure and activity and should be viewed as one possible pathway to understanding concepts of genetics…

Learning New Grammatical Structures in Task-Based Language Learning: The Effects of Recasts and Prompts

ERIC Educational Resources Information Center

Van de Guchte, Marrit; Braaksma, Martine; Rijlaarsdam, Gert; Bimmel, Peter

2015-01-01

In the present study, we examine the effects of prompts and recasts on the acquisition of two new and different grammar structures in a task-based learning environment. Sixty-four 14-year-old 9th grade students (low intermediate) learning German as a foreign language were randomly assigned to three conditions: two experimental groups (one…

The Role of Goal Structure in Undergraduates' Use of Self-Regulatory Processes in Two Hypermedia Learning Tasks

ERIC Educational Resources Information Center

Moos, Daniel C.; Azevedo, Roger

2006-01-01

We collected think-aloud and posttest data from 60 undergraduates to examine whether they used different proportions of self-regulated learning (SRL) variables in two related learning tasks about science topics while using a hypermedia environment. We also manipulated the goal structure of the two hypermedia learning tasks to explore whether the…

Undergraduate Students' Conceptions of and Approaches to Learning in Biology: A Study of Their Structural Models and Gender Differences

ERIC Educational Resources Information Center

Chiou, Guo-Li; Liang, Jyh-Chong; Tsai, Chin-Chung

2012-01-01

This study reports the findings of a study which examined the relationship between conceptions of learning and approaches to learning in biology. This study, which used structural equation modelling, also sorted to identify gender differences in the relationship. Two questionnaires, the Conceptions of Learning Biology (COLB) and the Approaches to…

Structural Enhancement of Learning

ERIC Educational Resources Information Center

Trumpower, David L.; Goldsmith, Timothy E.

2004-01-01

Structural learning aids, such as interactive overviews (IOs), have previously been shown to facilitate text comprehension and recall. In this study, we examined the effects of structural aids on learners' structural knowledge and their performance on a procedural transfer task. In Experiment 1, 90 college students were presented definitions of…

Embellishing Problem-Solving Examples with Deep Structure Information Facilitates Transfer

ERIC Educational Resources Information Center

Lee, Hee Seung; Betts, Shawn; Anderson, John R.

2017-01-01

Appreciation of problem structure is critical to successful learning. Two experiments investigated effective ways of communicating problem structure in a computer-based learning environment and tested whether verbal instruction is necessary to specify solution steps, when deep structure is already embellished by instructional examples.…

Comparison Promotes Learning and Transfer of Relational Categories

ERIC Educational Resources Information Center

Kurtz, Kenneth J.; Boukrina, Olga; Gentner, Dedre

2013-01-01

We investigated the effect of co-presenting training items during supervised classification learning of novel relational categories. Strong evidence exists that comparison induces a structural alignment process that renders common relational structure more salient. We hypothesized that comparisons between exemplars would facilitate learning and…

Ethics and Justice in Learning Analytics

ERIC Educational Resources Information Center

Johnson, Jeffrey Alan

2017-01-01

The many complex challenges posed by learning analytics can best be understood within a framework of structural justice, which focuses on the ways in which the informational, operational, and organizational structures of learning analytics influence students' capacities for self-development and self-determination. This places primary…

Structured and Unstructured Learning.

ERIC Educational Resources Information Center

1996

This document contains four papers presented at a sympoisum on structured and unstructured learning moderated by Catherine Sleezer at the 1996 conference of the Academy of Human Resource Development (AHRD). "Designing Experiential Learning into Organizational Work Life: Proposing a Framework for Theory and Research" (Cheri Maben-Crouch)…

Learning to Predict Combinatorial Structures

NASA Astrophysics Data System (ADS)

Vembu, Shankar

2009-12-01

The major challenge in designing a discriminative learning algorithm for predicting structured data is to address the computational issues arising from the exponential size of the output space. Existing algorithms make different assumptions to ensure efficient, polynomial time estimation of model parameters. For several combinatorial structures, including cycles, partially ordered sets, permutations and other graph classes, these assumptions do not hold. In this thesis, we address the problem of designing learning algorithms for predicting combinatorial structures by introducing two new assumptions: (i) The first assumption is that a particular counting problem can be solved efficiently. The consequence is a generalisation of the classical ridge regression for structured prediction. (ii) The second assumption is that a particular sampling problem can be solved efficiently. The consequence is a new technique for designing and analysing probabilistic structured prediction models. These results can be applied to solve several complex learning problems including but not limited to multi-label classification, multi-category hierarchical classification, and label ranking.

How learning one category influences the learning of another: intercategory generalization based on analogy and specific stimulus information.

PubMed

Nahinsky, Irwin D; Lucas, Barbara A; Edgell, Stephen E; Overfelt, Joseph; Loeb, Richard

2004-01-01

We investigated the effect of learning one category structure on the learning of a related category structure. Photograph-name combinations, called identifiers, were associated with values of four demographic attributes. Two problems were related by analogous demographic attributes, common identifiers, or both to examine the impact of common identifier, related general characteristics, and the interaction of the two variables in mediating learning transfer from one category structure to another. Problems sharing the same identifier information prompted greater positive transfer than those not sharing the same identifier information. In contrast, analogous defining characteristics in the two problems did not facilitate transfer. We computed correlations between responses to first-problem stimuli and responses to analogous second-problem stimuli for each participant. The analogous characteristics produced a tendency to respond in the same way to corresponding stimuli in the two problems. The results support an alignment between category structures related by analogous defining characteristics, which is facilitated by specific identifier information shared by two category structures.

Structural and Functional Bases for Individual Differences in Motor Learning

PubMed Central

Tomassini, Valentina; Jbabdi, Saad; Kincses, Zsigmond T.; Bosnell, Rose; Douaud, Gwenaelle; Pozzilli, Carlo; Matthews, Paul M.; Johansen-Berg, Heidi

2013-01-01

People vary in their ability to learn new motor skills. We hypothesize that between-subject variability in brain structure and function can explain differences in learning. We use brain functional and structural MRI methods to characterize such neural correlates of individual variations in motor learning. Healthy subjects applied isometric grip force of varying magnitudes with their right hands cued visually to generate smoothly-varying pressures following a regular pattern. We tested whether individual variations in motor learning were associated with anatomically colocalized variations in magnitude of functional MRI (fMRI) signal or in MRI differences related to white and grey matter microstructure. We found that individual motor learning was correlated with greater functional activation in the prefrontal, premotor, and parietal cortices, as well as in the basal ganglia and cerebellum. Structural MRI correlates were found in the premotor cortex [for fractional anisotropy (FA)] and in the cerebellum [for both grey matter density and FA]. The cerebellar microstructural differences were anatomically colocalized with fMRI correlates of learning. This study thus suggests that variations across the population in the function and structure of specific brain regions for motor control explain some of the individual differences in skill learning. This strengthens the notion that brain structure determines some limits to cognitive function even in a healthy population. Along with evidence from pathology suggesting a role for these regions in spontaneous motor recovery, our results also highlight potential targets for therapeutic interventions designed to maximize plasticity for recovery of similar visuomotor skills after brain injury. PMID:20533562

Multimodal Speaker Diarization.

PubMed

Noulas, A; Englebienne, G; Krose, B J A

2012-01-01

We present a novel probabilistic framework that fuses information coming from the audio and video modality to perform speaker diarization. The proposed framework is a Dynamic Bayesian Network (DBN) that is an extension of a factorial Hidden Markov Model (fHMM) and models the people appearing in an audiovisual recording as multimodal entities that generate observations in the audio stream, the video stream, and the joint audiovisual space. The framework is very robust to different contexts, makes no assumptions about the location of the recording equipment, and does not require labeled training data as it acquires the model parameters using the Expectation Maximization (EM) algorithm. We apply the proposed model to two meeting videos and a news broadcast video, all of which come from publicly available data sets. The results acquired in speaker diarization are in favor of the proposed multimodal framework, which outperforms the single modality analysis results and improves over the state-of-the-art audio-based speaker diarization.

A study on the real-time reliability of on-board equipment of train control system

NASA Astrophysics Data System (ADS)

Zhang, Yong; Li, Shiwei

2018-05-01

Real-time reliability evaluation is conducive to establishing a condition based maintenance system for the purpose of guaranteeing continuous train operation. According to the inherent characteristics of the on-board equipment, the connotation of reliability evaluation of on-board equipment is defined and the evaluation index of real-time reliability is provided in this paper. From the perspective of methodology and practical application, the real-time reliability of the on-board equipment is discussed in detail, and the method of evaluating the realtime reliability of on-board equipment at component level based on Hidden Markov Model (HMM) is proposed. In this method the performance degradation data is used directly to realize the accurate perception of the hidden state transition process of on-board equipment, which can achieve a better description of the real-time reliability of the equipment.

Genome-Wide Identification and Mapping of NBS-Encoding Resistance Genes in Solanum tuberosum Group Phureja

PubMed Central

Lozano, Roberto; Ponce, Olga; Ramirez, Manuel; Mostajo, Nelly; Orjeda, Gisella

2012-01-01

The majority of disease resistance (R) genes identified to date in plants encode a nucleotide-binding site (NBS) and leucine-rich repeat (LRR) domain containing protein. Additional domains such as coiled-coil (CC) and TOLL/interleukin-1 receptor (TIR) domains can also be present. In the recently sequenced Solanum tuberosum group phureja genome we used HMM models and manual curation to annotate 435 NBS-encoding R gene homologs and 142 NBS-derived genes that lack the NBS domain. Highly similar homologs for most previously documented Solanaceae R genes were identified. A surprising ∼41% (179) of the 435 NBS-encoding genes are pseudogenes primarily caused by premature stop codons or frameshift mutations. Alignment of 81.80% of the 577 homologs to S. tuberosum group phureja pseudomolecules revealed non-random distribution of the R-genes; 362 of 470 genes were found in high density clusters on 11 chromosomes. PMID:22493716

A Winning Cast

NASA Technical Reports Server (NTRS)

2001-01-01

Howmet Research Corporation was the first to commercialize an innovative cast metal technology developed at Auburn University, Auburn, Alabama. With funding assistance from NASA's Marshall Space Flight Center, Auburn University's Solidification Design Center (a NASA Commercial Space Center), developed accurate nickel-based superalloy data for casting molten metals. Through a contract agreement, Howmet used the data to develop computer model predictions of molten metals and molding materials in cast metal manufacturing. Howmet Metal Mold (HMM), part of Howmet Corporation Specialty Products, of Whitehall, Michigan, utilizes metal molds to manufacture net shape castings in various alloys and amorphous metal (metallic glass). By implementing the thermophysical property data from by Auburn researchers, Howmet employs its newly developed computer model predictions to offer customers high-quality, low-cost, products with significantly improved mechanical properties. Components fabricated with this new process replace components originally made from forgings or billet. Compared with products manufactured through traditional casting methods, Howmet's computer-modeled castings come out on top.