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https://www.readbyqxmd.com/read/28231302/bioinformatic-prediction-of-g-protein-coupled-receptor-encoding-sequences-from-the-transcriptome-of-the-foreleg-including-the-haller-s-organ-of-the-cattle-tick-rhipicephalus-australis
#1
Sergio Munoz, Felix D Guerrero, Anastasia Kellogg, Andrew M Heekin, Ming-Ying Leung
The cattle tick of Australia, Rhipicephalus australis, is a vector for microbial parasites that cause serious bovine diseases. The Haller's organ, located in the tick's forelegs, is crucial for host detection and mating. To facilitate the development of new technologies for better control of this agricultural pest, we aimed to sequence and annotate the transcriptome of the R. australis forelegs and associated tissues, including the Haller's organ. As G protein-coupled receptors (GPCRs) are an important family of eukaryotic proteins studied as pharmaceutical targets in humans, we prioritized the identification and classification of the GPCRs expressed in the foreleg tissues...
2017: PloS One
https://www.readbyqxmd.com/read/28227387/detecting-cell-division-of-pseudomonas-aeruginosa-bacteria-from-bright-field-microscopy-images-with-hidden-conditional-random-fields
#2
Lee-Ling S Ong, Xinghua Zhang, Binu Kundukad, Justin Dauwels, Patrick Doyle, H Harry Asada, Lee-Ling S Ong, Xinghua Zhang, Binu Kundukad, Justin Dauwels, Patrick Doyle, H Harry Asada, Binu Kundukad, Lee-Ling S Ong, Xinghua Zhang, Justin Dauwels, Patrick Doyle, H Harry Asada
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...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227257/why-should-you-model-time-when-you-use-markov-models-for-heart-sound-analysis
#3
Jorge Oliveira, Theofrastos Mantadelis, Miguel Coimbra, Jorge Oliveira, Theofrastos Mantadelis, Miguel Coimbra, Jorge Oliveira, Miguel Coimbra, Theofrastos Mantadelis
Auscultation is a widely used technique in clinical activity to diagnose heart diseases. However, heart sounds are difficult to interpret because a) of events with very short temporal onset between them (tens of milliseconds) and b) dominant frequencies that are out of the human audible spectrum. In this paper, we propose a model to segment heart sounds using a semi-hidden Markov model instead of a hidden Markov model. Our model in difference from the state-of-the-art hidden Markov models takes in account the temporal constraints that exist in heart cycles...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227168/a-novel-method-for-splice-sites-prediction-using-sequence-component-and-hidden-markov-model
#4
Elham Pashaei, Alper Yilmaz, Mustafa Ozen, Nizamettin Aydin, Elham Pashaei, Alper Yilmaz, Mustafa Ozen, Nizamettin Aydin, Nizamettin Aydin, Alper Yilmaz, Elham Pashaei, Mustafa Ozen
With increasing growth of DNA sequence data, it has become an urgent demand to develop new methods to accurately predict the genes. The performance of gene detection methods mainly depend on the efficiency of splice site prediction methods. In this paper, a novel method for detecting splice sites is proposed by using a new effective DNA encoding method and AdaBoost.M1 classifier. Our proposed DNA encoding method is based on multi-scale component (MSC) and first order Markov model (MM1). It has been applied to the HS3D dataset with repeated 10 fold cross validation...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227004/automated-tracking-segmentation-and-trajectory-classification-of-pelvic-organs-on-dynamic-mri
#5
Iman Nekooeimehr, Susana Lai-Yuen, Paul Bao, Alfredo Weitzenfeld, Stuart Hart, Iman Nekooeimehr, Susana Lai-Yuen, Paul Bao, Alfredo Weitzenfeld, Stuart Hart, Paul Bao, Susana Lai-Yuen, Stuart Hart, Alfredo Weitzenfeld, Iman Nekooeimehr
Pelvic organ prolapse is a major health problem in women where pelvic floor organs (bladder, uterus, small bowel, and rectum) fall from their normal position and bulge into the vagina. Dynamic Magnetic Resonance Imaging (DMRI) is presently used to analyze the organs' movements from rest to maximum strain providing complementary support for diagnosis. However, there is currently no automated or quantitative approach to measure the movement of the pelvic organs and their correlation with the severity of prolapse...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226822/hidden-markov-factor-analysis-as-a-spatiotemporal-model-for-electrocorticography
#6
Akinyinka Omigbodun, Werner K Doyle, Orrin Devinsky, Daniel Friedman, Thomas Thesen, Vikash Gilja, Akinyinka Omigbodun, Werner K Doyle, Orrin Devinsky, Daniel Friedman, Thomas Thesen, Vikash Gilja, Akinyinka Omigbodun, Thomas Thesen, Vikash Gilja, Werner K Doyle, Orrin Devinsky, Daniel Friedman
We present a new approach to extracting low-dimensional neural trajectories that summarize the electrocorticographic (ECoG) signals recorded with high-channel-count electrode arrays implanted subdurally. In our approach, Hidden-Markov Factor Analysis (HMFA), a finite set of factor analyzers are used to model the relationship between the high-dimensional ECoG neural space and a low-dimensional latent neural space; the factor analyzers at different time points are in turn linked together with a hidden Markov model...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226806/multistep-model-for-predicting-upper-limb-3d-isometric-force-application-from-pre-movement-electrocorticographic-features
#7
Jing Wu, Benjamin R Shuman, Bingni W Brunton, Katherine M Steele, Jared D Olson, Rajesh P N Rao, Jeffrey G Ojemann, Jing Wu, Benjamin R Shuman, Bingni W Brunton, Katherine M Steele, Jared D Olson, Rajesh P N Rao, Jeffrey G Ojemann, Rajesh P N Rao, Jared D Olson, Benjamin R Shuman, Katherine M Steele, Jing Wu, Jeffrey G Ojemann, Bingni W Brunton
Neural correlates of movement planning onset and direction may be present in human electrocorticography in the signal dynamics of both motor and non-motor cortical regions. We use a three-stage model of jPCA reduced-rank hidden Markov model (jPCA-RR-HMM), regularized shrunken-centroid discriminant analysis (RDA), and LASSO regression to extract direction-sensitive planning information and movement onset in an upper-limb 3D isometric force task in a human subject. This mode achieves a relatively high true positive force-onset prediction rate of 60% within 250ms, and an above-chance 36% accuracy (17% chance) in predicting one of six planned 3D directions of isometric force using pre-movement signals...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226798/decoding-speech-using-the-timing-of-neural-signal-modulation
#8
Werner Jiang, Tejaswy Pailla, Benjamin Dichter, Edward F Chang, Vikash Gilja, Werner Jiang, Tejaswy Pailla, Benjamin Dichter, Edward F Chang, Vikash Gilja, Vikash Gilja, Werner Jiang, Tejaswy Pailla, Benjamin Dichter, Edward F Chang
Brain-machine interfaces (BMIs) have great potential for applications that restore and assist communication for paralyzed individuals. Recently, BMIs decoding speech have gained considerable attention due to their potential for high information transfer rates. In this study, we propose a novel decoding approach based on hidden Markov models (HMMs) that uses the timing of neural signal changes to decode speech. We tested the decoder's performance by predicting vowels from electrocorticographic (ECoG) data of three human subjects...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226656/scoring-sequences-of-hippocampal-activity-using-hidden-markov-models
#9
Etienne Ackermann, Caleb Kemere, Etienne Ackermann, Caleb Kemere, Etienne Ackermann, Caleb Kemere
We propose a novel sequence score to determine to what extent neural activity is consistent with trajectories through latent ensemble states - virtual place fields - in an associated environment. In particular, we show how hidden Markov models (HMMs) can be used to model and analyze sequences of neural activity, and how the resulting joint probability of an observation sequence and an underlying sequence of states naturally lead to the development of a two component sequence score in which the sequential and contextual information are decoupled...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226633/autonomous-vision-guided-approach-for-the-analysis-and-grading-of-vertical-suspension-tests-during-hammersmith-infant-neurological-examination-hine
#10
Prasenjit Dey, Debi Prosad Dogra, Partha Pratim Roy, Harish Bhaskar, Prasenjit Dey, Debi Prosad Dogra, Partha Pratim Roy, Harish Bhaskar, Debi Prosad Dogra, Harish Bhaskar, Prasenjit Dey, Partha Pratim Roy
Computer vision assisted diagnostic systems are gaining popularity in different healthcare applications. This paper presents a video analysis and pattern recognition framework for the automatic grading of vertical suspension tests on infants during the Hammersmith Infant Neurological Examination (HINE). The proposed vision-guided pipeline applies a color-based skin region segmentation procedure followed by the localization of body parts before feature extraction and classification. After constrained localization of lower body parts, a stick-diagram representation is used for extracting novel features that correspond to the motion dynamic characteristics of the infant's leg movements during HINE...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226493/towards-an-unsupervised-device-for-the-diagnosis-of-childhood-pneumonia-in-low-resource-settings-automatic-segmentation-of-respiratory-sounds
#11
J Sola, F Braun, E Muntane, C Verjus, M Bertschi, F Hugon, S Manzano, M Benissa, A Gervaix, J Sola, F Braun, E Muntane, C Verjus, M Bertschi, F Hugon, S Manzano, M Benissa, A Gervaix, F Hugon, C Verjus, A Gervaix, S Manzano, M Bertschi, E Muntane, M Benissa, J Sola, F Braun
Pneumonia remains the worldwide leading cause of children mortality under the age of five, with every year 1.4 million deaths. Unfortunately, in low resource settings, very limited diagnostic support aids are provided to point-of-care practitioners. Current UNICEF/WHO case management algorithm relies on the use of a chronometer to manually count breath rates on pediatric patients: there is thus a major need for more sophisticated tools to diagnose pneumonia that increase sensitivity and specificity of breath-rate-based algorithms...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226466/using-respiratory-signals-for-the-recognition-of-human-activities
#12
Raul I Ramos-Garcia, Stephen Tiffany, Edward Sazonov, Raul I Ramos-Garcia, Stephen Tiffany, Edward Sazonov, Raul I Ramos-Garcia, Stephen Tiffany, Edward Sazonov
Human activity recognition through wearable sensors is becoming integral to health monitoring and other applications. Typically, human activity is captured through signals from inertial sensors, while signals from other sensors have been utilized less frequently. In this study, we explored the feasibility of classifying human activities by analyzing the temporal information of respiratory signals through hidden Markov models (HMMs). Left-to-right HMMs were trained for five activities: sedentary, walking, eating, talking, and cigarette smoking...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28224501/modeling-movement-primitives-with-hidden-markov-models-for-robotic-and-biomedical-applications
#13
Michelle Karg, Dana Kulić
Movement primitives are elementary motion units and can be combined sequentially or simultaneously to compose more complex movement sequences. A movement primitive timeseries consist of a sequence of motion phases. This progression through a set of motion phases can be modeled by Hidden Markov Models (HMMs). HMMs are stochastic processes that model time series data as the evolution of a hidden state variable through a discrete set of possible values, where each state value is associated with an observation (emission) probability...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/28224500/automated-estimation-of-mouse-social-behaviors-based-on-a-hidden-markov-model
#14
Toshiya Arakawa, Akira Tanave, Aki Takahashi, Satoshi Kakihara, Tsuyoshi Koide, Takashi Tsuchiya
Recent innovations in sensing and Information and Communication Technology (ICT) have enabled researchers in animal behavior to collect an enormous amount of data. Consequently, the development of an automated system to substitute for some of the observations and analyses that are performed currently by expert researchers is becoming a crucial issue so that the vast amount of accumulated data can be processed efficiently. For this purpose, we introduce a process for the automated classification of the social interactive status of two mice in a square field on the basis of a Hidden Markov model (HMM)...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/28224499/finding-rna-protein-interaction-sites-using-hmms
#15
Tao Wang, Jonghyun Yun, Yang Xie, Guanghua Xiao
RNA-binding proteins play important roles in the various stages of RNA maturation through binding to its target RNAs. Cross-linking immunoprecipitation coupled with high-throughput sequencing (CLIP-Seq) has made it possible to identify the targeting sites of RNA-binding proteins in various cell culture systems and tissue types on a genome-wide scale. Several Hidden Markov model-based (HMM) approaches have been suggested to identify protein-RNA binding sites from CLIP-Seq datasets. In this chapter, we describe how HMM can be applied to analyze CLIP-Seq datasets, including the bioinformatics preprocessing steps to extract count information from the sequencing data before HMM and the downstream analysis steps following peak-calling...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/28224497/hidden-markov-models-in-population-genomics
#16
Julien Y Dutheil
With the advent of sequencing techniques population genomics took a major shift. The structure of data sets has evolved from a sample of a few loci in the genome, sequenced in dozens of individuals, to collections of complete genomes, virtually comprising all available loci. Initially sequenced in a few individuals, such genomic data sets are now reaching and even exceeding the size of traditional data sets in the number of haplotypes sequenced. Because all loci in a genome are not independent, this evolution of data sets is mirrored by a methodological change...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/28224496/computationally-tractable-multivariate-hmm-in-genome-wide-mapping-studies
#17
Hyungwon Choi, Debashis Ghosh, Zhaohui Qin
Hidden Markov model (HMM) is widely used for modeling spatially correlated genomic data (series data). In genomics, datasets of this kind are generated from genome-wide mapping studies through high-throughput methods such as chromatin immunoprecipitation coupled with massively parallel sequencing (ChIP-seq). When multiple regulatory protein binding sites or related epigenetic modifications are mapped simultaneously, the correlation between data series can be incorporated into the latent variable inference in a multivariate form of HMM, potentially increasing the statistical power of signal detection...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/28224495/hidden-markov-models-in-bioinformatics-snv-inference-from-next-generation-sequence
#18
Jiawen Bian, Xiaobo Zhou
The rapid development of next generation sequencing (NGS) technology provides a novel avenue for genomic exploration and research. Hidden Markov models (HMMs) have wide applications in pattern recognition as well as Bioinformatics such as transcription factor binding sites and cis-regulatory modules detection. An application of HMM is introduced in this chapter with the in-deep developing of NGS. Single nucleotide variants (SNVs) inferred from NGS are expected to reveal gene mutations in cancer. However, NGS has lower sequence coverage and poor SNV detection capability in the regulatory regions of the genome...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/28224494/modelling-chip-seq-data-using-hmms
#19
Veronica Vinciotti
Chromatin ImmunoPrecipitation-sequencing (ChIP-seq) experiments have now become routine in biology for the detection of protein binding sites. In this chapter, we show how hidden Markov models can be used for the analysis of data generated by ChIP-seq experiments. We show how a hidden Markov model can naturally account for spatial dependencies in the ChIP-seq data, how it can be used in the presence of data from multiple ChIP-seq experiments under the same biological condition, and how it naturally accounts for the different IP efficiencies of individual ChIP-seq experiments...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/28224493/analyzing-single-molecule-fret-trajectories-using-hmm
#20
Kenji Okamoto
Structural dynamics of biomolecules, such as proteins, plays essential roles in many biological phenomena at molecular level. It is crucial to understand such dynamics in recent biology. The single-molecule Förster resonance energy transfer (smFRET) measurement is one of few methods that enable us to observe structural changes of biomolecules in realtime. Time series data of smFRET, however, typically contains significant fluctuation, making analysis difficult. On the other hand, one can often assume a Markov process behind such data so that the hidden Markov model (HMM) can be used to reproduce a state transition trajectory (STT)...
2017: Methods in Molecular Biology
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