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https://www.readbyqxmd.com/read/28974026/joint-toxicity-of-different-heavy-metal-mixtures-after-a-short-term-oral-repeated-administration-in-rats
#1
Hong Su, Zhou Li, Samuel Selorm Fiati Kenston, Hongbo Shi, Yafei Wang, Xin Song, Yuanliang Gu, Tabatha Barber, Joni Aldinger, Baobo Zou, Min Ding, Jinshun Zhao, Xialu Lin
The systemic toxicity of different combinations of heavy metal mixtures (HMMs) was studied according to equivalent proportions of the eight most common detectable heavy metals found in fish consumption in the Ningbo area of China. The ion mass proportions of Zn, Cu, Mn, Cr, Ni, Cd, Pb, and Hg were 1070.0, 312.6, 173.1, 82.6, 30.0, 13.3, 6.6, and 1.0, respectively. In this study, 10 experimental groups were set as follows: M8 (Pb + Cd + Hg + Ni + Cu + Zn + Mn + Cr); M5 (Pb + Cd + Hg + Ni + Cr); M4A (Pb + Cd + Hg + Ni); M4B (Cu + Zn + Mn + Cr); M3 (Cu + Zn + Mn); Cr; Cu; Zn; Mn; and control...
October 1, 2017: International Journal of Environmental Research and Public Health
https://www.readbyqxmd.com/read/28970740/maximum-likelihood-estimation-for-stochastic-volatility-in-mean-models-with-heavy-tailed-distributions
#2
Carlos A Abanto-Valle, Roland Langrock, Ming-Hui Chen, Michel V Cardoso
In this article, we introduce a likelihood-based estimation method for the stochastic volatility in mean (SVM) model with scale mixtures of normal (SMN) distributions (Abanto-Valle et al., 2012). Our estimation method is based on the fact that the powerful hidden Markov model (HMM) machinery can be applied in order to evaluate an arbitrarily accurate approximation of the likelihood of an SVM model with SMN distributions. The method is based on the proposal of Langrock et al. (2012) and makes explicit the useful link between HMMs and SVM models with SMN distributions...
July 2017: Applied Stochastic Models in Business and Industry
https://www.readbyqxmd.com/read/28964073/a-fully-automated-approach-for-baby-cry-signal-segmentation-and-boundary-detection-of-expiratory-and-inspiratory-episodes
#3
Lina Abou-Abbas, Chakib Tadj, Hesam Alaie Fersaie
The detection of cry sounds is generally an important pre-processing step for various applications involving cry analysis such as diagnostic systems, electronic monitoring systems, emotion detection, and robotics for baby caregivers. Given its complexity, an automatic cry segmentation system is a rather challenging topic. In this paper, a framework for automatic cry sound segmentation for application in a cry-based diagnostic system has been proposed. The contribution of various additional time- and frequency-domain features to increase the robustness of a Gaussian mixture model/hidden Markov model (GMM/HMM)-based cry segmentation system in noisy environments is studied...
September 2017: Journal of the Acoustical Society of America
https://www.readbyqxmd.com/read/28961686/sequence2vec-a-novel-embedding-approach-for-modeling-transcription-factor-binding-affinity-landscape
#4
Hanjun Dai, Ramzan Umarov, Hiroyuki Kuwahara, Yu Li, Le Song, Xin Gao
Motivation: An accurate characterization of transcription factor (TF)-DNA affinity landscape is crucial to a quantitative understanding of the molecular mechanisms underpinning endogenous gene regulation. While recent advances in biotechnology have brought the opportunity for building binding affinity prediction methods, the accurate characterization of TF-DNA binding affinity landscape still remains a challenging problem. Results: Here we propose a novel sequence embedding approach for modeling the transcription factor binding affinity landscape...
July 27, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28952936/dual-sticky-hierarchical-dirichlet-process-hidden-markov-model-and-its-application-to-natural-language-description-of-motions
#5
Weiming Hu, Guodong Tian, Yongxin Kang, Chunfeng Yuan, Stephen Maybank
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...
September 25, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28948473/buccal-micronucleus-cytome-assay-of-populations-under-chronic-heavy-metal-and-other-metal-exposure-along-the-santiago-river-mexico
#6
B C Gómez-Meda, G M Zúñiga-González, L V Sánchez-Orozco, A L Zamora-Perez, J P Rojas-Ramírez, A D Rocha-Muñoz, A A Sobrevilla-Navarro, M A Arellano-Avelar, A A Guerrero-de León, J S Armendáriz-Borunda, M G Sánchez-Parada
The Santiago River is one of the most contaminated rivers in Mexico, with heavy metal levels above the allowed limits. Scientific evidence indicates that chronic heavy metal exposure leads to cytogenotoxic effects. The aims of this study were to evaluate the genotoxic and cytotoxic effects of such exposure in buccal mucosa cells by micronucleus (MN) assay and to identify other nuclear abnormalities (NAs), such as nuclear buds (NBUDs), binucleated cells (BNs), pyknotic nuclei (PNs), karyorrhexis (KX), karyolysis (KL), and abnormally condensed chromatin (CC)...
September 26, 2017: Environmental Monitoring and Assessment
https://www.readbyqxmd.com/read/28945590/interactions-between-large-scale-functional-brain-networks-are-captured-by-sparse-coupled-hmms
#7
Thomas Aw Bolton, Anjali Tarun, Virginie Sterpenich, Sophie Schwartz, Dimitri Van De Ville
Functional magnetic resonance imaging (fMRI) provides a window on the human brain at work. Spontaneous brain activity measured during resting-state has already provided many insights into brain function. In particular, recent interest in dynamic interactions between brain regions has increased the need for more advanced modeling tools. Here, we deploy a recent fMRI deconvolution technique to express resting-state temporal fluctuations as a combination of large-scale functional network activity profiles. Then, building upon a novel sparse coupled hidden Markov model (SCHMM) framework, we parameterised their temporal evolution as a mix between intrinsic dynamics, and a restricted set of cross-network modulatory couplings extracted in data-driven manner...
September 21, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28944062/coupling-spectral-analysis-and-hidden-markov-models-for-the-segmentation-of-behavioural-patterns
#8
Karine Heerah, Mathieu Woillez, Ronan Fablet, François Garren, Stéphane Martin, Hélène De Pontual
BACKGROUND: Movement pattern variations are reflective of behavioural switches, likely associated with different life history traits in response to the animals' abiotic and biotic environment. Detecting these can provide rich information on the underlying processes driving animal movement patterns. However, extracting these signals from movement time series, requires tools that objectively extract, describe and quantify these behaviours. The inference of behavioural modes from movement patterns has been mainly addressed through hidden Markov models...
2017: Movement Ecology
https://www.readbyqxmd.com/read/28869811/hidden-markov-model-analysis-reveals-the-advantage-of-analytic-eye-movement-patterns-in-face-recognition-across-cultures
#9
Tim Chuk, Kate Crookes, William G Hayward, Antoni B Chan, Janet H Hsiao
It remains controversial whether culture modulates eye movement behavior in face recognition. Inconsistent results have been reported regarding whether cultural differences in eye movement patterns exist, whether these differences affect recognition performance, and whether participants use similar eye movement patterns when viewing faces from different ethnicities. These inconsistencies may be due to substantial individual differences in eye movement patterns within a cultural group. Here we addressed this issue by conducting individual-level eye movement data analysis using hidden Markov models (HMMs)...
December 2017: Cognition
https://www.readbyqxmd.com/read/28841506/parkinsonian-rest-tremor-can-be-detected-accurately-based-on-neuronal-oscillations-recorded-from-the-subthalamic-nucleus
#10
J Hirschmann, J M Schoffelen, A Schnitzler, M A J van Gerven
OBJECTIVE: To investigate the possibility of tremor detection based on deep brain activity. METHODS: We re-analyzed recordings of local field potentials (LFPs) from the subthalamic nucleus in 10 PD patients (12 body sides) with spontaneously fluctuating rest tremor. Power in several frequency bands was estimated and used as input to Hidden Markov Models (HMMs) which classified short data segments as either tremor-free rest or rest tremor. HMMs were compared to direct threshold application to individual power features...
October 2017: Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology
https://www.readbyqxmd.com/read/28720084/sequence-based-identification-of-inositol-monophosphatase-like-histidinol-phosphate-phosphatases-hisn-in-corynebacterium-glutamicum-actinobacteria-and-beyond
#11
Robert Kasimir Kulis-Horn, Christian Rückert, Jörn Kalinowski, Marcus Persicke
BACKGROUND: The eighth step of L-histidine biosynthesis is carried out by an enzyme called histidinol-phosphate phosphatase (HolPase). Three unrelated HolPase families are known so far. Two of them are well studied: HAD-type HolPases known from Gammaproteobacteria like Escherichia coli or Salmonella enterica and PHP-type HolPases known from yeast and Firmicutes like Bacillus subtilis. However, the third family of HolPases, the inositol monophosphatase (IMPase)-like HolPases, present in Actinobacteria like Corynebacterium glutamicum (HisN) and plants, are poorly characterized...
July 18, 2017: BMC Microbiology
https://www.readbyqxmd.com/read/28682786/application-of-computational-methods-in-planaria-research-a-current-update
#12
Shyamasree Ghosh
Planaria is a member of the Phylum Platyhelminthes including flatworms. Planarians possess the unique ability of regeneration from adult stem cells or neoblasts and finds importance as a model organism for regeneration and developmental studies. Although research is being actively carried out globally through conventional methods to understand the process of regeneration from neoblasts, biology of development, neurobiology and immunology of Planaria, there are many thought provoking questions related to stem cell plasticity, and uniqueness of regenerative potential in Planarians amongst other members of Phylum Platyhelminthes...
July 6, 2017: Journal of Integrative Bioinformatics
https://www.readbyqxmd.com/read/28639636/hyperbolic-metamaterials-for-dispersion-assisted-directional-light-emission
#13
Lorenzo Ferrari, Joseph Stephen Thomas Smalley, Yeshaiahu Fainman, Zhaowei Liu
A novel method is presented to outcouple high spatial frequency (large-k) waves from hyperbolic metamaterials (HMMs) without the use of a grating. This approach relies exclusively on dispersion engineering, and enables preferential power extraction from the top or from the side of a HMM. Multilayer (ML) HMMs are shown to be better suited for lateral outcoupling, while nanowire HMMs are the most convenient choice for top outcoupling. A 6-fold increase in laterally extracted power is predicted for a dipole-HMM system with a Ag/Si ML operating at λ = 530 nm, when metallic filling ratio is changed from an unoptimized to the optimized one...
July 6, 2017: Nanoscale
https://www.readbyqxmd.com/read/28606054/variable-order-sequence-modeling-improves-bacterial-strain-discrimination-for-ion-torrent-dna-reads
#14
Thomas M Poulsen, Martin Frith
BACKGROUND: Genome sequencing provides a powerful tool for pathogen detection and can help resolve outbreaks that pose public safety and health risks. Mapping of DNA reads to genomes plays a fundamental role in this approach, where accurate alignment and classification of sequencing data is crucial. Standard mapping methods crudely treat bases as independent from their neighbors. Accuracy might be improved by using higher order paired hidden Markov models (HMMs), which model neighbor effects, but introduce design and implementation issues that have typically made them impractical for read mapping applications...
June 12, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28538149/icon-an-adaptation-of-infinite-hmms-for-time-traces-with-drift
#15
Ioannis Sgouralis, Steve Pressé
Bayesian nonparametric methods have recently transformed emerging areas within data science. One such promising method, the infinite hidden Markov model (iHMM), generalizes the HMM that itself has become a workhorse in single molecule data analysis. The iHMM goes beyond the HMM by self-consistently learning all parameters learned by the HMM in addition to learning the number of states without recourse to any model selection steps. Despite its generality, simple features (such as drift), common to single molecule time traces, result in an overinterpretation of drift and the introduction of artifact states...
May 23, 2017: Biophysical Journal
https://www.readbyqxmd.com/read/28538142/an-introduction-to-infinite-hmms-for-single-molecule-data-analysis
#16
REVIEW
Ioannis Sgouralis, Steve Pressé
The hidden Markov model (HMM) has been a workhorse of single-molecule data analysis and is now commonly used as a stand-alone tool in time series analysis or in conjunction with other analysis methods such as tracking. Here, we provide a conceptual introduction to an important generalization of the HMM, which is poised to have a deep impact across the field of biophysics: the infinite HMM (iHMM). As a modeling tool, iHMMs can analyze sequential data without a priori setting a specific number of states as required for the traditional (finite) HMM...
May 23, 2017: Biophysical Journal
https://www.readbyqxmd.com/read/28532384/in-silico-approach-to-designing-rational-metagenomic-libraries-for-functional-studies
#17
Anna Kusnezowa, Lars I Leichert
BACKGROUND: With the development of Next Generation Sequencing technologies, the number of predicted proteins from entire (meta-) genomes has risen exponentially. While for some of these sequences protein functions can be inferred from homology, an experimental characterization is still a requirement for the determination of protein function. However, functional characterization of proteins cannot keep pace with our capabilities to generate more and more sequence data. RESULTS: Here, we present an approach to reduce the number of proteins from entire (meta-) genomes to a reasonably small number for further experimental characterization without loss of important information...
May 22, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28494756/solving-the-master-equation-for-indels
#18
EDITORIAL
Ian H Holmes
BACKGROUND: Despite the long-anticipated possibility of putting sequence alignment on the same footing as statistical phylogenetics, theorists have struggled to develop time-dependent evolutionary models for indels that are as tractable as the analogous models for substitution events. MAIN TEXT: This paper discusses progress in the area of insertion-deletion models, in view of recent work by Ezawa (BMC Bioinformatics 17:304, 2016); (BMC Bioinformatics 17:397, 2016); (BMC Bioinformatics 17:457, 2016) on the calculation of time-dependent gap length distributions in pairwise alignments, and current approaches for extending these approaches from ancestor-descendant pairs to phylogenetic trees...
May 12, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28469382/performance-of-hidden-markov-models-in-recovering-the-standard-classification-of-glycoside-hydrolases
#19
Mariana Fonseca Rossi, Beatriz Mello, Carlos G Schrago
Glycoside hydrolases (GHs) are carbohydrate-active enzymes that assist the hydrolysis of glycoside bonds of complex sugars into carbohydrates. The current standard GH family classification is available in the CAZy database, which is based on the similarities of amino acid sequences and curated semi-automatically. However, with the exponential increase in data availability from genome sequences, automated classification methods are required for the fast annotation of coding sequences. Currently, the dbCAN database offers automatic annotations of signature domains from CAZy-defined classifications using a statistical approach, the hidden Markov models (HMMs)...
2017: Evolutionary Bioinformatics Online
https://www.readbyqxmd.com/read/28466793/seqping-gene-prediction-pipeline-for-plant-genomes-using-self-training-gene-models-and-transcriptomic-data
#20
Kuang-Lim Chan, Rozana Rosli, Tatiana V Tatarinova, Michael Hogan, Mohd Firdaus-Raih, Eng-Ti Leslie Low
BACKGROUND: 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...
January 27, 2017: BMC Bioinformatics
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