Markov Model Approach Research Articles

PF514 LOSS OF 18Q22.1‐Q23 IS SIGNIFICANTLY ASSOCIATED WITH GCB‐DLBCL AND FAILURE TO ACHIEVE EFS24

Background:Recent large next generation sequencing studies have redefined the genomic landscape of diffuse large B‐cell lymphoma (DLBCL). While many of the more common genetic variants, primarily mutations, have been biologically characterized, little is known about newly emerging genetic aberrations. In particular, somatic copy number alterations remain poorly studied due to the large genomic regions they cover and the number of genes involved. This, in combination with lack of remission or early relapse in a portion of DLBCL, opens new opportunities to explore yet unidentified genetic contributions to lymphomagenesis.Aims:The goal of this study was to identify variants associated with failure to achieve event free survival at 24 months (EFS24) and define their biologic significance in DLBCL.Methods:Newly diagnosed DLBCL treated with standard immunochemotherapy were included in this study (n = 246). Copy number data was generated by using the molecular inversion probe OncoScan™ FFPE Assay Kit (Affymetrix, Santa Clara, CA, USA). OncoScan OSCHP files were analyzed using Nexus Copy Number 9.0 software (Biodiscovery, El Segundo, USA). Data interpretation and copy number calling was done using the human reference genome GRCh37/hg19. Files were analyzed using the Nexus FASST2‐Segmentation algorithm, which is based on a Hidden Markov Model approach for calling genetic events. For functional analysis, SOCS6 was overexpressed and studied in GCB‐DLBCL cell lines.Results:Upon review of genomic copy number data from DLBCL cases (n = 246), loss of 18q22.1‐q23 was a significant event in our cohort (p = 0.018), is found in 11% (n = 27) of DLBCL, and is enriched in germinal center DLBCL (GCB‐DLBCL) patients (18/107, 17%, p < 0.05) compared to activated B‐cell (ABC)/non‐GCB DLBCL patients (1/93, 1%). Furthermore, GCB‐DLBCL patients with a loss of 18q22.1‐q23 were significantly more likely to fail EFS24 (50%, p < 0.05) compared to GCB‐DLBCL patients without the loss (29%). Genes located at 18q22.1‐q23 include, TMX3, DOK6 and SOCS6. SOCS family proteins have been shown to play an important role in tumor biology, although little is known about SOCS6. SOCS6 gene expression is downregulated in a variety of solid tumors and is hypermethylated in some hematological malignancies. RNASeq data from DLBCL suggest variable expression across tumors. Using a panel of GCB cell lines we found that SOCS6 expression was low to negative, and publically available cell line data suggests this region is hypermethylated. Expression of SOCS6 was higher in a panel of mantle cell lymphoma and ABC‐DLBCL cell lines and this again correlated with their cell line methylation data, where no hypermethylation was found in those cell lines. Together, this data suggests that downregulation of SOCS6 through genomic loss or hypermethylation may have biologic importance. To explore the role of SOCS6, we restored expression in GCB cell lines and immuno‐fluorescence staining suggest localization to the nucleus and cytoplasm, as previously reported in other cell types. Studies to explore the role of SOCS6 in cytokine signaling and STAT family protein regulation are ongoing.Summary/Conclusion:Using genome copy number data from 246 DLBCL, we have found that loss of 18q22.1‐q23 (p = 0.018) is associated with GCB‐DLBCL and failure to achieve EFS24. Furthermore, we have identified that SOCS6 may have clinical and biologic significance in GCB‐DLBCL.

A Systematic Review of Model-Based Economic Evaluations of Treatments for Venous Leg Ulcers.

ObjectiveThe aim of this review was to identify, and assess the quality of, published model-based economic evaluations relating to treatments for patients with venous leg ulcers to help inform future decision-analytic models in this clinical area.MethodsA systematic literature search was performed on six electronic databases, from database inception until 21 May 2018. Search results were screened against predefined criteria by two independent reviewers. Data was then extracted from the included studies using a standardised form, whilst the decision-analytic model-specific Philips Checklist was used to assess quality and to inform model critique.ResultsA total of 23 models were identified, 12 studies used a Markov modelling approach, five used decision trees and six studies did not detail the model type. Studies were predominantly from the National Health Service (NHS)/payer perspective, with only two taking a societal perspective. Interventions were wide ranging, but dressing technologies (11/23) were most common. The intervention studied was found to be dominant in 22/23 studies. The reporting quality of papers was mostly low, with evidence behind model structures, time horizons and data selection consistently underreported across the included papers.ConclusionsThis review has identified a sizeable literature of model-based economic evaluations, evaluating treatments for venous leg ulcers. However, the methods used to conduct such studies were generally poorly reported. In particular, the reporting of evidence surrounding the model structure, justification of the time horizon used and the rationale for selecting data inputs should be focused on in any future models developed.Electronic supplementary materialThe online version of this article (10.1007/s41669-019-0148-x) contains supplementary material, which is available to authorized users.

Characterization of an OrtT-like toxin of Salmonella enterica serovar Houten.

The Escherichia coli GhoT/GhoS system is a type V toxin-antitoxin system in which the antitoxin GhoS cleaves the GhoT mRNA, controlling its translation. GhoT is a small hydrophobic protein that damages bacterial membranes. OrtT is a GhoT-like toxin, but it apparently lacks a corresponding antitoxin and serves a different physiologic role. Using a profile hidden Markov model approach, a Salmonella enterica serovar Houten genome was screened to obtain homologs of GhoT/OrtT. We only found one protein (referred to here as OrtT-Sal) that shared more sequence identity with OrtT than GhoT. The chromosomal region around the coding sequence of OrtT-Sal suggests that it is an orphan toxin and can be under RpoH activation. To study OrtT-Sal, we chemically synthesized and expressed in E. coli the whole toxin and its N- and C-terminal regions (OrtT-Sal1-29 and OrtT-Sal29-57, respectively). Our findings have shown that the overproduction of the polypeptides resulted in severe growth inhibition and cell lysis. Using circular dichroism, we found that OrtT-Sal, OrtT-Sal1-29, and OrtT-Sal29-57 form an alpha-helical structure in the presence of SDS micelles or TFE. Finally, using carboxyfluorescein-loaded lipid vesicles, we determined that the polypeptides damage lipid membrane directly.

Deconstructing progression of amyotrophic lateral sclerosis in stages: a Markov modeling approach. (P1.4-003)

Deconstructing progression of amyotrophic lateral sclerosis in stages: a Markov modeling approach. (P1.4-003)

Identifying Critical State of Complex Diseases by Single-Sample-Based Hidden Markov Model.

The progression of complex diseases is generally divided as a normal state, a pre-disease state or tipping point, and a disease state. Developing individual-specific method that can identify the pre-disease state just before a catastrophic deterioration, is critical for patients with complex diseases. However, with only a case sample, it is challenging to detect a pre-disease state which has little significant differences comparing with a normal state in terms of phenotypes and gene expressions. In this study, by regarding the tipping point as the end point of a stationary Markov process, we proposed a single-sample-based hidden Markov model (HMM) approach to explore the dynamical differences between a normal and a pre-disease states, and thus can signal the upcoming critical transition immediately after a pre-disease state. Using this method, we identified the pre-disease state or tipping point in a numerical simulation and two real datasets including stomach adenocarcinoma and influenza infection, which demonstrate the effectiveness of the method.

Sequencing-Based Bin Map Construction of a Tomato Mapping Population, Facilitating High-Resolution Quantitative Trait Loci Detection.

Genotyping-by-sequencing (GBS) was employed to construct a highly saturated genetic linkage map of a tomato ( L.) recombinant inbred line (RIL) population, derived from a cross between cultivar NC EBR-1 and the wild tomato L. accession LA2093. A pipeline was developed to convert single nucleotide polymorphism (SNP) data into genomic bins, which could be used for fine mapping of quantitative trait loci (QTL) and identification of candidate genes. The pipeline, implemented in a python script named SNPbinner, adopts a hidden Markov model approach for calculation of recombination breakpoints followed by genomic bins construction. The total length of the newly developed high-resolution genetic map was 1.2-fold larger than previously estimated based on restriction fragment length polymorphism (RFLP) and polymerase chain reaction (PCR)-based markers. The map was used to verify and refine QTL previously identified for two fruit quality traits in the RIL population, fruit weight (FW) and fruit lycopene content (LYC). Two well-described FW QTL ( and ) were localized precisely at their known underlying causative genes, and the QTL intervals were decreased by two- to tenfold. A major QTL for LYC content () was verified at high resolution and its underlying causative gene was determined to be ζ (). The RIL population, the high resolution genetic map, and the easy-to-use genotyping pipeline, SNPbinner, are made publicly available.

Accuracy analysis of a single-fault Markov model for FADEC system

Time-limited dispatching (TLD) analysis of the full authority digital engine control (FADEC) systems is an important part of the aircraft system safety analysis and a necessary task for the certification of commercial aircraft and aeroengines. In the time limited dispatch guidance document ARP5107B, a single-fault Markov model (MM) approach is proposed for TLD analysis. However, ARP5107B also requires that the loss of thrust control (LOTC) rate error calculated by applying the single-fault MM must be less than 5% when performing airworthiness certification. Firstly, the sources of accuracy errors in three kinds of MM are analyzed and specified through a case study of the general FADEC system, and secondly a two-fault MM considering maintenance policy is established through analyzing and calculating the expected repair time when two related faults happen. Finally, a specific FADEC system is given to study on the influence factors of accuracy error in the single-fault MM, and the results show that the accuracy error of the single-fault MM decreases with the increase of short or long prescribed dispatch time, and the range values of short time (ST) and long time (LT) are determined to satisfy the requirement of accuracy error within 5%.

Uncovering hidden states in African lion movement data using hidden Markov models

Context Direct observations of animals are the most reliable way to define their behavioural characteristics; however, to obtain these observations is costly and often logistically challenging. GPS tracking allows finer-scale interpretation of animal responses by measuring movement patterns; however, the true behaviour of the animal during the period of observation is seldom known. Aims The aim of our research was to draw behavioural inferences for a lioness with a hidden Markov model and to validate the predicted latent-state sequence with field observations of the lion pride. Methods We used hidden Markov models to model the movement of a lioness in the Kruger National Park, South Africa. A three-state log-normal model was selected as the most suitable model. The model outputs are related to collected data by using an observational model, such as, for example, a distribution for the average movement rate and/or direction of movement that depends on the underlying model states that are taken to represent behavioural states of the animal. These inferred behavioural states are validated against direct observation of the pride’s behaviour. Key results Average movement rate provided a useful alternative for the application of hidden Markov models to irregularly spaced GPS locations. The movement model predicted resting as the dominant activity throughout the day, with a peak in the afternoon. The local-movement state occurred consistently throughout the day, with a decreased proportion during the afternoon, when more resting takes place, and an increase towards the early evening. The relocating state had three peaks, namely, during mid-morning, early evening and about midnight. Because of the differences in timing of the direct observations and the GPS locations, we had to compare point observations of the true behaviour with an interval prediction of the modelled behavioural state. In 75% of the cases, the model-predicted behaviour and the field-observed behaviour overlapped. Conclusions Our data suggest that the hidden Markov modelling approach is successful at predicting a realistic behaviour of lions on the basis of the GPS location coordinates and the average movement rate between locations. The present study provided a unique opportunity to uncover the hidden states and compare the true behaviour with the inferred behaviour from the predicted state sequence. Implications Our results illustrated the potential of using hidden Markov models with movement rate as an input to understand carnivore behavioural patterns that could inform conservation management practices.

Analyzing the Dynamics of Single TBP-DNA-NC2 Complexes Using Hidden Markov Models

Single-pair Förster resonance energy transfer (spFRET) has become an important tool for investigating conformational dynamics in biological systems. To extract dynamic information from the spFRET traces measured with total internal reflection fluorescence microscopy, we extended the hidden Markov model (HMM) approach. In our extended HMM analysis, we incorporated the photon-shot noise from camera-based systems into the HMM. Thus, the variance in Förster resonance energy transfer (FRET) efficiency of the various states, which is typically a fitted parameter, is explicitly included in the analysis estimated from the number of detected photons. It is also possible to include an additional broadening of the FRET state, which would then only reflect the inherent flexibility of the dynamic biological systems. This approach is useful when comparing the dynamics of individual molecules for which the total intensities vary significantly. We used spFRET with the extended HMM analysis to investigate the dynamics of TATA-box-binding protein (TBP) on promoter DNA in the presence of negative cofactor 2 (NC2). We compared the dynamics of two promoters as well as DNAs of different length and labeling location. For the adenovirus major late promoter, four FRET states were observed; three states correspond to different conformations of the DNA in the TBP-DNA-NC2 complex and a four-state model in which the complex has shifted along the DNA. The HMM analysis revealed that the states are connected via a linear, four-well model. For the H2B promoter, more complex dynamics were observed. By clustering the FRET states detected with the HMM analysis, we could compare the general dynamics observed for the two promoter sequences. We observed that the dynamics from a stretched DNA conformation to a bent conformation for the two promoters were similar, whereas the bent conformation of the TBP-DNA-NC2 complex for the H2B promoter is approximately three times more stable than for the adenovirus major late promoter.

Individuals with insomnia misrecognize angry faces as fearful faces while missing the eyes: an eye-tracking study.

Individuals with insomnia have been found to have disturbed perception of facial expressions. Through eye movement examinations, here we test the hypothesis that this effect is due to impaired visual attention functions for retrieving diagnostic features in facial expression judgments. Twenty-three individuals with insomnia symptoms and 23 controls without insomnia completed a task to categorize happy, sad, fearful, and angry facial expressions. The participants with insomnia were less accurate in recognizing angry faces and misidentified them as fearful faces more often than the controls. A hidden Markov modeling approach for eye movement data analysis revealed that when viewing facial expressions, more individuals with insomnia adopted a nose-mouth eye movement pattern focusing on the vertical face midline while more controls adopted an eyes-mouth pattern preferentially attending to lateral features, particularly the two eyes. As previous studies found that the primary diagnostic feature for recognizing angry faces is the eyes while the diagnostic features for other facial expressions involve the mouth region, missing the eye region may contribute to specific difficulties in recognizing angry facial expressions, consistent with our behavioral finding in participants with insomnia symptoms. Taken together, the findings suggest that impaired information selection through visual attention control may be related to the compromised emotion perception in individuals with insomnia.

HsegHMM: hidden Markov model-based allele-specific copy number alteration analysis accounting for hypersegmentation

BackgroundSomatic copy number alternation (SCNA) is a common feature of the cancer genome and is associated with cancer etiology and prognosis. The allele-specific SCNA analysis of a tumor sample aims to identify the allele-specific copy numbers of both alleles, adjusting for the ploidy and the tumor purity. Next generation sequencing platforms produce abundant read counts at the base-pair resolution across the exome or whole genome which is susceptible to hypersegmentation, a phenomenon where numerous regions with very short length are falsely identified as SCNA.ResultsWe propose hsegHMM, a hidden Markov model approach that accounts for hypersegmentation for allele-specific SCNA analysis. hsegHMM provides statistical inference of copy number profiles by using an efficient E-M algorithm procedure. Through simulation and application studies, we found that hsegHMM handles hypersegmentation effectively with a t-distribution as a part of the emission probability distribution structure and a carefully defined state space. We also compared hsegHMM with FACETS which is a current method for allele-specific SCNA analysis. For the application, we use a renal cell carcinoma sample from The Cancer Genome Atlas (TCGA) study.ConclusionsWe demonstrate the robustness of hsegHMM to hypersegmentation. Furthermore, hsegHMM provides the quantification of uncertainty in identifying allele-specific SCNAs over the entire chromosomes. hsegHMM performs better than FACETS when read depth (coverage) is uneven across the genome.

Hidden state models improve state-dependent diversification approaches, including biogeographical models.

The state-dependent speciation and extinction (SSE) models have recently been criticized due to their high rates of "false positive" results. Many researchers have advocated avoiding SSE models in favor of other "nonparametric" or "semiparametric" approaches. The hidden Markov modeling (HMM) approach provides a partial solution to the issues of model adequacy detected with SSE models. The inclusion of "hidden states" can account for rate heterogeneity observed in empirical phylogenies and allows for reliable detection of state-dependent diversification or diversification shifts independent of the trait of interest. However, the adoption of HMM has been hampered by the interpretational challenges of what exactly a "hidden state" represents, which we clarify herein. We show that HMMs in combination with a model-averaging approach naturally account for hidden traits when examining the meaningful impact of a suspected "driver" of diversification. We also extend the HMM to the geographic state-dependent speciation and extinction (GeoSSE) model. We test the efficacy of our "GeoHiSSE" extension with both simulations and an empirical dataset. On the whole, we show that hidden states are a general framework that can distinguish heterogeneous effects of diversification attributed to a focal character.

Hidden Markov model based drone sound recognition using MFCC technique in practical noisy environments

The development of drones has captured the attention of hobbyists and investors alike; drones now have greater commercial and military applications owing to their relatively small size and ability to fly without an on-board pilot. However, certain drone applications may pose serious threats to public safety. The most important problem to be addressed is the recognition of drones in security-sensitive areas. This paper presents an approach to recognize drones through sounds emitted by their propellers using Mel frequency cepstral coefficients (MFCCs) technique for feature extraction and the hidden Markov model (HMM) approach for classification. In the feature extraction stage, two schemes for feature vectors (one using twenty-four MFCCs and the other using the proposed thirty-six MFCCs) are applied, where additional dynamic information of the features is added in the latter. The classifier based on HMMs is then trained using the extracted features according to different training datasets in order to validate the effect of the number of sound types in each cluster on the recognition rate performance. We perform experiments for drone sound recognition utilizing various training datasets for the purpose of classifier optimization, as well as for the two MFCC schemes that are applied in each trial, using the same training datasets for a fair comparison. The experimental results finally validate the feasibility and effectiveness of our proposed methods with relatively high recognition rates, even in noisy environments.

Evaluation of session-based recommendation algorithms

Recommender systems help users find relevant items of interest, for example on e-commerce or media streaming sites. Most academic research is concerned with approaches that personalize the recommendations according to long-term user profiles. In many real-world applications, however, such long-term profiles often do not exist and recommendations therefore have to be made solely based on the observed behavior of a user during an ongoing session. Given the high practical relevance of the problem, an increased interest in this problem can be observed in recent years, leading to a number of proposals for session-based recommendation algorithms that typically aim to predict the user's immediate next actions. In this work, we present the results of an in-depth performance comparison of a number of such algorithms, using a variety of datasets and evaluation measures. Our comparison includes the most recent approaches based on recurrent neural networks like GRU4REC, factorized Markov model approaches such as FISM or FOSSIL, as well as simpler methods based, e.g., on nearest neighbor schemes. Our experiments reveal that algorithms of this latter class, despite their sometimes almost trivial nature, often perform equally well or significantly better than today's more complex approaches based on deep neural networks. Our results therefore suggest that there is substantial room for improvement regarding the development of more sophisticated session-based recommendation algorithms.

User-Centered Predictive Model for Improving Cultural Heritage Augmented Reality Applications: An HMM-Based Approach for Eye-Tracking Data

Today, museum visits are perceived as an opportunity for individuals to explore and make up their own minds. The increasing technical capabilities of Augmented Reality (AR) technology have raised audience expectations, advancing the use of mobile AR in cultural heritage (CH) settings. Hence, there is the need to define a criteria, based on users’ preference, able to drive developers and insiders toward a more conscious development of AR-based applications. Starting from previous research (performed to define a protocol for understanding the visual behaviour of subjects looking at paintings), this paper introduces a truly predictive model of the museum visitor’s visual behaviour, measured by an eye tracker. A Hidden Markov Model (HMM) approach is presented, able to predict users’ attention in front of a painting. Furthermore, this research compares users’ behaviour between adults and children, expanding the results to different kind of users, thus providing a reliable approach to eye trajectories. Tests have been conducted defining areas of interest (AOI) and observing the most visited ones, attempting the prediction of subsequent transitions between AOIs. The results demonstrate the effectiveness and suitability of our approach, with performance evaluation values that exceed 90%.

Deconstructing progression of amyotrophic lateral sclerosis in stages: a Markov modeling approach

Objectives: Propose an empirical amyotrophic lateral sclerosis (ALS) staging approach called Fine’til 9 (FT9) based on how many of the patient’s ALS functional rating scale (ALSFRS-R) subscores are 9 or less (of normal 12). Gain insights into progression of ALS by applying Markov models to ALS stages by multiple systems (King’s, Milan–Torino system (MITOS) and FT9).Methods: Patients from the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) dataset were staged using ALSFRS-R responses. Risks of progression through stages and death were estimated, as were effects of prognostic variables on these risks.Results: A total of 29,947 time points in 3199 patients from the PRO-ACT dataset were assigned stages. Although the three systems were moderately correlated, MITOS stages were heavily skewed toward advanced disease, whereas King’s and FT9 stages were more balanced. Non-sequential progression was observed with King’s system. Markov models adequately described transitions from stage to stage in the first year of observation, but underestimated risks beyond that point. Regardless of staging method, initial rate of ALSFRS-R decline had a powerful effect on rate of progression through sequential stages, whereas age predominantly influenced stage-specific mortality.Conclusion: King’s and FT9 are more sensitive to observed progression of disease in clinical trials than MITOS. FT9 can partition the course similar to King’s, and may have advantages of sequential progression and easy applicability to retrospective data. Markov transition intensity estimates may be of value for counseling, health economic studies, and research design. In particular, this framework permits estimation of multidimensional effects of variables (including treatment) on outcome.

Unveiling endogeneity and temporal dependence between tourism revenues/expenditures and macroeconomic variables in Brazil: A stochastic hidden Markov model approach

Thus far, a comprehensive analysis on the feedback processes that may exist between macroeconomic variables and tourism activity in Brazil is still missing. This article fills this literature gap by analyzing the endogenous and temporally dependent pattern between Brazilian monthly tourism revenue/expenditures and macroeconomic variables over 20 years. A novel stochastic hidden Markov model approach reveals the feedback processes that exist between them. While tourism revenues are autocorrelated and impacted by gross domestic product (GDP) growth, tourism expenditures are detached from any macroeconomic variable, but are rather directly dependent on tourism revenues past performance, which also exert an impact on exchange rates and GDP growth, thus indirectly benefiting tourism expenditures abroad. Policy implications in terms of a specific tourism exchange rate for Brazil are derived in order to sustain tourism expenditures apart from tourism revenue flows.

Exploration of Plasmodium vivax transmission dynamics and recurrent infections in the Peruvian Amazon using whole genome sequencing

BackgroundPlasmodium vivax poses a significant challenge to malaria elimination due to its ability to cause relapsed infections from reactivation of dormant liver parasites called hypnozoites. We analyzed 69 P. vivax whole genome sequences obtained from subjects residing in three different villages along the Peruvian Amazon. This included 23 paired P. vivax samples from subjects who experienced recurrent P. vivax parasitemia following observed treatment with chloroquine and primaquine.MethodsGenomic DNA was extracted from whole blood samples collected from subjects. P. vivax DNA was enriched using selective whole genome amplification and whole genome sequencing. We used single nucleotide polymorphisms (SNPs) from the core P. vivax genome to determine characteristics of the parasite population using discriminant analysis of principal components, maximum likelihood estimation of individual ancestries, and phylogenetic analysis. We estimated the relatedness of the paired samples by calculating the number of segregating sites and using a hidden Markov model approach to estimate identity by descent.ResultsWe present a comprehensive dataset of population genetics of Plasmodium vivax in the Peruvian Amazonian. We define the parasite population structure in this region and demonstrate a novel method for distinguishing homologous relapses from reinfections or heterologous relapses with improved accuracy. The parasite population in this area was quite diverse with an estimated five subpopulations and evidence of a highly heterogeneous ancestry of some of the isolates, similar to previous analyses of P. vivax in this region. Pairwise comparison of recurrent infections determined that there were 12 homologous relapses and 3 likely heterologous relapses with highly related parasites. To the best of our knowledge, this is the first large-scale study to evaluate recurrent P. vivax infections using whole genome sequencing.ConclusionsWhole genome sequencing is a high-resolution tool that can identify P. vivax homologous relapses with increased sensitivity, while also providing data about drug resistance and parasite population genetics. This information is important for evaluating the efficacy of known and novel antirelapse medications in endemic areas and thus advancing the campaign to eliminate malaria.

Limiting relaxation times from Markov state models.

Markov state models (MSMs) are more and more widely used in the analysis of molecular simulations to incorporate multiple trajectories together and obtain more accurate time scale information of the slowest processes in the system. Typically, however, multiple lagtimes are used and analyzed as input parameters, yet convergence with respect to the choice of lagtime is not always possible. Here, we present a simple method for calculating the slowest relaxation time (RT) of the system in the limit of very long lagtimes. Our approach relies on the fact that the second eigenvector's autocorrelation function of the propagator will be approximately single exponential at long lagtimes. This allows us to obtain a simple equation for the behavior of the MSM's relaxation time as a function of the lagtime with only two free parameters, one of these being the RT of the system. We demonstrate that the second parameter is a useful indicator of how Markovian a selected variable is for building the MSM. Fitting this function to data gives a limiting value for the optimal variational RT. Testing this on analytic and molecular dynamics data for Ala5 and umbrella sampling-biased ion channel simulations shows that the function accurately describes the behavior of the RT and furthermore that this RT can improve noticeably the value calculated at the longest accessible lagtime. We compare our RT limit to the hidden Markov model (HMM) approach that typically finds RTs of comparable values. However, HMMs cannot be used in conjunction with biased simulation data, requiring more complex algorithms to construct than MSMs, and the derived RTs are not variational, leading to ambiguity in the choice of lagtime at which to build the HMM.

GFCC based discriminatively trained noise robust continuous ASR system for Hindi language

A statistically designed Automatic Speech Recognition (ASR) system extracts features from speech signals using feature extraction methods, links the extracted features with the expected phonetics of the hypothesis using acoustic models, uses language model to add prior information about the structure of the target language. For many years, Mel-frequency Cepstral coefficients (MFCC), n-gram, and Hidden Markov Model (HMM) approaches have been used predominantly for feature extraction, language modeling and acoustic modeling, respectively. However, performance degradation of MFCC in noisy conditions and inaccuracy of HMMs while handling large vocabularies have made researchers to propose more efficient methods. The proposed work uses noise robust method Gammatone Frequency Cepstral Coefficients(GFCC) for feature extraction, trigram language modeling, and HMM-Gaussian mixture model (GMM) based acoustic modeling to implement a continuous Hindi language ASR system. Also, it applies Differential Evolution (DE) technique to refine the GFCC features and discriminative techniques to enhance performance of the acoustic model. The performance of the implemented system has been evaluated by using different feature extraction methods, variants of n-gram language modeling techniques and different discriminative techniques in clean as well as noisy conditions. Initially, the results reveal that DE optimized GFCC with HMM-Gaussian Mixture Model (GMM) acoustic modeling performs better than MFCC, PLP and MF-PLP feature extraction methods. Secondly, the experimental results show that the Minimum Phone Error (MPE) outperforms Maximum Mutual Information (MMI) and Maximum Likelihood Estimation (MLE) and trigram based language modeling gives more accurate results than unigram and bigram language modeling. Finally, it has been concluded that the continuous Hindi language ASR system implemented using DE refined GFFC feature extraction method with MPE discriminative training technique and trigram based language modeling gives better accuracy in clean as-well-as noisy environments.