Hidden Markov Model Analysis Research Articles

A sticky Poisson Hidden Markov Model for spike data.

Fitting a hidden Markov Model (HMM) to neural data is a powerful method to segment a spatiotemporal stream of neural activity into sequences of discrete hidden states. Application of HMM has allowed to uncover hidden states and signatures of neural dynamics that seem relevant for sensory and cognitive processes. This has been accomplished especially in datasets comprising ensembles of simultaneously recorded cortical spike trains. However, the HMM analysis of spike data is involved and requires a careful handling of model selection. Two main issues are: (i) the cross-validated likelihood function typically increases with the number of hidden states; (ii) decoding the data with an HMM can lead to very rapid state switching due to fast oscillations in state probabilities. The first problem is related to the phenomenon of over-segmentation and leads to overfitting. The second problem is at odds with the empirical fact that hidden states in cortex tend to last from hundred of milliseconds to seconds. Here, we show that we can alleviate both problems by regularizing a Poisson-HMM during training so as to enforce large self-transition probabilities. We call this algorithm the 'sticky Poisson-HMM' (sPHMM). When used to-gether with the Bayesian Information Criterion for model selection, the sPHMM successfully eliminates rapid state switching, outperforming an alternative strategy based on an HMM with a large prior on the self-transition probabilities. The sPHMM also captures the ground truth in surrogate datasets built to resemble the statistical properties of the experimental data.

Dynamic network analysis of electrophysiological task data

Abstract An important approach for studying the human brain is to use functional neuroimaging combined with a task. In electrophysiological data, this often involves a time-frequency analysis, in which recorded brain activity is time-frequency transformed and epoched around task events of interest, followed by trial-averaging of the power. While this simple approach can reveal fast oscillatory dynamics, the brain regions are analysed one at a time. This causes difficulties for interpretation and a debilitating number of multiple comparisons. In addition, it is now recognised that the brain responds to tasks through the coordinated activity of networks of brain areas. As such, techniques that take a whole-brain network perspective are needed. Here, we show how the oscillatory task responses from conventional time-frequency approaches can be represented more parsimoniously at the network level using two state-of-the-art methods: the HMM (Hidden Markov Model) and DyNeMo (Dynamic Network Modes). Both methods reveal frequency-resolved networks of oscillatory activity with millisecond resolution. Comparing DyNeMo, HMM, and traditional oscillatory response analysis, we show DyNeMo can identify task activations/deactivations that the other approaches fail to detect. DyNeMo offers a powerful new method for analysing task data from the perspective of dynamic brain networks.

Constrained hidden Markov models reveal further Hsp90 protein states

Time series of conformational dynamics in proteins are usually evaluated with hidden Markov models (HMMs). This approach works well if the number of states and their connectivity is known. However, for the multi-domain protein Hsp90, a standard HMM analysis with optimization of the BIC (Bayesian information criterion) cannot explain long-lived states well. Therefore, here we employ constrained HMMs, which neglect transitions between states by including assumptions. Gradually tuning a model with justified and focused changes allows us to improve its effectiveness and the score of the BIC. This became possible by analyzing time traces with several thousand observable transitions and, therefore, superb statistics. In this scheme, we also monitor the residences in the states reconstructed by the model, aiming to find exponentially distributed dwell times. We show how introducing new states can achieve these statistics but also point out limitations, e.g. for substantial similarity of two states connected to a common neighbor. One of the states displays the lowest free energy and could be the idle open ‘waiting state’, in which Hsp90 waits for the binding of nucleotides, cochaperones, or clients.

Comparative Analysis of Hidden Markov Model and Bidirectional Long Short-Term Memory for POS Tagging in Eastern Armenian

This scientific article introduces a novel POS tagging system specifically developed for the Eastern Armenian language. The primary objective of the study is to conduct a comparative analysis of two well-established methods for part-of-speech (POS) tagging: the hidden Markov model (HMM) coupled with the Viterbi algorithm, and an artificial neural network in the form of a recurrent neural network. The study places particular emphasis on the Eastern Armenian language and employs the ArmSpeech-POS Eastern Armenian part-of-speech tagged corpus for conducting comprehensive experiments and evaluations. POS tagging is a fundamental task in natural language processing (NLP) that involves assigning grammatical tags to words in a given text. Accurate POS tagging is crucial for various NLP applications, including machine translation, information retrieval, and sentiment analysis. The Viterbi algorithm is a well-established probabilistic method that utilizes a hidden Markov model (HMM) to determine the most likely sequence of POS tags. On the other hand, RNNs, a type of deep learning model, can capture complex patterns and dependencies in sequential data. Experimental results indicate that both methods achieve reasonable accuracy in POS tagging for Eastern Armenian. However, the RNN outperforms the Viterbi algorithm, exhibiting higher accuracy rates. This can be attributed to the RNN’s ability to capture long-range dependencies and learn intricate linguistic patterns. The article concludes by discussing the implications of the study’s findings and potential areas for further research. It emphasizes the significance of accurate POS tagging for improving NLP applications in Eastern Armenian and suggests exploring advanced neural network architectures and incorporating linguistic features to enhance POS tagging performance.

Prediction of Disorientation by Accelerometric and Gait Features in Young and Older Adults Navigating in a Virtually Enriched Environment

ObjectiveTo determine whether gait and accelerometric features can predict disorientation events in young and older adults.MethodsCognitively healthy younger (18–40 years, n = 25) and older (60–85 years, n = 28) participants navigated on a treadmill through a virtual representation of the city of Rostock featured within the Gait Real-Time Analysis Interactive Lab (GRAIL) system. We conducted Bayesian Poisson regression to determine the association of navigation performance with domain-specific cognitive functions. We determined associations of gait and accelerometric features with disorientation events in real-time data using Bayesian generalized mixed effect models. The accuracy of gait and accelerometric features to predict disorientation events was determined using cross-validated support vector machines (SVM) and Hidden Markov models (HMM).ResultsBayesian analysis revealed strong evidence for the effect of gait and accelerometric features on disorientation. The evidence supported a relationship between executive functions but not visuospatial abilities and perspective taking with navigation performance. Despite these effects, the cross-validated percentage of correctly assigned instances of disorientation was only 72% in the SVM and 63% in the HMM analysis using gait and accelerometric features as predictors.ConclusionDisorientation is reflected in spatiotemporal gait features and the accelerometric signal as a potentially more easily accessible surrogate for gait features. At the same time, such measurements probably need to be enriched with other parameters to be sufficiently accurate for individual prediction of disorientation events.

Spatiotemporal soil moisture response and controlling factors along a hillslope

Soil moisture is a critical variable for evaluating water movement in hillslope hydrology. To understand its temporal response patterns via statistical distribution, time series of soil moisture spatial distributions were collected for nine months along a steep montane hillside. In this study, a novel synthesized probability density function was introduced to accurately describe the soil moisture variability in conjunction with controls. The optimal number of componential distribution model was determined using hidden Markov model (HMM). A univariate HMM captured the profile development of latent soil moisture states as well as the transitional probabilities between regression and switch patterns that were useful for categorizing soil moisture variability corresponding to hydrological processes. The deeper and lower the locations in the hillslope, the greater the complexity in soil moisture latent states, indicating a higher number of flow paths. Both seasonal- and event-based soil moisture variations were adequately characterized through the identification of latent soil moisture states. The impact of environmental factors on soil moisture was evaluated via a comparative analysis of multivariate HMM and principle component analysis, revealing that the controlling factors were related to the context of seasonal and spatial variability, as well as wetting and drying periods during rainfall event.

Hidden Markov model tracking of continuous gravitational waves from a binary neutron star with wandering spin. III. Rotational phase tracking

A hidden Markov model (HMM) solved recursively by the Viterbi algorithm can be configured to search for persistent, quasimonochromatic gravitational radiation from an isolated or accreting neutron star, whose rotational frequency is unknown and wanders stochastically. Here an existing HMM analysis pipeline is generalized to track rotational phase and frequency simultaneously, by modeling the intra-step rotational evolution according to a phase-wrapped Ornstein-Uhlenbeck process, and by calculating the emission probability using a phase-sensitive version of the Bayesian matched filter known as the $\mathcal{B}$-statistic. The generalized algorithm tracks signals from isolated and binary sources with characteristic wave strain $h_0 \geq 1.3\times 10^{-26}$ in Gaussian noise with amplitude spectral density $4\times 10^{-24}\,{\rm Hz^{-1/2}}$, for a simulated observation composed of $N_T=37$ data segments, each $T_{\rm drift}=10\,{\rm days}$ long, the typical duration of a search for the low-mass X-ray binary (LMXB) Sco X$-$1 with the Laser Interferometer Gravitational Wave Observatory (LIGO). It is equally sensitive to isolated and binary sources and $\approx 1.5$ times more sensitive than the previous pipeline. Receiver operating characteristic curves and errors in the recovered parameters are presented for a range of practical $h_0$ and $N_T$ values. The generalized algorithm successfully detects every available synthetic signal in Stage I of the Sco X$-$1 Mock Data Challenge convened by the LIGO Scientific Collaboration, recovering the frequency and orbital semimajor axis with accuracies of better than $9.5\times 10^{-7}\,{\rm Hz}$ and $1.6\times 10^{-3}\,{\rm lt\,s}$ respectively. The Viterbi solver runs in $\approx 2\times 10^3$ CPU-hr for an isolated source and $\sim 10^5$ CPU-hr for a LMXB source in a typical, broadband ($0.5$-${\rm kHz}$) search.

Time stability of the impact of institutions on economic growth and real convergence of the EU countries: implications from the hidden Markov models analysis

Research background: It is not straightforward to identify the role of institutions for the economic growth. The possible unknown or uncertain areas refer to nonlinearities, time stability, transmission channels, and institutional complementarities. The research problem tackled in this paper is the analysis of the time stability of the relationship between institutions and economic growth and real economic convergence. Purpose of the article: The article aims to verify whether the impact of the institutional environment on GDP dynamics was stable over time or diffed in various subperiods. The analysis covers the EU28 countries and the 1995?2019 period. Methods: We use regression equations with time dummies and interactions to assess the stability of the impact of institutions on economic growth. The analysis is based on the partially overlapping observations. The models are estimated with the use of Blundell and Bond?s GMM system estimator. The results are then averaged with the Bayesian Model Averaging (BMA) approach. Structural breaks are identified on the basis of the Hidden Markov Models (HMM). Findings & value added: The value added of the study is threefold. First, we use the HMM approach to find structural breaks. Second, the BMA method is applied to assess the robustness of the outcomes. Third, we show the potential of HMM in foresighting. The results of regression estimates indicate that good institution reflected in the greater scope of economic freedom and better governance lead to the higher economic growth of the EU countries. However, the impact of institutions on economic growth was not stable over time.

A Comparative Analysis of Hidden Markov Model, Hybrid Support Vector Machines, and Hybrid Artificial Neural Fuzzy Inference System in Reservoir Inflow Forecasting (Case Study: The King Fahd Dam, Saudi Arabia)

The precise prediction of the streamflow of reservoirs is of considerable importance for many activities relating to water resource management, such as reservoir operation and flood and drought control and protection. This study aimed to develop and evaluate the applicability of a hidden Markov model (HMM) and two hybrid models, i.e., the support vector machine-genetic algorithm (SVM-GA) and artificial neural fuzzy inference system-genetic algorithm (ANFIS-GA), for reservoir inflow forecasting at the King Fahd dam, Saudi Arabia. The results obtained by the HMM model were compared with those for the two hybrid models ANFIS-GA and SVM-GA, and with those for individual SVM and ANFIS models based on performance evaluation indicators and visual inspection. The results of the comparison revealed that the ANFIS-GA model and ANFIS model provided superior results for forecasting monthly inflow with satisfactory accuracy in both training (R2 = 0.924, 0.857) and testing (R2 = 0.842, 0.810) models. The performance evaluation results for the developed models showed that the GA-induced improvement in the ANFIS and SVR forecasts was matched by an approximately 25% decrease in RMSE and around a 13% increase in Nash–Sutcliffe efficiency. The promising accuracy of the proposed models demonstrates their potential for applications in monthly inflow forecasting in the present semiarid region.

Dynamic analysis on simultaneous iEEG-MEG data via hidden Markov model

BackgroundIntracranial electroencephalography (iEEG) recordings are used for clinical evaluation prior to surgical resection of the focus of epileptic seizures and also provide a window into normal brain function. A major difficulty with interpreting iEEG results at the group level is inconsistent placement of electrodes between subjects making it difficult to select contacts that correspond to the same functional areas. Recent work using time delay embedded hidden Markov model (HMM) applied to magnetoencephalography (MEG) resting data revealed a distinct set of brain states with each state engaging a specific set of cortical regions. Here we use a rare group dataset with simultaneously acquired resting iEEG and MEG to test whether there is correspondence between HMM states and iEEG power changes that would allow classifying iEEG contacts into functional clusters. MethodsSimultaneous MEG-iEEG recordings were performed at rest on 11 patients with epilepsy whose intracranial electrodes were implanted for pre-surgical evaluation. Pre-processed MEG sensor data was projected to source space. Time delay embedded HMM was then applied to MEG time series. At the same time, iEEG time series were analyzed with time-frequency decomposition to obtain spectral power changes with time. To relate MEG and iEEG results, correlations were computed between HMM probability time courses of state activation and iEEG power time course from the mid contact pair for each electrode in equally spaced frequency bins and presented as correlation spectra for the respective states and iEEG channels. Association of iEEG electrodes with HMM states based on significant correlations was compared to that based on the distance to peaks in subject-specific state topographies. ResultsFive HMM states were inferred from MEG. Two of them corresponded to the left and the right temporal activations and had a spectral signature primarily in the theta/alpha frequency band. All the electrodes had significant correlations with at least one of the states (p < 0.05 uncorrected) and for 27/50 electrodes these survived within-subject FDR correction (q < 0.05). These correlations peaked in the theta/alpha band. There was a highly significant dependence between the association of states and electrodes based on functional correlations and that based on spatial proximity (p = 5.6e−6,χ2 test for independence). Despite the potentially atypical functional anatomy and physiological abnormalities related to epilepsy, HMM model estimated from the patient group was very similar to that estimated from healthy subjects. ConclusionEpilepsy does not preclude HMM analysis of interictal data. The resulting group functional states are highly similar to those reported for healthy controls. Power changes recorded with iEEG correlate with HMM state time courses in the alpha-theta band and the presence of this correlation can be related to the spatial location of electrode contacts close to the individual peaks of the corresponding state topographies. Thus, the hypothesized relation between iEEG contacts and HMM states exists and HMM could be further explored as a method for identifying comparable iEEG channels across subjects for the purposes of group analysis.

Urea as a Protein LID Opener: Overcoming Substrate Inhibition in Adenylate Kinase by Tuning Conformational Dynamics

Enzymes are designed to accelerate vital chemical reactions by multiple orders of magnitude. Many of them harness large-scale motions of domains and subunits to promote their activity. Studying structural dynamics is hence essential in order to decipher how protein machines function. Combined with H2MM, a photon-by-photon hidden Markov model analysis, single-molecule FRET studies on the enzyme adenylate kinase (AK) recently revealed very fast domain motions, two orders of magnitude faster than the turnover of the enzyme. AK may use numerous cycles of conformational rearrangement in order to find a relative orientation of its substrates that is optimal for the chemical reaction, the interconversion of adenine nucleotides. In the present work we studied the impact of urea on the domain motions of AK, particularly those of its ATP-binding LID-domain. Although urea has been commonly used as a protein denaturant, it is reported that in minor amounts it actually enhances the catalytic activity of AK. We show that this activation is caused by an effect on substrate inhibition. High concentrations of the substrate AMP decrease the rate of LID-domain opening, disturb the balance between open and closed conformations and reduce turnover. Minor amounts of urea partially revert this effect by opening the domains and allow the enzyme to regain activity. Importantly, in absence of inhibitory concentrations of AMP, the activity of AK is not enhanced by urea and can even be reduced, although urea here again favors the open conformation This suggests a delicate balance between domain closure dynamics and substrate binding, which might not be a unique feature of AK, and is likely employed by a multitude of enzymes to regulate their activity.

A Fast and Fully Automated HMM Fitting Algorithm Enables Accurate Analysis of Biophysical Data with Numerous States

Hidden Markov model (HMM) is widely used to analyze biophysical chronological data with discrete states, such as protein/nucleotide conformational changes. Despite its usefulness, the classical HMM fitting has practical drawbacks that it requires predefinition of the number of hidden states and fine initialization of many parameters. To overcome the drawbacks, several HMM pre-analyses have been reported, but do not provide enough accuracy when data have unknown kinetics and/or low signal-to-noise ratio. Therefore, in many cases, HMM fitting needs trial-and-error manual process that can impair objectivity of the analysis. In particular, for data composed of numerous hidden states, such as stepping data of cytoskeletal motors, there has been difficulty in HMM analysis because the large number of parameters were almost unable to properly initialized. Here, by combining a statistical step finding method and Gaussian mixture model clustering, we developed a new algorithm for more objective HMM analysis. Our algorithm can execute accurate state number estimation and parameter optimization with fully automated way. Simulation analysis demonstrated that our algorithm accurately fit both fast- and slow-transition trajectories. Compared with the previous method, speed of our algorithm was 10-20 times faster for standard size data. Our algorithm also showed accurate fit of the simulated motor stepping data with more than 10 states, suggesting applicability of the method to the data with numerous states. Furthermore, the algorithm can retain flexibility because some available prior information, such as dwell time of each state, can be integrated into the algorithm via two user-tunable parameters. In summary, our method enables fast, accurate and objective HMM analysis, and broadens the application range of HMM fitting that could provide more accurate interpretation of a wide variety of biophysical data.

How Do Art Skills Influence Visual Search? - Eye Movements Analyzed With Hidden Markov Models.

The results of two experiments are analyzed to find out how artistic expertise influences visual search. Experiment I comprised survey data of 1,065 students on self-reported visual memory skills and their ability to find three targets in four images of artwork. Experiment II comprised eye movement data of 50 Visual Literacy (VL) experts and non-experts whose eye movements during visual search were analyzed for nine images of artwork as an external validation of the assessment tasks performed in Sample I. No time constraint was set for completion of the visual search task. A latent profile analysis revealed four typical solution patterns for the students in Sample I, including a mainstream group, a group that completes easy images fast and difficult images slowly, a fast and erroneous group, and a slow working student group, depending on task completion time and on the probability of finding all three targets. Eidetic memory, performance in art education and visual imagination as self-reported visual skills have significant impact on latent class membership probability. We present a hidden Markov model (HMM) approach to uncover underlying regions of attraction that result from visual search eye-movement behavior in Experiment II. VL experts and non-experts did not significantly differ in task time and number of targets found but they did differ in their visual search process: compared to non-experts, experts showed greater precision in fixating specific prime and target regions, assessed through hidden state fixation overlap. Exploratory analysis of HMMs revealed differences between experts and non-experts in image locations of attraction (HMM states). Experts seem to focus their attention on smaller image parts whereas non-experts used wider parts of the image during their search. Differences between experts and non-experts depend on the relative saliency of targets embedded in images. HMMs can determine the effect of expertise on exploratory eye movements executed during visual search tasks. Further research on HMMs and art expertise is required to confirm exploratory results.

Motor-related oscillatory activity in schizophrenia according to phase of illness and clinical symptom severity

Magnetoencephalography (MEG) measures magnetic fields generated by synchronised neural current flow and provides direct inference on brain electrophysiology and connectivity, with high spatial and temporal resolution. The movement-related beta decrease (MRBD) and the post-movement beta rebound (PMBR) are well-characterised effects in magnetoencephalography (MEG), with the latter having been shown to relate to long-range network integrity. Our previous work has shown that the PMBR is diminished (relative to controls) in a group of schizophrenia patients. However, little is known about how this effect might differ in patients at different stages of illness and degrees of clinical severity. Here, we extend our previous findings showing that the MEG derived PMBR abnormality in schizophrenia exists in 29 recent-onset and 35 established cases (i.e., chronic patients), compared to 42 control cases. In established cases, PMBR is negatively correlated with severity of disorganization symptoms. Further, using a hidden Markov model analysis, we show that transient pan-spectral oscillatory ”bursts”, which underlie the PMBR, differ between healthy controls and patients. Results corroborate that PMBR is associated with disorganization of mental activity in schizophrenia.

Hidden Markov model technique for dynamic spectrum access

Dynamic spectrum access is a paradigm used to access the spectrum dynamically. A hidden Markov model (HMM) is one in which you observe a sequence of emissions, but do not know the sequence of states the model went through to generate the emissions. Analysis of hidden Markov models seeks to recover the sequence of states from the observed data. In this paper, we estimate the occupancy state of channels using hidden Markov process. Using Viterbi algorithm, we generate the most likely states and compare it with the channel states. We generated two HMMs, one slowly changing and another more dynamic and compare their performance. Using the Baum-Welch algorithm and maximum likelihood algorithm we calculated the estimated transition and emission matrix, and then we compare the estimated states prediction performance of both the methods using stationary distribution of average estimated transition matrix calculated by both the methods.

Analysis of CNV in the Genomes of the Marrow Cells of the Patients with AML Revealed That High Numbers of Aberrations Associate with Better Survival and Deletions with the Aberrant Expression of CD19 in the Leukemic Blasts

In our previous study (Jaskula et al., Blood 2017 130:1420) we reported that the number of CNVs within the KANSL1 gene was associated with the phenotype of AML. In the present study we looked at the presence of CNV across the whole genome. One hundred and twenty seven AML patients (diagnosed according to the FAB/WHO criteria, F/M=62/65, age median: 57, range: 21 - 84 years) were included to the present study. The patients were genetically analysed including GTG karyotyping and/or FISH for X or Y deletion, inv (3), -5/5q-, -7/7q-,+8, MLL, RUNX1, PML/RARA or RARA, inv(16). In all patients the microarray analysis of the bone marrow cells having blasts cells (median value 56%) at diagnosis Agilent - Catalog Agilent Cancer CGH+SNP 180K (74 patients) or Roche - WG Catalog NimbleGene 12x270K (53 patients) microarrays were employed and the results were analysed with the use the Partek software employing the Partek Hidden Markov Model (HMM) and segmentation algorithms. Results: The number of CNV in M0 AML marrow cells was significantly lower (median: 0 aberration), as compared to secondary to MDS AML (median: 4.5 aberrations, p=0.006). Patients with AML subtypes from M1 to M6 had higher number of CNV amplifications (median: 2) as compared to the patients with minimally differentiated blasts (M0, median: 0 amplifications, p=0.030). Knowing that (i) changes in the chromatin structure may be associated with the CNV prevalence within the genome (Gheldof et al., PLoS One. 2013 Nov 12;8(11):e79973) and (ii) the aberrant expression of CD19 in AML blasts results from the chromatin structure variations (Walter et al., Oncogene. 2010 May 20;29(20):2927-37) we looked at the presence of association between the numbers of CNV and the presence of the aberrant CD19 expression in the leukemic blasts. It appeared that 11 AML patients having aberrant expression of CD19 (within whom 4 had t(8:21)) had more frequently CNV deletions than those lacking this aberrant expression (median: 2 vs 1 deletions, p=0.018, HMM algorithm). Having the survival curves of the patients plotted accordingly to the high and low numbers of CNV, the situation is more complex and shows that: the patients having higher numbers of CNV aberrations (exceeding the mean of the whole group +SD) enjoyed better survival (20% vs 11%, p=0.090) when segmentation algorithm was employed. HMM analysis also suggested that the higher values of CNV (amplifications, exceeding the mean of the whole group +SD) was associated with better 5-year survival as compared to those with low numbers (42% vs 20%, ns). The aberrant expression of CD19 analysis was associated with higher numbers of deletions (see above) and with better 5-year survival than those lacking this aberrant expression (45% vs 20%, p=0.064). In conclusion, the prevalence of CNV within the genome shapes the phenotype of the leukemia and facilitates the survival. Disclosures No relevant conflicts of interest to declare.

Climatological Features of the Weakly and Very Stably Stratified Nocturnal Boundary Layers. Part I: State Variables Containing Information about Regime Occupation

AbstractThe atmospheric nocturnal stable boundary layer (SBL) can be classified into two distinct regimes: the weakly SBL (wSBL) with sustained turbulence and the very SBL (vSBL) with weak and intermittent turbulence. A hidden Markov model (HMM) analysis of the three-dimensional state-variable space of Reynolds-averaged mean dry static stability, mean wind speed, and wind speed shear is used to classify the SBL into these two regimes at nine different tower sites, in order to study long-term regime occupation and transition statistics. Both Reynolds-averaged mean data and measures of turbulence intensity (eddy variances) are separated in a physically meaningful way. In particular, fluctuations of the vertical wind component are found to be much smaller in the vSBL than in the wSBL. HMM analyses of these data using more than two SBL regimes do not result in robust results across measurement locations. To identify which meteorological state variables carry the information about regime occupation, the HMM analyses are repeated using different state-variable subsets. Reynolds-averaged measures of turbulence intensity (such as turbulence kinetic energy) at any observed altitude hold almost the same information as the original set, without adding any additional information. In contrast, both stratification and shear depend on surface information to capture regime transitions accurately. Use of information only in the bottom 10 m of the atmosphere is sufficient for HMM analyses to capture important information about regime occupation and transition statistics. It follows that the commonly measured 10-m wind speed is potentially a good indicator of regime occupation.

Construction and analysis of hidden Markov model for piano notes recognition algorithm

Construction and analysis of hidden Markov model for piano notes recognition algorithm

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.

Reconciliation of inconsistent data sources by correction for measurement error: The feasibility of parameter re-use

National Statistical Institutes (NSIs) often obtain information about a single variable from separate data sources. Administrative registers and surveys, in particular, often provide overlapping information on a range of phenomena of interest to official statistics. However, even though the two sources overlap, they both contain measurement error that prevents identical units from yielding identical values. Reconciling such separate data sources and providing accurate statistics, which is an important challenge for NSIs, is typically achieved through macro-integration. In this study we investigate the feasibility of an alternative method based on the application of previously obtained results from a recently introduced extension of the Hidden Markov Model (HMM) to newer data. The method allows a reconciliation of separate error-prone data sources without having to repeat the full HMM analysis, provided the estimated measurement error processes are stable over time. As we find that these processes are indeed stable over time, the proposed method can be used effectively for macro-integration, to reconciliate both first-order statistics-e.g. the size of temporary employment in the Netherlands-and second-order statistics-e.g. the amount of mobility from temporary to permanent employment.