Hidden markov

BACKGROUND: Resting state (RS) brain activity is inherently non-stationary. Hidden Semi-Markov Models (HsMM) can characterize continuous RS data as a sequence of recurring and distinct brain states along with their spatio-temporal dynamics. NEW METHOD: Recent explorations suggest that HsMM state dynamics in the alpha frequency band link to auditory hallucination proneness (HP) in non-clinical individuals. The present study aimed to replicate these findings to elucidate robust neural correlates of hallucinatory vulnerability...

BACKGROUND: Gait impairment has a major impact on quality of life in patients with Parkinson's disease (PD). It is believed that basal ganglia oscillatory activity at β frequencies (15-30 Hz) may contribute to gait impairment, but the precise dynamics of this oscillatory activity during gait remain unclear. Additionally, auditory cues are known to lead to improvements in gait kinematics in PD. If the neurophysiological mechanisms of this cueing effect were better understood they could be leveraged to treat gait impairments using adaptive Deep Brain Stimulation (aDBS) technologies...

Plants are remarkable in their ability to adapt to changing environments, with receptor-like kinases (RLKs) playing a pivotal role in perceiving and transmitting environmental cues into cellular responses. Despite extensive research on RLKs from the plant kingdom, the function and activity of many kinases, i.e., their substrates or "clients", remain uncharted. To validate a novel client prediction workflow and learn more about an important RLK, this study focuses on P2K1 (DORN1), which acts as a receptor for extracellular ATP (eATP), playing a crucial role in plant stress resistance and immunity...

DNA methylation is critical to the regulation of transposable elements and gene expression and can play an important role in the adaptation of stress response mechanisms in plants. Traditional methods of methylation quantification rely on bisulfite conversion that can compromise accuracy. Recent advances in long-read sequencing technologies allow for methylation detection in real time. The associated algorithms that interpret these modifications have evolved from strictly statistical approaches to Hidden Markov Models and, recently, deep learning approaches...

To explore a noninvasive method for diagnosis of SEA-thalassemia and to investigate whether the regional factors affect the accuracy of this method. The method involved using a public database and bioinformatics software to construct parental haplotypes for proband and predicting fetal genotypes using relative haplotype dosage. We screened and downloaded sequencing data of couples who were both SEA-thalassemia carriers from the China National Genebank public data platform, and matched the sequencing data format with that of the reference panel using Ubuntu system tools...

The statistics of the diffusive motion of particles often serve as an experimental proxy for their interaction with the environment. However, inferring the physical properties from the observed trajectories is challenging. Inspired by a recent experiment, here we analyze the problem of particles undergoing two-dimensional Brownian motion with transient tethering to the surface. We model the problem as a hidden Markov model where the physical position is observed and the tethering state is hidden. We develop an alternating maximization algorithm to infer the hidden state of the particle and estimate the physical parameters of the system...

The Pyrococcus horikoshii amino acid transporter GltPh revealed, like other channels and transporters, activity mode switching, previously termed wanderlust kinetics. Unfortunately, to date, the basis of these activity fluctuations is not understood, probably due to a lack of experimental tools that directly access the structural features of transporters related to their instantaneous activity. Here, we take advantage of high-speed atomic force microscopy, unique in providing simultaneous structural and temporal resolution, to uncover the basis of kinetic mode switching in proteins...

In this study, we investigate the effectiveness of noise reduction in electron holography, based on the wavelet hidden Markov model (WHMM), which allows the reasonable separation of weak signals from noise. Electron holography observations from a Nd2 Fe14 B thin foil showed that the noise reduction method suppressed artificial phase discontinuities generated by phase retrieval. From the peak signal-to-noise ratio, it was seen that the impact of denoising was significant for observations with a narrow spacing of interference fringes, which is a key parameter for the spatial resolution of electron holography...

BACKGROUND: As a globally widespread apex predator, humans have unprecedented lethal and non-lethal effects on prey populations and ecosystems. Yet compared to non-human predators, little is known about the movement ecology of human hunters, including how hunting behavior interacts with the environment. METHODS: We characterized the hunting modes, habitat selection, and harvest success of 483 rifle hunters in California using high-resolution GPS data. We used Hidden Markov Models to characterize fine-scale movement behavior, and k-means clustering to group hunters by hunting mode, on the basis of their time spent in each behavioral state...

BACKGROUND: Genomes are inherently inhomogeneous, with features such as base composition, recombination, gene density, and gene expression varying along chromosomes. Evolutionary, biological, and biomedical analyses aim to quantify this variation, account for it during inference procedures, and ultimately determine the causal processes behind it. Since sequential observations along chromosomes are not independent, it is unsurprising that autocorrelation patterns have been observed e.g...

This paper develops two-filter particle smoothing (TFPS) algorithms for the nonlinear fixed-interval smoothing problem of one generalized hidden Markov model (GHMM), where the current observation depends not only on the current state, but also on one-step previous state. Firstly, by Bayesian approach, the two-filter smoothing (TFS) formula for GHMM is established to calculate smoothing densities. In this TFS formula, the backward information prediction density is generally not a density of the state. This results in a difficulty that the normal sequential Monte Carlo (SMC) sampling technique cannot be directly applied to design corresponding TFPS algorithms based on the TFS formula...

Hidden Markov models (HMMs), which can characterize dynamic heterogeneity, are valuable tools for analyzing longitudinal data. The order of HMMs (ie, the number of hidden states) is typically assumed to be known or predetermined by some model selection criterion in conventional analysis. As prior information about the order frequently lacks, pairwise comparisons under criterion-based methods become computationally expensive with the model space growing. A few studies have conducted order selection and parameter estimation simultaneously, but they only considered homogeneous parametric instances...

I present phlash , a new Bayesian method for inferring population history from whole genome sequence data. phlash is p opulation h istory l earning by a veraging s ampled h istories: it works by drawing random, low-dimensional projections of the coalescent intensity function from the posterior distribution of a psmc -like model, and averaging them together to form an accurate and adaptive size history estimator. On simulated data, phlash tends to be faster and have lower error than several competing methods including smc ++, msmc 2, and F it C oal ...

Actinobacteria undergo a complex multicellular life cycle and produce a wide range of specialized metabolites, including the majority of the antibiotics. These biological processes are controlled by intricate regulatory pathways, and to better understand how they are controlled we need to augment our insights into the transcription factor binding sites. Here, we present LogoMotif (https://logomotif.bioinformatics.nl), an open-source database for characterized and predicted transcription factor binding sites in Actinobacteria, along with their cognate position weight matrices and hidden Markov models...

Mixed feelings, the simultaneous presence of feelings with positive and negative valence, remain an understudied topic. They pose a specific set of challenges due to individual variation, and their investigation requires analtyic approaches focusing on individually self-reported states. We used functional magnetic resonance imaging (fMRI) to scan 27 subjects watching an animated short film chosen to induce bittersweet mixed feelings. The same subjects labeled when they had experienced positive, negative, and mixed feelings...

BACKGROUND: Ecological segregation allows populations to reduce competition and coexist in sympatry. Using as model organisms two closely related gadfly petrels endemic to the Madeira archipelago and breeding with a two month allochrony, we investigated how movement and foraging preferences shape ecological segregation in sympatric species. We tested the hypothesis that the breeding allochrony is underpinned by foraging niche segregation. Additionally, we investigated whether our data supported the hypothesis that allochrony is driven by species-specific adaptations to different windscapes...

MOTIVATION: Automated chromatin segmentation based on ChIP-seq data reveals insights into the epigenetic regulation of chromatin accessibility. Existing segmentation methods are constrained by simplifying modeling assumptions, which may have a negative impact on the segmentation quality. RESULTS: We introduce EpiSegMix, a novel segmentation method based on a hidden Markov model with flexible read count distribution types and state duration modeling, allowing for a more flexible modeling of both histone signals and segment lengths...

A pervasive challenge in neuroscience is testing whether neuronal connectivity changes over time due to specific causes, such as stimuli, events, or clinical interventions. Recent hardware innovations and falling data storage costs enable longer, more naturalistic neuronal recordings. The implicit opportunity for understanding the self-organised brain calls for new analysis methods that link temporal scales: from the order of milliseconds over which neuronal dynamics evolve, to the order of minutes, days, or even years over which experimental observations unfold...

Analysis of DNA methylation in cell-free DNA reveals clinically relevant biomarkers but requires specialized protocols such as whole-genome bisulfite sequencing. Meanwhile, millions of cell-free DNA samples are being profiled by whole-genome sequencing. Here, we develop FinaleMe, a non-homogeneous Hidden Markov Model, to predict DNA methylation of cell-free DNA and, therefore, tissues-of-origin, directly from plasma whole-genome sequencing. We validate the performance with 80 pairs of deep and shallow-coverage whole-genome sequencing and whole-genome bisulfite sequencing data...

Motor skills dynamically evolve during practice and after training. Using magnetoencephalography, we investigated the neural dynamics underpinning motor learning and its consolidation in relation to sleep during resting-state periods after the end of learning (boost window, within 30 min) and at delayed time scales (silent 4 h and next day 24 h windows) with intermediate daytime sleep or wakefulness. Resting-state neural dynamics were investigated at fast (sub-second) and slower (supra-second) timescales using Hidden Markov modelling (HMM) and functional connectivity (FC), respectively, and their relationship to motor performance...