Inference of genomic landscapes using ordered Hidden Markov Models with emission densities (oHMMed)

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., in human base composition. In this article, we develop a class of Hidden Markov Models (HMMs) called oHMMed (ordered HMM with emission densities, the corresponding R package of the same name is available on CRAN): They identify the number of comparably homogeneous regions within autocorrelated observed sequences. These are modelled as discrete hidden states; the observed data points are realisations of continuous probability distributions with state-specific means that enable ordering of these distributions. The observed sequence is labelled according to the hidden states, permitting only neighbouring states that are also neighbours within the ordering of their associated distributions. The parameters that characterise these state-specific distributions are inferred.

RESULTS

We apply our oHMMed algorithms to the proportion of G and C bases (modelled as a mixture of normal distributions) and the number of genes (modelled as a mixture of poisson-gamma distributions) in windows along the human, mouse, and fruit fly genomes. This results in a partitioning of the genomes into regions by statistically distinguishable averages of these features, and in a characterisation of their continuous patterns of variation. In regard to the genomic G and C proportion, this latter result distinguishes oHMMed from segmentation algorithms based in isochore or compositional domain theory. We further use oHMMed to conduct a detailed analysis of variation of chromatin accessibility (ATAC-seq) and epigenetic markers H3K27ac and H3K27me3 (modelled as a mixture of poisson-gamma distributions) along the human chromosome 1 and their correlations.

CONCLUSIONS

Our algorithms provide a biologically assumption free approach to characterising genomic landscapes shaped by continuous, autocorrelated patterns of variation. Despite this, the resulting genome segmentation enables extraction of compositionally distinct regions for further downstream analyses.

De Novo Atomic Protein Structure Modeling for Cryo-EM Density Maps Using 3D Transformer and Hidden Markov Model

Accurately building three-dimensional (3D) atomic structures from 3D cryo-electron microscopy (cryo-EM) density maps is a crucial step in the cryo-EM-based determination of the structures of protein complexes. Despite improvements in the resolution of 3D cryo-EM density maps, the de novo conversion of density maps into 3D atomic structures for protein complexes that do not have accurate homologous or predicted structures to be used as templates remains a significant challenge. Here, we introduce Cryo2Struct, a fully automated ab initio cryo-EM structure modeling method that utilizes a 3D transformer to identify atoms and amino acid types in cryo-EM density maps first, and then employs a novel Hidden Markov Model (HMM) to connect predicted atoms to build backbone structures of proteins. Tested on a standard test dataset of 128 cryo-EM density maps with varying resolutions (2.1 - 5.6 °A) and different numbers of residues (730 - 8,416), Cryo2Struct built substantially more accurate and complete protein structural models than the widely used ab initio method - Phenix in terms of multiple evaluation metrics. Moreover, on a new test dataset of 500 recently released density maps with varying resolutions (1.9 - 4.0 °A) and different numbers of residues (234 - 8,828), it built more accurate models than on the standard dataset. And its performance is rather robust against the change of the resolution of density maps and the size of protein structures.

Discovering the neuronal dynamics in major depressive disorder using Hidden Markov Model

Introduction

Major Depressive Disorder (MDD) is a leading cause of worldwide disability, and standard clinical treatments have limitations due to the absence of neurological evidence. Electroencephalography (EEG) monitoring is an effective method for recording neural activities and can provide electroneurophysiological evidence of MDD.

Methods

In this work, we proposed a probabilistic graphical model for neural dynamics decoding on MDD patients and healthy controls (HC), utilizing the Hidden Markov Model with Multivariate Autoregressive observation (HMM-MAR). We testified the model on the MODMA dataset, which contains resting-state and task-state EEG data from 53 participants, including 24 individuals with MDD and 29 HC.

Results

The experimental results suggest that the state time courses generated by the proposed model could regress the Patient Health Questionnaire-9 (PHQ-9) score of the participants and reveal differences between the MDD and HC groups. Meanwhile, the Markov property was observed in the neuronal dynamics of participants presented with sad face stimuli. Coherence analysis and power spectrum estimation demonstrate consistent results with the previous studies on MDD.

Discussion

In conclusion, the proposed HMM-MAR model has revealed its potential capability to capture the neuronal dynamics from EEG signals and interpret brain disease pathogenesis from the perspective of state transition. Compared with the previous machine-learning or deep-learning-based studies, which regarded the decoding model as a black box, this work has its superiority in the spatiotemporal pattern interpretability by utilizing the Hidden Markov Model.

Discovery of paralogous GnRH and corazonin signaling systems in an invertebrate chordate

Gonadotropin-releasing hormone (GnRH) is a key regulator of reproductive function in vertebrates. GnRH is related to the corazonin (CRZ) neuropeptide which influences metabolism and stress responses in insects. Recent evidence suggests that GnRH and CRZ are paralogous and arose by a gene duplication in a common ancestor of bilaterians. Here we report the identification and complete characterization of the GnRH and CRZ signaling systems in the amphioxus Branchiostoma floridae. We have identified a novel GnRH peptide (YSYSYGFAP-NH2) that specifically activates two GnRH receptors and a CRZ peptide (FTYTHTW-NH2) that activates three CRZ receptors in B. floridae. The latter appear to be promiscuous, as two CRZ receptors can also be activated by GnRH in the physiological range. Hence, there is a potential for cross-talk between these closely-related signaling systems. Discovery of both the GnRH and CRZ signaling systems in one of the closestliving relatives of vertebrates provides a framework to discover their roles at the transition from invertebrates to vertebrates.

Phylogenetic Analysis of Anti-CRISPR and Member Addition in the Families

CRISPR-Cas is a widespread anti-viral adaptive immune system in the microorganisms. Viruses living in bacteria or some phages carry anti-CRISPR proteins to evade immunity by CRISPR-Cas. The anti-CRISPR proteins are prevalent in phages capable of lying dormant in a CRISPR-carrying host, while their orthologs frequently found in virulent phages. Here, we propose a probabilistic strategy of ancestral sequence reconstruction (ASR) and Hidden Markov Model (HMM) profile search to fish out sequences of anti-CRISPR proteins from environmental metagenomic, human microbiome metagenomic, human microbiome reference genome, and NCBI's non-redundant databases. Our results revealed that the metagenome database dark matter might contain anti-CRISPR encoding genes.

Female and male Leach's Storm Petrels (Hydrobates leucorhous) pursue different foraging strategies during the incubation period

Reproduction in procellariiform birds is characterized by a single egg clutch, slow development, a long breeding season and obligate biparental care. Female Leach's Storm Petrels Hydrobates leucorhous, nearly monomorphic members of this order, produce eggs that are between 20 and 25% of adult body weight. We tested whether female foraging behaviour differs from male foraging behaviour during the ~ 44-day incubation period across seven breeding colonies in the Northwest Atlantic. Over six breeding seasons, we used a combination of Global Positioning System and Global Location Sensor devices to measure characteristics of individual foraging trips during the incubation period. Females travelled significantly greater distances and went farther from the breeding colony than did males on individual foraging trips. For both sexes, the longer the foraging trip, the greater the distance. Independent of trip duration, females travelled farther, and spent a greater proportion of their foraging trips prospecting widely as defined by behavioural categories derived from a Hidden Markov Model. For both sexes, trip duration decreased with date. Sex differences in these foraging metrics were apparently not a consequence of morphological differences or spatial segregation. Our data are consistent with the idea that female foraging strategies differed from male foraging strategies during incubation in ways that would be expected if females were still compensating for egg formation.

Drivers of polar bear behavior and the possible effects of prey availability on foraging strategy

BACKGROUND

Change in behavior is one of the earliest responses to variation in habitat suitability. It is therefore important to understand the conditions that promote different behaviors, particularly in areas undergoing environmental change. Animal movement is tightly linked to behavior and remote tracking can be used to study ethology when direct observation is not possible.

METHODS

We used movement data from 14 polar bears (Ursus maritimus) in Hudson Bay, Canada, during the foraging season (January-June), when bears inhabit the sea ice. We developed an error-tolerant method to correct for sea ice drift in tracking data. Next, we used hidden Markov models with movement and orientation relative to wind to study three behaviors (stationary, area-restricted search, and olfactory search) and examine effects of 11 covariates on behavior.

RESULTS

Polar bears spent approximately 47% of their time in the stationary drift state, 29% in olfactory search, and 24% in area-restricted search. High energy behaviors occurred later in the day (around 20:00) compared to other populations. Second, olfactory search increased as the season progressed, which may reflect a shift in foraging strategy from still-hunting to active search linked to a shift in seal availability (i.e., increase in haul-outs from winter to the spring pupping and molting seasons). Last, we found spatial patterns of distribution linked to season, ice concentration, and bear age that may be tied to habitat quality and competitive exclusion.

CONCLUSIONS

Our observations were generally consistent with predictions of the marginal value theorem, and differences between our findings and other populations could be explained by regional or temporal variation in resource availability. Our novel movement analyses and finding can help identify periods, regions, and conditions of critical habitat.

Packet Loss Characterization Using Cross Layer Information and HMM for Wi-Fi Networks

Packet loss is a major problem for wireless networks and has significant effects on the perceived quality of many internet services. Packet loss models are used to understand the behavior of packet losses caused by several reasons, e.g., interferences, coexistence, fading, collisions, and insufficient/excessive memory buffers. Among these, the Gilbert-Elliot (GE) model, based on a two-state Markov chain, is the most used model in communication networks. However, research has proven that the GE model is inadequate to represent the real behavior of packet losses in Wi-Fi networks. In this last category, variables of a single network layer are used, usually the physical one. In this article, we propose a new packet loss model for Wi-Fi that simultaneously considers the temporal behavior of losses and the variables that describe the state of the network. In addition, the model uses two important variables, the signal-to-noise ratio and the network occupation, which none of the packet loss models available for Wi-Fi networks simultaneously take into account. The proposed model uses the well-known Hidden Markov Model (HMM), which facilitates training and forecasting. At each state of HMM, the burst-length of losses is characterized using probability distributions. The model was evaluated by comparing computer simulation and real data samples for validation, and using the log-log complementary distribution of burst-length. We compared the proposed model with competing models through the analysis of mean square error (MSE) using a validation sample collected from a real network. Results demonstrated that the proposed model outperforms the currently available models for packet loss in Wi-Fi networks.

A Depression-Risk Mental Pattern Identified by Hidden Markov Model in Undergraduates

Few studies have examined depression risk screening approaches. Universal depression screening in youth typically focuses on directly measuring the current distress and impairment by several kinds of depression rating scales. However, as many people have stigmatizing attitudes to individuals with depression, youths with depression were in fear of being known, and embarrassment held them back from reporting their depression symptoms. Thus, the present study aimed to identify the best, most easy access screening approach for indirectly predicting depression risks in undergraduates. Here, the depression score was ranked and viewed as the different stages in the development of depression; then, we used a Hidden Markov Model (HMM) approach to identify depression risks. Participants included 1247 undergraduates (female = 720, mean age = 19.86 years (std =1.31), from 17 to 25) who independently completed inventories for depressive symptoms, emotion regulation, subjective well-being (life satisfaction, negative and positive affect), and coping styles (positive and negative). Our findings indicated that the risk pattern (state 1) and the health pattern (state 2) showed distinct different rating results in emotional regulation, subjective well-being, and coping style. Screening for prospective risk of depression can be better accomplished by HMM incorporating subjective well-being, emotion regulation, and coping style. This study discussed the implications for future research and evidence-based decision-making for depression screening initiatives.

Alternative cleavage and polyadenylation generates downstream uncapped RNA isoforms with translation potential

The use of alternative promoters, splicing, and cleavage and polyadenylation (APA) generates mRNA isoforms that expand the diversity and complexity of the transcriptome. Here, we uncovered thousands of previously undescribed 5' uncapped and polyadenylated transcripts (5' UPTs). We show that these transcripts resist exonucleases due to a highly structured RNA and N6-methyladenosine modification at their 5' termini. 5' UPTs appear downstream of APA sites within their host genes and are induced upon APA activation. Strong enrichment in polysomal RNA fractions indicates 5' UPT translational potential. Indeed, APA promotes downstream translation initiation, non-canonical protein output, and consistent changes to peptide presentation at the cell surface. Lastly, we demonstrate the biological importance of 5' UPTs using Bcl2, a prominent anti-apoptotic gene whose entire coding sequence is a 5' UPT generated from 5' UTR-embedded APA sites. Thus, APA is not only accountable for terminating transcripts, but also for generating downstream uncapped RNAs with translation potential and biological impact.

HMM-Based Action Recognition System for Elderly Healthcare by Colorizing Depth Map

Addressing the problems facing the elderly, whether living independently or in managed care facilities, is considered one of the most important applications for action recognition research. However, existing systems are not ready for automation, or for effective use in continuous operation. Therefore, we have developed theoretical and practical foundations for a new real-time action recognition system. This system is based on Hidden Markov Model (HMM) along with colorizing depth maps. The use of depth cameras provides privacy protection. Colorizing depth images in the hue color space enables compressing and visualizing depth data, and detecting persons. The specific detector used for person detection is You Look Only Once (YOLOv5). Appearance and motion features are extracted from depth map sequences and are represented with a Histogram of Oriented Gradients (HOG). These HOG feature vectors are transformed as the observation sequences and then fed into the HMM. Finally, the Viterbi Algorithm is applied to recognize the sequential actions. This system has been tested on real-world data featuring three participants in a care center. We tried out three combinations of HMM with classification algorithms and found that a fusion with Support Vector Machine (SVM) had the best average results, achieving an accuracy rate (84.04%).

Coping with the liquidity crisis: a new dynamic quota readjustment scheme for carbon markets

Because of insufficient liquidity, prices in the carbon market are more vulnerable to unexpected events, for which the impact duration lasts longer than that of the general market. The root reason for this phenomenon lies in the irrationality of quota distribution. The existing quota adjustment schemes and policies, e.g., the market stability reserve (MSR) and some recent adjustment measures, have poor timeliness and effectiveness, which has increased the risk of market crashes. Using the Hidden Markov Model (HMM), this paper develops a new dynamic quota adjustment scheme that can rapidly reduce the risk of quota supply by bridging quota price and quantity with price feedback as a response signal. To achieve this, we integrated the HMM algorithm and a two-step quota adjustment model by setting price thresholds and then connected the quota adjustment transition matrix and historical quota price. By comparing the MSR from 2013 to 2018, our scheme will help mitigate risks in quota price because the HMM can show the actual impact of price feedback on quota adjustment with merits of steady quota price and timely supply optimization. Moreover, our scheme, which recalculates the transition matrix, can be applied in other mature carbon markets.

Inferring Brain State Dynamics Underlying Naturalistic Stimuli Evoked Emotion Changes With dHA-HMM

The brain functional mechanisms underlying emotional changes have been primarily studied based on the traditional task design with discrete and simple stimuli. However, the brain state transitions when exposed to continuous and naturalistic stimuli with rich affection variations remain poorly understood. This study proposes a dynamic hyperalignment algorithm (dHA) to functionally align the inter-subject neural activity. The hidden Markov model (HMM) was used to study how the brain dynamics responds to emotion during long-time movie-viewing activity. The results showed that dHA significantly improved inter-subject consistency and allowed more consistent temporal HMM states across participants. Afterward, grouping the emotions in a clustering dendrogram revealed a hierarchical grouping of the HMM states. Further emotional sensitivity and specificity analyses of ordered states revealed the most significant differences in happiness and sadness. We then compared the activation map in HMM states during happiness and sadness and found significant differences in the whole brain, but strong activation was observed during both in the superior temporal gyrus, which is related to the early process of emotional prosody processing. A comparison of the inter-network functional connections indicates unique functional connections of the memory retrieval and cognitive network with the cerebellum network during happiness. Moreover, the persistent bilateral connections among salience, cognitive, and sensorimotor networks during sadness may reflect the interaction between high-level cognitive networks and low-level sensory networks. The main results were verified by the second session of the dataset. All these findings enrich our understanding of the brain states related to emotional variation during naturalistic stimuli.

Effective psychological therapy for PTSD changes the dynamics of specific large-scale brain networks

In posttraumatic stress disorder (PTSD), re-experiencing of the trauma is a hallmark symptom proposed to emerge from a de-contextualised trauma memory. Cognitive therapy for PTSD (CT-PTSD) addresses this de-contextualisation through different strategies. At the brain level, recent research suggests that the dynamics of specific large-scale brain networks play an essential role in both the healthy response to a threatening situation and the development of PTSD. However, very little is known about how these dynamics are altered in the disorder and rebalanced after treatment and successful recovery. Using a data-driven approach and fMRI, we detected recurring large-scale brain functional states with high temporal precision in a population of healthy trauma-exposed and PTSD participants before and after successful CT-PTSD. We estimated the total amount of time that each participant spent on each of the states while being exposed to trauma-related and neutral pictures. We found that PTSD participants spent less time on two default mode subnetworks involved in different forms of self-referential processing in contrast to PTSD participants after CT-PTSD (mtDMN+ and dmDMN+ ) and healthy trauma-exposed controls (only mtDMN+ ). Furthermore, re-experiencing severity was related to decreased time spent on the default mode subnetwork involved in contextualised retrieval of autobiographical memories, and increased time spent on the salience and visual networks. Overall, our results support the hypothesis that PTSD involves an imbalance in the dynamics of specific large-scale brain network states involved in self-referential processes and threat detection, and suggest that successful CT-PTSD might rebalance this dynamic aspect of brain function.

Spared performance but increased uncertainty in schizophrenia: Evidence from a probabilistic decision-making task

Aberrant attribution of salience to in fact little informative events might explain the emergence of positive symptoms in schizophrenia and has been linked to belief uncertainty. Uncertainty is thought to be encoded by neuromodulators, including norepinephrine. However, norepinephrinergic encoding of uncertainty, measured as task-related pupil dilation, has rarely been explored in schizophrenia. Here, we addressed this question by comparing individuals with a disorder from the schizophrenia spectrum to a non-psychiatric control group on behavioral and pupillometric measures in a probabilistic prediction task, where different levels of uncertainty were introduced. Behaviorally, patients performed similar to controls, but their belief uncertainty was higher, particularly when instability of the task environment was high, suggesting an increased sensitivity to this instability. Furthermore, while pupil dilation scaled positively with uncertainty, this was less the case for patients, suggesting aberrant neuromodulatory regulation of neural gain, which may hinder the reduction of uncertainty in the long run. Together, the findings point to abnormal uncertainty processing and norepinephrinergic signaling in schizophrenia, potentially informing future development of both psychopharmacological therapies and psychotherapeutic approaches that deal with the processing of uncertain information.

Prey encounters and spatial memory influence use of foraging patches in a marine central place forager

Given the patchiness and long-term predictability of marine resources, memory of high-quality foraging grounds is expected to provide fitness advantages for central place foragers. However, it remains challenging to characterize how marine predators integrate memory with recent prey encounters to adjust fine-scale movement and use of foraging patches. Here, we used two months of movement data from harbour seals (Phoca vitulina) to quantify the repeatability in foraging patches as a proxy for memory. We then integrated these data into analyses of fine-scale movement and underwater behaviour to test how both spatial memory and prey encounter rates influenced the seals' area-restricted search (ARS) behaviour. Specifically, we used one month's GPS data from 29 individuals to build spatial memory maps of searched areas and archived accelerometery data from a subset of five individuals to detect prey catch attempts, a proxy for prey encounters. Individuals were highly consistent in the areas they visited over two consecutive months. Hidden Markov models showed that both spatial memory and prey encounters increased the probability of seals initiating ARS. These results provide evidence that predators use memory to adjust their fine-scale movement, and this ability should be accounted for in movement models.

The "Neural Shift" of Sleep Quality and Cognitive Aging: A Resting-State MEG Study of Transient Neural Dynamics

Sleep quality changes dramatically from young to old age, but its effects on brain dynamics and cognitive functions are not yet fully understood. We tested the hypothesis that a shift in brain networks dynamics relates to sleep quality and cognitive performance across the lifespan. Network dynamics were assessed using Hidden Markov Models (HMMs) in resting-state MEG data from a large cohort of population-based adults (N = 564, aged 18-88). Using multivariate analyses of brain-sleep profiles and brain-cognition profiles, we found an age-related "neural shift," expressed as decreased occurrence of "lower-order" brain networks coupled with increased occurrence of "higher-order" networks. This "neural shift" was associated with both increased sleep dysfunction and decreased fluid intelligence, and this relationship was not explained by age, sex or other covariates. These results establish the link between poor sleep quality, as evident in aging, and a behavior-related shift in neural dynamics.

A Transformer-Based Hierarchical Variational AutoEncoder Combined Hidden Markov Model for Long Text Generation

The Variational AutoEncoder (VAE) has made significant progress in text generation, but it focused on short text (always a sentence). Long texts consist of multiple sentences. There is a particular relationship between each sentence, especially between the latent variables that control the generation of the sentences. The relationships between these latent variables help in generating continuous and logically connected long texts. There exist very few studies on the relationships between these latent variables. We proposed a method for combining the Transformer-Based Hierarchical Variational AutoEncoder and Hidden Markov Model (HT-HVAE) to learn multiple hierarchical latent variables and their relationships. This application improves long text generation. We use a hierarchical Transformer encoder to encode the long texts in order to obtain better hierarchical information of the long text. HT-HVAE's generation network uses HMM to learn the relationship between latent variables. We also proposed a method for calculating the perplexity for the multiple hierarchical latent variable structure. The experimental results show that our model is more effective in the dataset with strong logic, alleviates the notorious posterior collapse problem, and generates more continuous and logically connected long text.

orthofisher: a broadly applicable tool for automated gene identification and retrieval

Identification and retrieval of genes of interest from genomic data are an essential step for many bioinformatic applications. We present orthofisher, a command-line tool for automated identification and retrieval of genes with high sequence similarity to a query profile Hidden Markov Model sequence alignment across a set of proteomes. Performance assessment of orthofisher revealed high accuracy and precision during single-copy orthologous gene identification. orthofisher may be useful for assessing gene annotation quality, identifying single-copy orthologous genes for phylogenomic analyses, estimating gene copy number, and other evolutionary analyses that rely on identification and retrieval of homologous genes from genomic data. orthofisher comes complete with comprehensive documentation (https://jlsteenwyk.com/orthofisher/), is freely available under the MIT license, and is available for download from GitHub (https://github.com/JLSteenwyk/orthofisher), PyPi (https://pypi.org/project/orthofisher/), and the Anaconda Cloud (https://anaconda.org/jlsteenwyk/orthofisher).

Climate influences on female survival in a declining population of southern elephant seals (Mirounga leonina)

The Southern Ocean has been disproportionately affected by climate change and is therefore an ideal place to study the influence of changing environmental conditions on ecosystems. Changes in the demography of predator populations are indicators of broader shifts in food web structure, but long-term data are required to study these effects. Southern elephant seals (Mirounga leonina) from Macquarie Island have consistently decreased in population size while all other major populations across the Southern Ocean have recently stabilized or are increasing. Two long-term mark-recapture studies (1956-1967 and 1993-2009) have monitored this population, which provides an opportunity to investigate demographic performance over a range of climatic conditions. Using a 9-state matrix population model, we estimated climate influences on female survival by incorporating two major climatic indices into our model: The Southern Annular Mode (SAM) and the Southern Oscillation Index (SOI). Our best model included a 1 year lagged effect of SAM and an unlagged SOI as covariates. A positive relationship with SAM1 (lagged) related the previous year's SAM with juvenile survival, potentially due to changes in local prey availability surrounding Macquarie Island. The unlagged SOI had a negative effect on both juvenile and adult seals, indicating that sea ice dynamics and access to foraging grounds on the East Antarctic continental shelf could explain the different contributions of ENSO events on the survival of females in this population.

A Global Approach to Estimating the Abundance and Duplication of Polyketide Synthase Domains in Dinoflagellates

Many dinoflagellate species make toxins in a myriad of different molecular configurations but the underlying chemistry in all cases is presumably via modular synthases, primarily polyketide synthases. In many organisms modular synthases occur as discrete synthetic genes or domains within a gene that act in coordination thus forming a module that produces a particular fragment of a natural product. The modules usually occur in tandem as gene clusters with a syntenic arrangement that is often predictive of the resultant structure. Dinoflagellate genomes however are notoriously complex with individual genes present in many tandem repeats and very few synthetic modules occurring as gene clusters, unlike what has been seen in bacteria and fungi. However, modular synthesis in all organisms requires a free thiol group that acts as a carrier for sequential synthesis called a thiolation domain. We scanned 47 dinoflagellate transcriptomes for 23 modular synthase domain models and compared their abundance among 10 orders of dinoflagellates as well as their co-occurrence with thiolation domains. The total count of domain types was quite large with over thirty-thousand identified, 29 000 of which were in the core dinoflagellates. Although there were no specific trends in domain abundance associated with types of toxins, there were readily observable lineage specific differences. The Gymnodiniales, makers of long polyketide toxins such as brevetoxin and karlotoxin had a high relative abundance of thiolation domains as well as multiple thiolation domains within a single transcript. Orders such as the Gonyaulacales, makers of small polyketides such as spirolides, had fewer thiolation domains but a relative increase in the number of acyl transferases. Unique to the core dinoflagellates, however, were thiolation domains occurring alongside tetratricopeptide repeats that facilitate protein-protein interactions, especially hexa and hepta-repeats, that may explain the scaffolding required for synthetic complexes capable of making large toxins. Clustering analysis for each type of domain was also used to discern possible origins of duplication for the multitude of single domain transcripts. Single domain transcripts frequently clustered with synonymous domains from multi-domain transcripts such as the BurA and ZmaK like genes as well as the multi-ketosynthase genes, sometimes with a large degree of apparent gene duplication, while fatty acid synthesis genes formed distinct clusters. Surprisingly the acyl-transferases and ketoreductases involved in fatty acid synthesis (FabD and FabG, respectively) were found in very large clusters indicating an unprecedented degree of gene duplication for these genes. These results demonstrate a complex evolutionary history of core dinoflagellate modular synthases with domain specific duplications throughout the lineage as well as clues to how large protein complexes can be assembled to synthesize the largest natural products known.

Motor-like neural dynamics in two parietal areas during arm reaching

The classical view on motor control makes a clear distinction between the role of motor cortex in controlling muscles and parietal cortex in processing movement plans and goals. However, the strong parieto-frontal connections argue against such clear-cut separation of function. Modern dynamical approaches revealed that population activity in motor cortex can be captured by a limited number of patterns, called neural states that are preserved across diverse motor behaviors. Whether such dynamics are also present in parietal cortex is unclear. Here, we studied neural dynamics in the primate parietal cortex during arm movements and found three main states temporally coupled to the planning, execution and target holding epochs. Strikingly, as reported recently in motor cortex, execution was subdivided into distinct, arm acceleration- and deceleration-related, states. These results suggest that dynamics across parieto-frontal areas are highly consistent and hint that parietal population activity largely reflects timing constraints while motor actions unfold.

Detection of Apnea Bradycardia from ECG Signals of Preterm Infants Using Layered Hidden Markov Model

Apnea-bradycardia (AB) is a common complication in prematurely born infants, which is associated with reduced survival and neurodevelopmental outcomes. Thus, early detection or predication of AB episodes is critical for initiating preventive interventions. To develop automatic real-time operating systems for early detection of AB, recent advances in signal processing can be employed. Hidden Markov Models (HMM) are probabilistic models with the ability of learning different dynamics of the real time-series such as clinical recordings. In this study, a hierarchy of HMMs named as layered HMM was presented to detect AB episodes from pre-processed single-channel Electrocardiography (ECG). For training the hierarchical structure, RR interval, and width of QRS complex were extracted from ECG as observations. The recordings of 32 premature infants with median 31.2 (29.7, 31.9) weeks of gestation were used for this study. The performance of the proposed layered HMM was evaluated in detecting AB. The best average accuracy of 97.14 ± 0.31% with detection delay of - 5.05 ± 0.41 s was achieved. The results show that layered structure can improve the performance of the detection system in early detecting of AB episodes. Such system can be incorporated for more robust long-term monitoring of preterm infants.

Activity-Integrated Hidden Markov Model to Predict Calving Time

Simple Summary

Dairy cows are known to become more active during the time calving approaches. Dairy farms provide individual calving pens to monitor the behavior of pregnant cows. Frequent posture changes such as alternating between lying and standing are good indicators that calving is imminent. In this paper, we aimed to determine how using these behavior changes or activities could help predict calving time. The activity monitoring video cameras in this study were located at a top corner of the calving pens so that the whole pens are visible. By processing the collected video sequences, the activities of pregnant cows three days before the calving were modeled in a Hidden Markov Model to predict the time when the calving event occurs. The experimental results show that the proposed method has promise.

Abstract

Accurately predicting when calving will occur can provide great value in managing a dairy farm since it provides personnel with the ability to determine whether assistance is necessary. Not providing such assistance when necessary could prolong the calving process, negatively affecting the health of both mother cow and calf. Such prolongation could lead to multiple illnesses. Calving is one of the most critical situations for cows during the production cycle. A precise video-monitoring system for cows can provide early detection of difficulties or health problems, and facilitates timely and appropriate human intervention. In this paper, we propose an integrated approach for predicting when calving will occur by combining behavioral activities extracted from recorded video sequences with a Hidden Markov Model. Specifically, two sub-systems comprise our proposed system: (i) Behaviors extraction such as lying, standing, number of changing positions between lying down and standing up, and other significant activities, such as holding up the tail, and turning the head to the side; and, (ii) using an integrated Hidden Markov Model to predict when calving will occur. The experiments using our proposed system were conducted at a large dairy farm in Oita Prefecture in Japan. Experimental results show that the proposed method has promise in practical applications. In particular, we found that the high frequency of posture changes has played a central role in accurately predicting the time of calving.

The human malaria parasite genome is configured into thousands of coexpressed linear regulatory units

The human malaria parasite Plasmodium falciparum thrives in radically different host environments in mosquitoes and humans, with only a limited set of transcription factors. The nature of regulatory elements or their target genes in the P. falciparum genome remains elusive. Here, we found that this eukaryotic parasite uses an efficient way to maximally use genetic and epigenetic regulation to form regulatory units (RUs) during blood infections. Genes located in the same RU tend to have the same pattern of expression over time and are associated with open chromatin along regulatory elements. To precisely define and quantify these RUs, a novel hidden Markov model was developed to capture the regulatory structure in a genome-wide fashion by integrating expression and epigenetic evidence. We successfully identified thousands of RUs and cross-validated with previous findings. We found more genes involved in red blood cell (RBC) invasion located in the same RU as the PfAP2-I (AP2-I) transcription factor, demonstrating that AP2-I is responsible for regulating RBC invasion. Our study has provided a regulatory mechanism for a compact eukaryotic genome and offers new insights into the in vivo transcriptional regulation of the P. falciparum intraerythrocytic stage.

Signal diffusion along connectome gradients and inter-hub routing differentially contribute to dynamic human brain function

Human cognition is dynamic, alternating over time between externally-focused states and more abstract, often self-generated, patterns of thought. Although cognitive neuroscience has documented how networks anchor particular modes of brain function, mechanisms that describe transitions between distinct functional states remain poorly understood. Here, we examined how time-varying changes in brain function emerge within the constraints imposed by macroscale structural network organization. Studying a large cohort of healthy adults (n = 326), we capitalized on manifold learning techniques that identify low dimensional representations of structural connectome organization and we decomposed neurophysiological activity into distinct functional states and their transition patterns using Hidden Markov Models. Structural connectome organization predicted dynamic transitions anchored in sensorimotor systems and those between sensorimotor and transmodal states. Connectome topology analyses revealed that transitions involving sensorimotor states traversed short and intermediary distances and adhered strongly to communication mechanisms of network diffusion. Conversely, transitions between transmodal states involved spatially distributed hubs and increasingly engaged long-range routing. These findings establish that the structure of the cortex is optimized to allow neural states the freedom to vary between distinct modes of processing, and so provides a key insight into the neural mechanisms that give rise to the flexibility of human cognition.

Learning Scan Paths of Eye Movement in Autism Spectrum Disorder

Eye tracking studies have demonstrated deficits in attention in individuals with Autism Spectrum Disorder (ASD) for a range of different social attention-based tasks. Here we examined social attention skills in a large sample of ASD participants (n = 120), using eye tracking data from a social information processing task, and compared them with a typically developing (TD) group (n = 35). Assuming eye movement parameters are random variables generated by an underlying stochastic process, we modeled the fixation sequences of participants in ASD and TD groups with a Hidden Markov Model. The Regions of Interests (ROIs), modeled as hidden states, corresponded to the true ROIs with a prediction accuracy of >90% for each group. The transition between ROIs revealed bias towards a specific area in the scene in ASD group, which deviated from the TD group. Objective time-dynamic measures of gaze patterns can potentially serve as useful endpoints in ASD diagnosis. Clinical Trial Registration: NCT02299700.

Image Processing Technique and Hidden Markov Model for an Elderly Care Monitoring System

Advances in image processing technologies have provided more precise views in medical and health care management systems. Among many other topics, this paper focuses on several aspects of video-based monitoring systems for elderly people living independently. Major concerns are patients with chronic diseases and adults with a decline in physical fitness, as well as falling among elderly people, which is a source of life-threatening injuries and a leading cause of death. Therefore, in this paper, we propose a video-vision-based monitoring system using image processing technology and a Hidden Markov Model for differentiating falls from normal states for people. Specifically, the proposed system is composed of four modules: (1) object detection; (2) feature extraction; (3) analysis for differentiating normal states from falls; and (4) a decision-making process using a Hidden Markov Model for sequential states of abnormal and normal. In the object detection module, background and foreground segmentation is performed by applying the Mixture of Gaussians model, and graph cut is applied for foreground refinement. In the feature extraction module, the postures and positions of detected objects are estimated by applying the hybrid features of the virtual grounding point, inclusive of its related area and the aspect ratio of the object. In the analysis module, for differentiating normal, abnormal, or falling states, statistical computations called the moving average and modified difference are conducted, both of which are employed to estimate the points and periods of falls. Then, the local maximum or local minimum and the half width value are determined in the observed modified difference to more precisely estimate the period of a falling state. Finally, the decision-making process is conducted by developing a Hidden Markov Model. The experimental results used the Le2i fall detection dataset, and showed that our proposed system is robust and reliable and has a high detection rate.

Growth Rate and Acceleration Analysis of the COVID-19 Pandemic Reveals the Effect of Public Health Measures in Real Time

Background: Ending the COVID-19 pandemic is arguably one of the most prominent challenges in recent human history. Following closely the growth dynamics of the disease is one of the pillars toward achieving that goal.

Objective: We aimed at developing a simple framework to facilitate the analysis of the growth rate (cases/day) and growth acceleration (cases/day²) of COVID-19 cases in real-time.

Methods: The framework was built using the Moving Regression (MR) technique and a Hidden Markov Model (HMM). The dynamics of the pandemic was initially modeled via combinations of four different growth stages: lagging (beginning of the outbreak), exponential (rapid growth), deceleration (growth decay), and stationary (near zero growth). A fifth growth behavior, namely linear growth (constant growth above zero), was further introduced to add more flexibility to the framework. An R Shiny application was developed, which can be accessed at https://theguarani.com.br/ or downloaded from https://github.com/adamtaiti/SARS-CoV-2. The framework was applied to data from the European Center for Disease Prevention and Control (ECDC), which comprised 3,722,128 cases reported worldwide as of May 8th 2020.

Results: We found that the impact of public health measures on the prevalence of COVID-19 could be perceived in seemingly real-time by monitoring growth acceleration curves. Restriction to human mobility produced detectable decline in growth acceleration within 1 week, deceleration within ~2 weeks and near-stationary growth within ~6 weeks. Countries exhibiting different permutations of the five growth stages indicated that the evolution of COVID-19 prevalence is more complex and dynamic than previously appreciated.

Conclusions: These results corroborate that mass social isolation is a highly effective measure against the dissemination of SARS-CoV-2, as previously suggested. Apart from the analysis of prevalence partitioned by country, the proposed framework is easily applicable to city, state, region and arbitrary territory data, serving as an asset to monitor the local behavior of COVID-19 cases.

A novel machine learning unsupervised algorithm for sleep/wake identification using actigraphy

Actigraphy is widely used in sleep studies but lacks a universal unsupervised algorithm for sleep/wake identification. An unsupervised algorithm is useful in large-scale population studies and in cases where polysomnography (PSG) is unavailable, as it does not require sleep outcome labels to train the model but utilizes information solely contained in actigraphy to learn sleep and wake characteristics and separate the two states. In this study, we proposed a machine learning unsupervised algorithm based on the Hidden Markov Model (HMM) for sleep/wake identification. The proposed algorithm is also an individualized approach that takes into account individual variabilities and analyzes each individual actigraphy profile separately to infer sleep and wake states. We used Actiwatch and PSG data from 43 individuals in the Multi-Ethnic Study of Atherosclerosis study to evaluate the method performance. Epoch-by-epoch comparisons and sleep variable comparisons were made between our algorithm, the unsupervised algorithm embedded in the Actiwatch software (AS), and the pre-trained supervised UCSD algorithm. Using PSG as the reference, the accuracy was 85.7% for HMM, 84.7% for AS, and 85.0% for UCSD. The sensitivity was 99.3%, 99.7%, and 98.9% for HMM, AS, and UCSD, respectively, and the specificity was 36.4%, 30.0%, and 31.7%, respectively. The Kappa statistic was 0.446 for HMM, 0.399 for AS, and 0.311 for UCSD, suggesting fair to moderate agreement between PSG and actigraphy. The Bland-Altman plots further show that the total sleep time, sleep latency, and sleep efficiency estimates by HMM were closer to PSG with narrower 95% limits of agreement than AS and UCSD. All three methods tend to overestimate sleep and underestimate wake compared to PSG. Our HMM approach is also able to differentiate relatively active and sedentary individuals by quantifying variabilities in activity counts: individuals with higher estimated activity variabilities tend to show more frequent sedentary behaviors. Our unsupervised data-driven HMM algorithm achieved better performance than the commonly used Actiwatch software algorithm and the pre-trained UCSD algorithm. HMM can help expand the application of actigraphy in cases where PSG is hard to acquire and supervised methods cannot be trained. In addition, the estimated HMM parameters can characterize individual activity patterns and sedentary tendencies that can be further utilized in downstream analysis.

Covert sleep-related biological processes are revealed by probabilistic analysis in Drosophila

Reduced sleep duration and disrupted sleep quality are correlated with adverse mental and physical health outcomes. Better tools for measuring the internal drives for sleep and wake in model organisms would facilitate understanding the role of sleep quality in health. We defined two conditional probabilities, P(Wake) and P(Doze), that can be calculated from recordings of Drosophila activity without disturbing the animal. We demonstrated that P(Wake) is a measure of sleep depth and that P(Doze) is a measure of sleep pressure. In parallel, we developed an automatic classifier for state-based analysis of Drosophila behavior. These analysis tools will improve our understanding of the pharmacology and neuronal regulation of behavioral drives in the Drosophila brain.

Sleep pressure and sleep depth are key regulators of wake and sleep. Current methods of measuring these parameters in Drosophila melanogaster have low temporal resolution and/or require disrupting sleep. Here we report analysis tools for high-resolution, noninvasive measurement of sleep pressure and depth from movement data. Probability of initiating activity, P(Wake), measures sleep depth while probability of ceasing activity, P(Doze), measures sleep pressure. In vivo and computational analyses show that P(Wake) and P(Doze) are largely independent and control the amount of total sleep. We also develop a Hidden Markov Model that allows visualization of distinct sleep/wake substates. These hidden states have a predictable relationship with P(Doze) and P(Wake), suggesting that the methods capture the same behaviors. Importantly, we demonstrate that both the Doze/Wake probabilities and the sleep/wake substates are tied to specific biological processes. These metrics provide greater mechanistic insight into behavior than measuring the amount of sleep alone.

Genotype-free demultiplexing of pooled single-cell RNA-seq

A variety of methods have been developed to demultiplex pooled samples in a single cell RNA sequencing (scRNA-seq) experiment which either require hashtag barcodes or sample genotypes prior to pooling. We introduce scSplit which utilizes genetic differences inferred from scRNA-seq data alone to demultiplex pooled samples. scSplit also enables mapping clusters to original samples. Using simulated, merged, and pooled multi-individual datasets, we show that scSplit prediction is highly concordant with demuxlet predictions and is highly consistent with the known truth in cell-hashing dataset. scSplit is ideally suited to samples without external genotype information and is available at: https://github.com/jon-xu/scSplit.

Multi-channel EEG based automatic epileptic seizure detection using iterative filtering decomposition and Hidden Markov Model

Electroencephalography (EEG) is a non-invasive method for the analysis of neurological disorders. Epilepsy is one of the most widespread neurological disorders and often characterized by repeated seizures. This paper intends to conduct an iterative filtering based decomposition of EEG signals to improve upon the accuracy of seizure detection. The proposed approach is evaluated using All India Institute of Medical Science (AIIMS) Patna EEG database and online CHB-MIT surface EEG database. The iterative filtering decomposition technique is applied to extract sub-components from the EEG signal. The feature set obtained from each segmented intrinsic mode function consists of 2-D power spectral density and time-domain features dynamic mode decomposition power, variance, and Katz fractal dimension. The Hidden Markov Model (HMM) based probabilistic model has been designed using the above-stated features representing the seizure and non-seizure EEG events. The EEG signal is classified based on the maximum score obtained from the individual feature-based classifiers. The maximum score derived from each HMM classifier gives the final class information. The proposed decomposition of EEG signals achieved 99.60% and 99.74% accuracy in seizure detection for the online CHB-MIT surface EEG database and AIIMS Patna EEG database, respectively.

ProbPFP: a multiple sequence alignment algorithm combining hidden Markov model optimized by particle swarm optimization with partition function

BACKGROUND

During procedures for conducting multiple sequence alignment, that is so essential to use the substitution score of pairwise alignment. To compute adaptive scores for alignment, researchers usually use Hidden Markov Model or probabilistic consistency methods such as partition function. Recent studies show that optimizing the parameters for hidden Markov model, as well as integrating hidden Markov model with partition function can raise the accuracy of alignment. The combination of partition function and optimized HMM, which could further improve the alignment's accuracy, however, was ignored by these researches.

RESULTS

A novel algorithm for MSA called ProbPFP is presented in this paper. It intergrate optimized HMM by particle swarm with partition function. The algorithm of PSO was applied to optimize HMM's parameters. After that, the posterior probability obtained by the HMM was combined with the one obtained by partition function, and thus to calculate an integrated substitution score for alignment. In order to evaluate the effectiveness of ProbPFP, we compared it with 13 outstanding or classic MSA methods. The results demonstrate that the alignments obtained by ProbPFP got the maximum mean TC scores and mean SP scores on these two benchmark datasets: SABmark and OXBench, and it got the second highest mean TC scores and mean SP scores on the benchmark dataset BAliBASE. ProbPFP is also compared with 4 other outstanding methods, by reconstructing the phylogenetic trees for six protein families extracted from the database TreeFam, based on the alignments obtained by these 5 methods. The result indicates that the reference trees are closer to the phylogenetic trees reconstructed from the alignments obtained by ProbPFP than the other methods.

CONCLUSIONS

We propose a new multiple sequence alignment method combining optimized HMM and partition function in this paper. The performance validates this method could make a great improvement of the alignment's accuracy.

Profile Comparer Extended: phylogeny of lytic polysaccharide monooxygenase families using profile hidden Markov model alignments

Insight into the inter- and intra-family relationship of protein families is important, since it can aid understanding of substrate specificity evolution and assign putative functions to proteins with unknown function. To study both these inter- and intra-family relationships, the ability to build phylogenetic trees using the most sensitive sequence similarity search methods (e.g. profile hidden Markov model (pHMM)-pHMM alignments) is required. However, existing solutions require a very long calculation time to obtain the phylogenetic tree. Therefore, a faster protocol is required to make this approach efficient for research. To contribute to this goal, we extended the original Profile Comparer program (PRC) for the construction of large pHMM phylogenetic trees at speeds several orders of magnitude faster compared to pHMM-tree. As an example, PRC Extended (PRCx) was used to study the phylogeny of over 10,000 sequences of lytic polysaccharide monooxygenase (LPMO) from over seven families. Using the newly developed program we were able to reveal previously unknown homologs of LPMOs, namely the PFAM Egh16-like family. Moreover, we show that the substrate specificities have evolved independently several times within the LPMO superfamily. Furthermore, the LPMO phylogenetic tree, does not seem to follow taxonomy-based classification.

Dynamic Brain Interactions during Picture Naming

Brain computations involve multiple processes by which sensory information is encoded and transformed to drive behavior. These computations are thought to be mediated by dynamic interactions between populations of neurons. Here, we demonstrate that human brains exhibit a reliable sequence of neural interactions during speech production. We use an autoregressive Hidden Markov Model (ARHMM) to identify dynamical network states exhibited by electrocorticographic signals recorded from human neurosurgical patients. Our method resolves dynamic latent network states on a trial-by-trial basis. We characterize individual network states according to the patterns of directional information flow between cortical regions of interest. These network states occur consistently and in a specific, interpretable sequence across trials and subjects: the data support the hypothesis of a fixed-length visual processing state, followed by a variable-length language state, and then by a terminal articulation state. This empirical evidence validates classical psycholinguistic theories that have posited such intermediate states during speaking. It further reveals these state dynamics are not localized to one brain area or one sequence of areas, but are instead a network phenomenon.

An automatic single-channel EEG-based sleep stage scoring method based on hidden Markov Model

OBJECTIVE

Sleep stage scoring is essential for diagnosing sleep disorders. Visual scoring of sleep stages is very time-consuming and prone to human errors. In this work, we introduce an efficient approach to improve the accuracy of sleep stage scoring and classification for sleep analysis.

METHOD

In this approach, a set of optimal features was first selected from a pool of features extracted from sleep EEG epochs by using a feature selection method based on the relevance and redundancy analysis. EEG segments were then classified using a random forest classifier. Finally, a Hidden Markov Model (HMM) was used to reduce false positives by incorporating knowledge of the temporal structure of transitions between sleep stages. We evaluated the proposed method using single-channel EEG signals from four public sleep EEG datasets scored according to R&K and AASM guidelines. We compared the performance of our method with existing methods using different cross validation strategies.

RESULTS

Using a leave-one-out validation strategy, our method achieved overall accuracies in the range of (79.4-87.4%) and (77.6-80.4%) with Kappa values in the range of 0.7-0.85 for six-stage (R&K) and five-stage (AASM) classification, respectively. Our method showed a reduction in overall accuracy up to 8% using the cross-dataset validation strategy in comparison with the subject cross-validation method.

COMPARISON WITH EXISTING METHOD(S)

Our method outperformed the existing methods for all multi-stage classification.

CONCLUSIONS

The proposed single-channel method can be used for robust and reliable sleep stage scoring with high accuracy and relatively low complexity required for real time applications.

Entry Aggregation and Early Match Using Hidden Markov Model of Flow Table in SDN

The usage of multiple flow tables (MFT) has significantly extended the flexibility and applicability of software-defined networking (SDN). However, the size of MFT is usually limited due to the use of expensive ternary content addressable memory (TCAM). Moreover, the pipeline mechanism of MFT causes long flow processing time. In this paper a novel approach called Agg-ExTable is proposed to efficiently manage the MFT. Here the flow entries in MFT are periodically aggregated by applying pruning and the Quine–Mccluskey algorithm. Utilizing the memory space saved by the aggregation, a front-end ExTable is constructed, keeping popular flow entries for early match. Popular entries are decided by the Hidden Markov model based on the match frequency and match probability. Computer simulation reveals that the proposed scheme is able to save about 45% of space of MFT, and efficiently decrease the flow processing time compared to the existing schemes.

Bowing Gestures Classification in Violin Performance: A Machine Learning Approach

Gestures in music are of paramount importance partly because they are directly linked to musicians' sound and expressiveness. At the same time, current motion capture technologies are capable of detecting body motion/gestures details very accurately. We present a machine learning approach to automatic violin bow gesture classification based on Hierarchical Hidden Markov Models (HHMM) and motion data. We recorded motion and audio data corresponding to seven representative bow techniques (Détaché, Martelé, Spiccato, Ricochet, Sautillé, Staccato, and Bariolage) performed by a professional violin player. We used the commercial Myo device for recording inertial motion information from the right forearm and synchronized it with audio recordings. Data was uploaded into an online public repository. After extracting features from both the motion and audio data, we trained an HHMM to identify the different bowing techniques automatically. Our model can determine the studied bowing techniques with over 94% accuracy. The results make feasible the application of this work in a practical learning scenario, where violin students can benefit from the real-time feedback provided by the system.

Symbolic Important Point Perceptually and Hidden Markov Model Based Hydraulic Pump Fault Diagnosis Method

Hydraulic pump is a driving device of the hydraulic system, always working under harsh operating conditions, its fault diagnosis work is necessary for the smooth running of a hydraulic system. However, it is difficult to collect sufficient status information in practical operating processes. In order to achieve fault diagnosis with poor information, a novel fault diagnosis method that is the based on Symbolic Perceptually Important Point (SPIP) and Hidden Markov Model (HMM) is proposed. Perceptually important point technology is firstly imported into rotating machine fault diagnosis; it is applied to compress the original time-series into PIP series, which can depict the overall movement shape of original time series. The PIP series is transformed into symbolic series that will serve as feature series for HMM, Genetic Algorithm is used to optimize the symbolic space partition scheme. The Hidden Markov Model is then employed for fault classification. An experiment involves four operating conditions is applied to validate the proposed method. The results show that the fault classification accuracy of the proposed method reaches 99.625% when each testing sample only containing 250 points and the signal duration is 0.025 s. The proposed method could achieve good performance under poor information conditions.

Complexity of Products: The Effect of Data Regularisation

Among several developments, the field of Economic Complexity (EC) has notably seen the introduction of two new techniques. One is the Bootstrapped Selective Predictability Scheme (SPSb), which can provide quantitative forecasts of the Gross Domestic Product of countries. The other, Hidden Markov Model (HMM) regularisation, denoises the datasets typically employed in the literature. We contribute to EC along three different directions. First, we prove the convergence of the SPSb algorithm to a well-known statistical learning technique known as Nadaraya-Watson Kernel regression. The latter has significantly lower time complexity, produces deterministic results, and it is interchangeable with SPSb for the purpose of making predictions. Second, we study the effects of HMM regularization on the Product Complexity and logPRODY metrics, for which a model of time evolution has been recently proposed. We find confirmation for the original interpretation of the logPRODY model as describing the change in the global market structure of products with new insights allowing a new interpretation of the Complexity measure, for which we propose a modification. Third, we explore new effects of regularisation on the data. We find that it reduces noise, and observe for the first time that it increases nestedness in the export network adjacency matrix.

Probabilistic modeling personalized treatment pathways using electronic health records

BACKGROUND

Modeling personalized treatment pathways plays an important role in understanding essential/critical treatment behaviors performed on patients during their hospitalizations and thus provides the opportunity for the improvement of better health service delivery in treatment pathways.

OBJECTIVE

Unlike traditional business process mining, modeling personalized treatment pathways is more challenging because they are typically case-specific. Although several studies have been devoted to modeling patient treatment pathways, limited efforts have been made on the extraction of latent semantics and their transitions behind patient treatment pathways, which are often ambiguous and poorly understood.

METHODS

In this article, we propose an extension of the Hidden Markov Model to mine and model personalized treatment pathways by extracting latent treatment topics and identifying their sequential dependencies in pathways, in the form of probabilistic distributions and transitions of patients' raw Electronic Health Record (EHR) data.

RESULTS

We evaluated the proposed model on 48,024 patients with cardiovascular diseases. A total of 15 treatment topics and their typical transition routes were discovered from EHR data that contained 1,391,251 treatment events with 2786 types of interventions and that were evaluated by ten clinicians manually. The obtained p-values are 0.000146 and 0.009106 in comparison with both Latent Dirichlet Allocation and Sequent Naïve Bayes models, respectively; this outcome indicate that our approach achieves a better understanding of human evaluators on modeling personalized treatment pathway than that of benchmark models.

CONCLUSION

The experimental results on a real-world data set clearly suggest that the proposed model has efficiency in mining and modeling personalized treatment pathways. We argue that the discovered treatment topics and their transition routes, as actionable knowledge that represents the practice of treating individual patients in their clinical pathways, can be further exploited to help physicians better understand their specialty and learn from previous experiences for treatment analysis and improvement.

A general method for estimating seed dormancy and colonisation in annual plants from the observation of existing flora

In plant ecology, characterising colonisation and extinction in plant metapopulations is challenging due to the non-detectable seed bank that allows plants to emerge after several years of absence. In this study, we used a Hidden Markov Model to characterise seed dormancy, colonisation and germination solely from the presence-absence of standing flora. Applying the model to data from a long-term survey of 38 annual weeds across France, we identified three homogeneous functional groups: (1) species persisting preferentially through spatial colonisation, (2) species persisting preferentially through seed dormancy and (3) a mix of both strategies. These groups are consistent with existing ecological knowledge, demonstrating that ecologically meaningful parameters can be estimated from simple presence-absence observations. These results indicate that such studies could contribute to the design of weed management strategies. They also open the possibility of testing life-history theories such as the dormancy/colonisation trade-off in natura.

METHimpute: imputation-guided construction of complete methylomes from WGBS data

BACKGROUND

Whole-genome bisulfite sequencing (WGBS) has become the standard method for interrogating plant methylomes at base resolution. However, deep WGBS measurements remain cost prohibitive for large, complex genomes and for population-level studies. As a result, most published plant methylomes are sequenced far below saturation, with a large proportion of cytosines having either missing data or insufficient coverage.

RESULTS

Here we present METHimpute, a Hidden Markov Model (HMM) based imputation algorithm for the analysis of WGBS data. Unlike existing methods, METHimpute enables the construction of complete methylomes by inferring the methylation status and level of all cytosines in the genome regardless of coverage. Application of METHimpute to maize, rice and Arabidopsis shows that the algorithm infers cytosine-resolution methylomes with high accuracy from data as low as 6X, compared to data with 60X, thus making it a cost-effective solution for large-scale studies.

CONCLUSIONS

METHimpute provides methylation status calls and levels for all cytosines in the genome regardless of coverage, thus yielding complete methylomes even with low-coverage WGBS datasets. The method has been extensively tested in plants, but should also be applicable to other species. An implementation is available on Bioconductor.

On-line diagnosis of inter-turn short circuit fault for DC brushed motor

Extensive research effort has been made in fault diagnosis of motors and related components such as winding and ball bearing. In this paper, a new concept of inter-turn short circuit fault for DC brushed motors is proposed to include the short circuit ratio and short circuit resistance. A first-principle model is derived for motors with inter-turn short circuit fault. A statistical model based on Hidden Markov Model is developed for fault diagnosis purpose. This new method not only allows detection of motor winding short circuit fault, it can also provide estimation of the fault severity, as indicated by estimation of the short circuit ratio and the short circuit resistance. The estimated fault severity can be used for making appropriate decisions in response to the fault condition. The feasibility of the proposed methodology is studied for inter-turn short circuit of DC brushed motors using simulation in MATLAB/Simulink environment. In addition, it is shown that the proposed methodology is reliable with the presence of small random noise in the system parameters and measurement.

Smart Web-Based Platform to Support Physical Rehabilitation

The enhancement of ubiquitous and pervasive computing brings new perspectives in medical rehabilitation. In that sense, the present study proposes a smart, web-based platform to promote the reeducation of patients after hip replacement surgery. This project focuses on two fundamental aspects in the development of a suitable tele-rehabilitation application, which are: (i) being based on an affordable technology, and (ii) providing the patients with a real-time assessment of the correctness of their movements. A probabilistic approach based on the development and training of ten Hidden Markov Models (HMMs) is used to discriminate in real time the main faults in the execution of the therapeutic exercises. Two experiments are designed to evaluate the precision of the algorithm for classifying movements performed in the laboratory and clinical settings, respectively. A comparative analysis of the data shows that the models are as reliable as the physiotherapists to discriminate and identify the motion errors. The results are discussed in terms of the required setup for a successful application in the field and further implementations to improve the accuracy and usability of the system.

Using HaMMLET for Bayesian Segmentation of WGS Read-Depth Data

CNV detection requires a high-quality segmentation of genomic data. In many WGS experiments, sample and control are sequenced together in a multiplexed fashion using DNA barcoding for economic reasons. Using the differential read depth of these two conditions cancels out systematic additive errors. Due to this detrending, the resulting data is appropriate for inference using a hidden Markov model (HMM), arguably one of the principal models for labeled segmentation. However, while the usual frequentist approaches such as Baum-Welch are problematic for several reasons, they are often preferred to Bayesian HMM inference, which normally requires prohibitively long running times and exceeds a typical user's computational resources on a genome scale data. HaMMLET solves this problem using a dynamic wavelet compression scheme, which makes Bayesian segmentation of WGS data feasible on standard consumer hardware.

Using Hidden Markov Models to characterise intermittent social behaviour in fish shoals

The movement of animals in groups is widespread in nature. Understanding this phenomenon presents an important problem in ecology with many applications that range from conservation to robotics. Underlying all group movements are interactions between individual animals and it is therefore crucial to understand the mechanisms of this social behaviour. To date, despite promising methodological developments, there are few applications to data of practical statistical techniques that inferentially investigate the extent and nature of social interactions in group movement. We address this gap by demonstrating the usefulness of a Hidden Markov Model approach to characterise individual-level social movement in published trajectory data on three-spined stickleback shoals (Gasterosteus aculeatus) and novel data on guppy shoals (Poecilia reticulata). With these models, we formally test for speed-mediated social interactions and verify that they are present. We further characterise this inferred social behaviour and find that despite the substantial shoal-level differences in movement dynamics between species, it is qualitatively similar in guppies and sticklebacks. It is intermittent, occurring in varying numbers of individuals at different time points. The speeds of interacting fish follow a bimodal distribution, indicating that they are either stationary or move at a preferred mean speed, and social fish with more social neighbours move at higher speeds, on average. Our findings and methodology present steps towards characterising social behaviour in animal groups.

Mathematical basis of improved protein subfamily classification by a HMM-based sequence filter

Informative phylogenetic analysis is dependent on the presence of curated and annotated sequences. This may be complemented by the simultaneous availability of empirical data pertaining to their in vivo function. Confounding sequences, with their similarity to more than one functional cluster, can therefore, render any categorization ambiguous, subjective, and imprecise. Here, I analyze and discuss the development of a mathematical expression that can characterize a potential confounding protein sequence. Specifically, statistical descriptors of combinatorially arranged profile HMM scores are computed and evaluated. The resultant data is then incorporated into an index of sequence suitability. The sequence may then be recommended as either suitable for inclusion or be excluded all together. The index is independent of experimental data and, can, be computed from the primary structure of the protein sequence. This can be utilized to trim previously grouped sequences and can either finalize the composition of training set or reduce the search space of sequences to be tested.

Discovering Event Structure in Continuous Narrative Perception and Memory

During realistic, continuous perception, humans automatically segment experiences into discrete events. Using a novel model of cortical event dynamics, we investigate how cortical structures generate event representations during narrative perception and how these events are stored to and retrieved from memory. Our data-driven approach allows us to detect event boundaries as shifts between stable patterns of brain activity without relying on stimulus annotations and reveals a nested hierarchy from short events in sensory regions to long events in high-order areas (including angular gyrus and posterior medial cortex), which represent abstract, multimodal situation models. High-order event boundaries are coupled to increases in hippocampal activity, which predict pattern reinstatement during later free recall. These areas also show evidence of anticipatory reinstatement as subjects listen to a familiar narrative. Based on these results, we propose that brain activity is naturally structured into nested events, which form the basis of long-term memory representations.