Markov Model Research Articles

Information-theoretical limit on the estimates of dissipation by molecular machines using single-molecule fluorescence resonance energy transfer experiments.

Single-molecule fluorescence resonance energy transfer (FRET) experiments are commonly used to study the dynamics of molecular machines. While in vivo molecular processes often break time-reversal symmetry, the temporal directionality of cyclically operating molecular machines is often not evident from single-molecule FRET trajectories, especially in the most common two-color FRET studies. Solving a more quantitative problem of estimating the energy dissipation/entropy production by a molecular machine from single-molecule data is even more challenging. Here, we present a critical assessment of several practical methods of doing so, including Markov-model-based methods and a model-free approach based on an information-theoretical measure of entropy production that quantifies how (statistically) dissimilar observed photon sequences are from their time reverses. The Markov model approach is computationally feasible and may outperform model free approaches, but its performance strongly depends on how well the assumed model approximates the true microscopic dynamics. Markov models are also not guaranteed to give a lower bound on dissipation. Meanwhile, model-free, information-theoretical methods systematically underestimate entropy production at low photoemission rates, and long memory effects in the photon sequences make these methods demanding computationally. There is no clear winner among the approaches studied here, and all methods deserve to belong to a comprehensive data analysis toolkit.

Cost-Utility Analysis of Bilateral Cochlear Implants for Children With Severe-to-Profound Sensorineural Hearing Loss in Taiwan.

Cochlear implants are an option for children with sensorineural hearing loss who do not benefit from hearing aids. Although bilateral cochlear implantation (CI) has been shown to enhance hearing performance and quality of life, its cost-effectiveness remains unclear. This study aimed to evaluate the cost-effectiveness of bilateral CI compared with bimodal hearing for children with sensorineural hearing loss in Taiwan from both the perspectives of patients and Taiwan's National Health Insurance Administration (TNHIA). A four-state Markov model was utilized in the study, including "use the first internal device," "use the second internal device," "use the third internal device," and "death." Health utility values were obtained from a local survey of health professionals and then adjusted by a scale to reflect both the negative impact of aging on hearing and the time needed to develop the full benefit of treatment in the earliest years of life. The cost data were derived from a caregiver survey, hospital databases, clinical experts, and the TNHIA. The incremental cost-effectiveness ratio (ICER) was calculated over the lifetime horizon and presented as cost per quality-adjusted life year (QALY) to evaluate the cost-effectiveness of simultaneous bilateral CI, sequential bilateral CI, and bimodal hearing. In addition, one-way sensitivity analyses and probabilistic sensitivity analyses were conducted to investigate the impact of uncertainty and the robustness of the model. The base-case analysis showed that children with bilateral CI gained more QALYs while incurring more costs when compared with those with bimodal hearing. From the TNHIA perspective, compared with bimodal hearing, the ICER of simultaneous bilateral CI was New Taiwan Dollars 232,662 per QALY whereas from the patient perspective, the ICER was New Taiwan Dollars 1,006,965 per QALY. Moreover, simultaneous bilateral CI dominated sequential bilateral CI from both perspectives. Compared with bimodal hearing, the ICER of sequential bilateral CI did not exceed twice the gross domestic product per capita in Taiwan from either perspective. One-way sensitivity analysis demonstrated that the utility gain of bilateral CI compared with bimodal hearing was the most impactful parameter from both perspectives. Probabilistic sensitivity analysis confirmed the robustness of the base-case analysis results. Our findings reveal that bilateral CI was cost-effective when using the threshold of one to three times the 2022 gross domestic product per capita in Taiwan from both the TNHIA and patient perspectives. Future research incorporating cost and effectiveness data from other dimensions is needed to help decision-makers assess the cost-effectiveness of bilateral CI more comprehensively.

Exposure-response modeling for nausea incidence for cotadutide using a Markov modeling approach.

Cotadutide is a dual glucagon-like peptide-1 (GLP-1)/glucagon receptor agonist. Gastrointestinal adverse effects are known to be associated with GLP-1 receptor agonism and can be mitigated through tolerance development via a gradual up-titration. This analysis aimed to characterize the relationship between exposure and nausea incidence and to optimize titration schemes. The model was developed with pooled data from cotadutide-administrated studies. Three different modeling approaches, proportional odds (PO), discrete-time Markov, and two-stage discrete-time Markov models, were employed to characterize the exposure-nausea relationship. The severity of nausea was modeled as different states (non-nausea, mild, and moderate/severe). The most appropriate model was selected to perform the covariate analysis, and the final covariate model was used to simulate the nausea event rates for various titration scenarios. The two Markov models demonstrated comparable performance and were better than the PO model. The covariate analysis was conducted with the standard Markov model for operational simplification and identified disease indications (NASH, obesity) and sex as covariates on Markov parameters. The simulations indicated that the biweekly titration with twofold dose escalation is superior to other titration schemes with a relatively low predicted nausea event rate at 600 μg (25%) and a shorter titration interval (8 weeks) to reach the therapeutic dose. The model can be utilized to optimize starting dose and titration schemes for other therapeutics in clinical trials to achieve an optimal risk-benefit balance and reach the therapeutic dose with minimal titration steps.

Modeling behavioral patterns of family violence aggressors

Background settingThe presumption that family violence will repeat and escalate is embedded in practices including risk assessment and case management. However, there is limited evidence that further episodes are inevitable, or that subsequent episodes will increase in severity. Therefore, we need to better understand temporal patterns in aggressor behavior to inform how risk is conceptualized in practice.MethodsFor a sample of 2115 family violence aggressors who came to police attention in Integrated Safety Response catchment areas in Aotearoa New Zealand, we collected information New Zealand Police routinely recorded about reported harm between 2018 and 2020. We used a hidden Markov model to estimate the latent (i.e., unmeasurable) states behind the information reported to police, and modeled aggressors’ movement between those states over time.ResultsWe identified three latent states. The first contained low or no reported harm, the second contained low probabilities of reported harm, and the third involved a high probability of reported verbal abuse and a moderate probability of reported physical violence. We identified four pathways through the latent states over the two-year follow-up period, which we called No reported harm, High reported harm, Low reported harm, and De-escalation.ConclusionsThe findings add to the body of research indicating that family violence aggressors do not inevitably repeat or escalate their harmful behavior, and that a small subset of cases account for a large proportion of reported harm. This study demonstrates how information that police routinely collect can be used to estimate aggressors’ latent behavioral states and model pathways communicating the probability that they will continue to come to police attention for family violence, contributing to improved risk assessment and practice.

A policy option towards improving efficiencies in Victorian public oral healthcare.

International oral health policy directions led by the World Health Organisation call for the inclusion of oral health within universal health coverage. The aim of this study is to perform a budget impact analysis of a policy option for a more cost-efficient oral health workforce skill-mix (dentists and oral health therapists) to provide public oral healthcare in Victoria, Australia. Two hypothetical standard care pathways were developed. A dynamic population Markov model in TreeAge software, with a time horizon of 6years. Two scenarios were modelled to determine: (1) base-case scenario: the threshold the dentist workforce could reduce per year, while achieving the same service delivery outputs, and (2) alternative scenario: the potential cost-savings for utilising an optimally cost-efficient oral health workforce skill-mix. The threshold analysis showed a minimum reduction of 13% of the dentist workforce being replaced with oral health therapists can occur without having any impact on the same service delivery outputs. Under the alternative scenario, the potential cost-savings would be AUD$1,425,037 (standard deviation 58,954). Governments and policy-decision makers should consider strategies in training, attracting, and retaining oral health therapists to achieve an optimally cost-efficient oral health workforce skill-mix when delivering public oral healthcare.

Player Activity and Load Profiling with Hidden Markov Models: A Novel Application in Rugby League

ABSTRACT Player movement in rugby league is complex, being spatiotemporal and multifaceted. Modeling this complexity to provide robust measures of player activity and load has proved difficult, with important aspects of player movement yet to be considered. These include the influence of time-varying covariates on player activity and the combination of different dimensions of player movement. Few studies have simultaneously categorized player activity into different activity states and investigated factors influencing the transition between states, or compared player activity and load profiles between matches and training. This study applied hidden Markov models (HMMs)—a data-driven, multivariate approach—to rugby league training and match GPS data to i) demonstrate how HMMs can combine multiple variables in a data-driven way to effectively categorize player movement states, ii) investigate the influence of two time-varying covariates, score difference and elapsed match time on player activity states, and iii) compare player activity and load profiles within and between training and match modalities. HMMs were fitted to player GPS, accelerometer and heart rate data of one English Super League team across 60 training sessions and 35 matches. Distinct activity states were detected for both matches and training, with transitions between states in matches influenced by score difference and elapsed time and clear differences in activity and load profiles between training and matches. HMMs can model the complexity of player movement to effectively profile player activity and load in rugby league and have the potential to facilitate new research across several sports.

Cost-Effectiveness Analysis of Hepatic Arterial Chemotherapy for Advanced Hepatocellular Carcinoma in China: A Comparative Analysis of HAIC-FO and Sorafenib.

BACKGROUND The FOHAIC-1 trial showed hepatic arterial infusion chemotherapy with infusional fluorouracil, leucovorin, and oxaliplatin (HAIC-FO) improved survival, compared with sorafenib, in patients with advanced hepatocellular carcinoma (HCC). The aim of this study was to conduct a cost-effectiveness comparison between HAIC-FO and sorafenib from the perspective of the Chinese healthcare system. MATERIAL AND METHODS The economic evaluation was conducted between July 2023 and February 2024, spanning a 10-year investment horizon. A Markov model was developed to perform a cost-effectiveness analysis of HAIC-FO vs sorafenib. Health states incorporated in the model comprised progression-free disease, progressed disease, and death. Transition probabilities were derived from data obtained from the FOHAIC-1 trial. Incremental cost-effectiveness ratio (ICER) was calculated to evaluate cost-effectiveness. Additionally, one-way and probabilistic sensitivity analyses assessed the model's robustness. RESULTS The HAIC-FO group accrued a total cost of $22,781, whereas the sorafenib group totaled $18,795. In terms of effectiveness, the HAIC-FO group achieved 1.06 quality-adjusted life years (QALYs), whereas the sorafenib group attained 0.65 QALYs. Compared with sorafenib, HAIC-FO yielded an additional 0.41 QALYs at a cost of additional $3,985, resulting in an incremental cost of $9,720 per QALY gained. The one-way sensitivity analysis revealed the final ICER remained below the willingness-to-pay (WTP) threshold of $30,492 per QALY, when considering parameter fluctuations. Additionally, probabilistic sensitivity analysis indicated a 99.8% probability that the ICER for HAIC-FO compared with sorafenib would fall below the WTP threshold. CONCLUSIONS Compared with sorafenib, HAIC-FO emerged as a cost-effective first-line treatment option for patients facing advanced HCC in China.

Cost-utility and budget impact analysis of neoadjuvant dual HER2 targeted therapy for HER2-positive breast cancer in Sri Lanka

This study aimed to assess the cost-utility and budget impact of dual to single HER2 targeted neoadjuvant therapy for HER2-positive breast cancer in Sri Lanka. A five-health state Markov model with lifetime horizon was used to assess the cost-utility of neoadjuvant trastuzumab (T) plus pertuzumab (P) or lapatinib (L) compared to single therapy of T with chemotherapy (C), in public healthcare system and societal perspectives. Input parameters were estimated using local data, network meta-analysis, published reports and literature. Costs were adjusted to year 2021 (1USD = LKR194.78). Five-year budget impact for public healthcare system was assessed. Incremental cost-effectiveness ratios in societal perspective for neoadjuvantLTC plus adjuvantT (strategy 3), neoadjuvantPTC plus adjuvantT (strategy 2), neoadjuvantLTC plus adjuvantLT (strategy 5), and neoadjuvantPTC plus adjuvantPT (strategy 4) compared to neoadjuvantTC plus adjuvantT (strategy 1) were USD2716, USD5600, USD6878, and USD12127 per QALY gained, respectively. One GDP per-capita (USD3815) was considered as the cost-effectiveness threshold for the analysis. Even though only the ICER for strategy 3 was cost-effective, uncertainty of efficacy parameter was revealed. For strategy 2 neoadjuvant PTC plus adjuvant T, a 25% reduction of neoadjuvant regimen cost was required to be cost effective for use in early HER2 positive breast cancer.

Model of yearly transition to severe trachomatous scarring and trichiasis in a cohort of women in Kongwa Tanzania

One criterion for validation of trachoma elimination is the management of Trachomatous Trichiasis (TT) after Trachoma inflammation—follicular (TF) is eliminated in children ages 1–9 years at district level. No data exist on how long countries must have dedicated TT programs, as the timeline for progression to TT from trachomatous scarring is unknown. We used eight years of longitudinal data in women in Kongwa Tanzania to model progression from no scarring (S0) through grades of scarring severity (S1–S4) to TT. Markov models were used, with age, community prevalence of TF (CPTF), and household characteristics as co-variates. Adjusted for covariates, the incidence of S1 was estimated at 4∙7% per year, and the risk increased by 26% if the CPTF was between 5–10% and by 48% if greater than 10%. The transition from S4 to TT was estimated at 2∙6% per year. Districts, even after elimination of TF, may have some communities with TF ≥ 5% and increased risk of incident scarring. Once scarring progresses to S2, further progression is not dependent on CPTF. These data suggest that, depending on the district level of scarring and degree of heterogeneity in CPTF at the time of elimination, incident TT will still be an issue for decades.

Brain-implantable needle-type CMOS imaging device enables multi-layer dissection of seizure calcium dynamics in the hippocampus

Objective: Current neuronal imaging methods mostly use bulky lenses that either impede animal behavior or prohibit multi-depth imaging. To overcome these limitations, we developed a lightweight lensless biophotonic system for neuronal imaging, enabling compact and simultaneous visualization of multiple brain layers. Approach: Our developed ‘CIS-NAIST’ device integrates a micro-CMOS image sensor, thin-film fluorescence filter, micro-LEDs, and a needle-shaped flexible printed circuit. With this device, we monitored neuronal calcium dynamics during seizures across the different layers of the hippocampus and employed machine learning techniques for seizure classification and prediction. Main results: The CIS-NAIST device revealed distinct calcium activity patterns across the CA1, molecular interlayer, and dentate gyrus. Our findings indicated an elevated calcium amplitude activity specifically in the dentate gyrus compared to other layers. Then, leveraging the multi-layer data obtained from the device, we successfully classified seizure calcium activity and predicted seizure behavior using Long Short-Term Memory and Hidden Markov models. Significance: Taken together, our ‘CIS-NAIST’ device offers an effective and minimally invasive method of seizure monitoring that can help elucidate the mechanisms of temporal lobe epilepsy.

A danish healthcare-focused economic evaluation of first-line cryoballoon ablation versus antiarrhythmic drug therapy for the treatment of paroxysmal atrial fibrillation

IntroductionThree randomised controlled trials (RCTs) have demonstrated that first-line cryoballoon pulmonary vein isolation decreases atrial tachycardia in patients with symptomatic paroxysmal atrial fibrillation (PAF) compared with antiarrhythmic drugs (AADs). The aim of this study was to develop a cost-effectiveness model (CEM) for first-line cryoablation compared with first-line AADs for the treatment of PAF. The model used a Danish healthcare perspective.MethodsIndividual patient-level data from the Cryo-FIRST, STOP AF and EARLY-AF RCTs were used to parameterise the CEM. The model structure consisted of a hybrid decision tree (one-year time horizon) and a Markov model (40-year time horizon, with a three-month cycle length). Health-related quality of life was expressed in quality-adjusted life years (QALYs). Costs and benefits were discounted at 3% per year. Model outcomes were produced using probabilistic sensitivity analysis.ResultsFirst-line cryoablation is dominant, meaning it results in lower costs (-€2,663) and more QALYs (0.18) when compared to first-line AADs. First-line cryoablation also has a 99.96% probability of being cost-effective, at a cost-effectiveness threshold of €23,200 per QALY gained. Regardless of initial treatment, patients were expected to receive ∼ 1.2 ablation procedures over a lifetime horizon.ConclusionFirst-line cryoablation is both more effective and less costly (i.e. dominant), when compared with AADs for patients with symptomatic PAF in a Danish healthcare system.

Prospective transitions in hemoglobin A1c following gestational diabetes using Multistate Markov Models.

We characterized the state-to-state transitions in postpartum A1c levels after gestational diabetes, including remaining in a state of normoglycemia or transitions between prediabetes or diabetes states of varying severity. We used data from the APPLE Cohort, a postpartum population-based cohort of individuals with gestational diabetes between 2009-2011and linked HbA1c data with up to 9 years follow-up (N=34,171). We examined maternal sociodemographic and perinatal characteristics as predictors of transitions in A1c progression using Markov multistate models. In the first-year postpartum following gestational diabetes, 45.1% of people had no-diabetes, 43.1% had prediabetes, 4.6% had controlled diabetes and 7.2% had uncontrolled diabetes. Roughly two-thirds of individuals remained in same state in the next year. Black individuals were more likely to transition from pre-diabetes to uncontrolled diabetes (aHR: 2.32 95% CI: 1.21 ,4.47) than White persons. Perinatal risk factors were associated with disease progression and lower likelihood of improvement. For example, hypertensive disorders of pregnancy were associated with a stronger transition (aHR: 2.06 95% CI: 1.39, 3.05) from prediabetes to uncontrolled diabetes. We illustrate factors associated with adverse transitions in incremental A1c stages and describe patient profiles who may warrant enhanced postpartum monitoring.

Low Resource Malay Dialect Automatic Speech Recognition Modeling Using Transfer Learning from a Standard Malay Model

Approaches to automatic speech recognition have transited from Hidden Markov Model (HMM)-based ASR to deep neural networks. The advantages of deep neural network approaches are that they can be developed quickly and perform better given large language resources. Nevertheless, dialect speech recognition is still challenging due to the limited resources. Transfer learning approaches have been proposed to improve speech recognition for low resources. In the first approach, the model is pre-trained on a large and diverse labeled dataset to learn the acoustic and language patterns from the speech signal. Then, the model parameters are updated with a new dataset, and the pre-trained model is fine-tuned on a low-resource language dataset. The fine-tuning process is usually completed by freezing the pre-trained layers and training the remaining layers of the model on the low-resource language corpus. Another approach is to use a pre-trained model to capture the compact and meaningful features as input to the encoder. Pre-training in this approach usually involves using unsupervised learning methods to train models on a corpus of large amounts of unmarked data. It enables the model to learn the general patterns and relationships between the input speech signals. This paper proposes a training recipe using transfer learning and Standard Malay models to improve automatic speech recognition for Kelantan and Sarawak Malay dialects.

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.

Composite‐observer‐based asynchronous control for hidden Markov nonlinear systems with disturbances

SummaryIn this article, an asynchronous adaptive tracking control approach is presented for a type of hidden Markov jump nonlinear systems with external disturbances. In this joint jump process model, hidden Markov model signifies the dynamics of the actual system, whereas the signal emits from the detector symbolizes the transmitted information. This leads to the phenomenon of asynchronization between the modes of the system and that of the controller. Accordingly, an asynchronous observer is developed by using the mode information from the detector to develop an asynchronous control approach. The observer contains a disturbance estimation part, to compensate the unknown external inputs. Utilizing the backstepping scheme, a strict‐feedback asynchronous tracking controller is formulated, guaranteeing that all signals within the closed‐loop system are semi‐globally uniformly ultimately bounded in probability. Finally, the validity of the presented methodology is illustrated by means of a simulation example.

Brain state dynamics differ between eyes open and eyes closed rest.

The human brain exhibits spatio-temporally complex activity even in the absence of external stimuli, cycling through recurring patterns of activity known as brain states. Thus far, brain state analysis has primarily been restricted to unimodal neuroimaging data sets, resulting in a limited definition of state and a poor understanding of the spatial and temporal relationships between states identified from different modalities. Here, we applied hidden Markov model (HMM) to concurrent electroencephalography-functional magnetic resonance imaging (EEG-fMRI) eyes open (EO) and eyes closed (EC) resting-state data, training models on the EEG and fMRI data separately, and evaluated the models' ability to distinguish dynamics between the two rest conditions. Additionally, we employed a general linear model approach to identify the BOLD correlates of the EEG-defined states to investigate whether the fMRI data could be used to improve the spatial definition of the EEG states. Finally, we performed a sliding window-based analysis on the state time courses to identify slower changes in the temporal dynamics, and then correlated these time courses across modalities. We found that both models could identify expected changes during EC rest compared to EO rest, with the fMRI model identifying changes in the activity and functional connectivity of visual and attention resting-state networks, while the EEG model correctly identified the canonical increase in alpha upon eye closure. In addition, by using the fMRI data, it was possible to infer the spatial properties of the EEG states, resulting in BOLD correlation maps resembling canonical alpha-BOLD correlations. Finally, the sliding window analysis revealed unique fractional occupancy dynamics for states from both models, with a selection of states showing strong temporal correlations across modalities. Overall, this study highlights the efficacy of using HMMs for brain state analysis, confirms that multimodal data can be used to provide more in-depth definitions of state and demonstrates that states defined across different modalities show similar temporal dynamics.

Integrated use of the CA–Markov model and the Trends.Earth module to enhance the assessment of land cover degradation

This study aims to demonstrate the potential of assessing future land cover degradation status by combining the forecasting capabilities of the Cellular-Automata and Markov chain (CA-Markov) models in Idris Selva with the land cover degradation (LCD) model in the Trends.Earth module. The study focuses on the upper Zambezi Basin (UZB) in southern Africa, which is one of the regions with high rates of land degradation globally. Landsat satellite imagery is utilised to generate historical (1993–2023) land cover and land use (LCLU) maps for the UZB, while the global European Space Agency Climate Change Initiative (ESA CCI) LCLU maps are obtained from the Trends.Earth module. The CA-Markov chain model is employed to predict future LCLU changes between 2023 and 2043. The LCD model in the Trends.Earth module in QGIS 3.32.3 is then used to assess the historical and forecasted land cover degradation status. The findings reveal that land cover degradation maps produced from local LCLU classifications provide more detailed information compared to those produced from the global ESA CCI LCLU product. Between 2023 and 2043, the UZB is predicted to experience a net reduction of approximately 3.2 million hectares of forest cover, with an average annual reduction rate of − 0.13%. In terms of land cover degradation, the UZB is forecasted to remain generally stable, with 87% and 96% of the total land cover area expected to be stable during the periods 2023–2033 and 2033–2043, respectively, relative to the base years 2023 and 2033. Reduction in forest cover due to the expansion of grassland, human settlements, and cropland is projected to drive land cover degradation, while improvements in forest cover are anticipated through the conversion of grassland and cropland into forested areas. It appears that using locally produced LCLU with high-resolution images provides better assessments of land degradation in the Trends.Earth module than using global LCLU products. By leveraging the opportunities offered by models with capacity to predict LCLU such as the CA–Markov model and the capabilities of the LCD model, as evidenced in this study, valuable forecasted information can be effectively obtained for monitoring land cover degradation. This information can then be used to implement targeted interventions that align with the objective of realising the United Nations' land degradation neutral world target by 2030.

Cost-Effectiveness of Dupilumab and Oral Janus Kinase Inhibitors for the Treatment of Moderate-to-Severe Atopic Dermatitis in Singapore.

Atopic dermatitis (AD) affects both adults and children, impacting their quality of life and productivity; however, traditional systemic treatments such as cyclosporine have limitations. Emerging novel systemic interventions, including monoclonal antibodies and Janus kinase (JAK) inhibitors, have been shown to improve patient outcomes. This study evaluates the cost-effectiveness of novel systemic interventions for moderate-to-severe AD in adults compared with the best supportive care (BSC) in Singapore. The economic evaluation used a hybrid model consisting of a decision tree and Markov model. Treatment responses at 16 weeks were based on a network meta-analysis that was developed specifically for this study. Long-term response, discontinuation rates, episodes of flares and treatment-emergent adverse events were obtained from key dupilumab, abrocitinib, baricitinib and upadacitinib trials. The study had a 5-year time horizon and considered the healthcare payer's perspective. Sensitivity and scenario analyses were performed as well. Baricitinib 4mg and 2mg have the lowest incremental cost-effectiveness ratios, at Singapore dollars (S$)60,730/quality-adjusted life-year (QALY) and S$66,842/QALY, respectively. Upadacitinib 30mg offers the highest incremental QALY gain, while baricitinib 2mg offers the least. The cost of the intervention drugs accounted for the highest proportion of the overall expenses (68-93%) for those in the maintenance state. Other influential factors within the model included (1) the incremental utility derived from intervention response; (2) the probability of achieving Eczema Area and Severity Index 75 (EASI-75) with BSC; and (3) the relative risk of achieving EASI-75 with the interventions. In a scenario where the cost of all drugs is matched to the lowest-priced drug, the top three cost-effectiveness interventions are dupilumab, upadacitinib 30mg and abrocitinib 200mg, respectively. The interventions are found to be cost-effective at their existing prices when compared with BSC. Ideally, a composite score of treatment success and quality-of-life scores ought to be included, but such data were unavailable. Future research should consider conditional discontinuation data and long-term outcomes when such data become accessible.

Research on Load Decomposition and Optimization of Intelligent Elderly Care Service Based on Heuristic and Event Detection Algorithm

Against the backdrop of the digital age, the openness, equality and interaction of the Internet economy have injected new vitality into China’s traditional industries. The application of big data technology, especially in information integration and analysis, has become a key force in promoting the sustainable and healthy development of the national economy. This study focuses on the “Internet +” environment, discusses the impact of the aging problem of community workers on home care services, and proposes an optimization scheme based on a heuristic algorithm. The heuristic algorithm, inspired by the foraging behavior of ants in nature, optimizes the route selection problem by simulating an ant colony to choose the path with a high concentration of pheromones and shows outstanding application potential in the field of home care. The accuracy of the event detection algorithm is directly related to the performance of the load decomposition algorithm, and the change point detection algorithm can effectively identify the change point of the probability distribution in the time series data, which provides important input data for unsupervised clustering. Advanced computer theory, including the Hidden Markov model (HMM) and swarm intelligence optimization algorithm, is used in this research. By comparing different swarm intelligence algorithms, we find that the standard Gray Wolf optimization (SGWO) model is better than the basic Gray Wolf optimization (BGWO) algorithm and the improved Gray Wolf optimization (DGWO) algorithm in terms of stability and output results. The SGWO model significantly improves the efficiency of the load decomposition algorithm, which has been verified in the application of the smart elderly care service platform. The platform not only supports the operation of related technologies and information products but also realizes the seamless integration of information among various subjects of elderly care services. In addition, the factor hidden in the Markov model that can be selectively activated effectively monitors equipment status in the Internet of Things environment, provides real-time monitoring of user consumption behavior and fault information and further enhances the quality and efficiency of smart elderly care services.

Idiosyncratic fixation patterns generalize across dynamic and static facial expression recognition

Facial expression recognition (FER) is crucial for understanding the emotional state of others during human social interactions. It has been assumed that humans share universal visual sampling strategies to achieve this task. However, recent studies in face identification have revealed striking idiosyncratic fixation patterns, questioning the universality of face processing. More importantly, very little is known about whether such idiosyncrasies extend to the biological relevant recognition of static and dynamic facial expressions of emotion (FEEs). To clarify this issue, we tracked observers’ eye movements categorizing static and ecologically valid dynamic faces displaying the six basic FEEs, all normalized for time presentation (1 s), contrast and global luminance across exposure time. We then used robust data-driven analyses combining statistical fixation maps with hidden Markov Models to explore eye-movements across FEEs and stimulus modalities. Our data revealed three spatially and temporally distinct equally occurring face scanning strategies during FER. Crucially, such visual sampling strategies were mostly comparably effective in FER and highly consistent across FEEs and modalities. Our findings show that spatiotemporal idiosyncratic gaze strategies also occur for the biologically relevant recognition of FEEs, further questioning the universality of FER and, more generally, face processing.