Towards user-centric BCI design: Markov chain-based user assessment for mental imagery EEG-BCIs.

Objective.While electroencephalography (EEG)-based brain-computer interfaces (BCIs) have many potential clinical applications, their use is impeded by poor performance for many users. To improve BCI performance, either via enhanced signal processing or user training, it is critical to understand and describe each user's ability to perform mental control tasks and produce discernible EEG patterns. While classification accuracy has predominantly been used to assess user performance, limitations and criticisms of this approach have emerged, thus prompting the need to develop novel user assessment approaches with greater descriptive capability. Here, we propose a combination of unsupervised clustering and Markov chain models to assess and describe user skill.Approach.Using unsupervisedK-means clustering, we segmented the EEG signal space into regions representing pattern states that users could produce. A user's movement through these pattern states while performing different tasks was modeled using Markov chains. Finally, using the steady-state distributions and entropy rates of the Markov chains, we proposed two metricstaskDistinctandrelativeTaskInconsistencyto assess, respectively, a user's ability to (i) produce distinct task-specific patterns for each mental task and (ii) maintain consistent patterns during individual tasks.Main results.Analysis of data from 14 adolescents using a three-class BCI revealed significant correlations between thetaskDistinctandrelativeTaskInconsistencymetrics and classification F1 score. Moreover, analysis of the pattern states and Markov chain models yielded descriptive information regarding user performance not immediately apparent from classification accuracy.Significance.Our proposed user assessment method can be used in concert with classifier-based analysis to further understand the extent to which users produce task-specific, time-evolving EEG patterns. In turn, this information could be used to enhance user training or classifier design.

A multilevel Bayesian Markov Chain Monte Carlo Poisson modelling of factors associated with components of antenatal care offered to pregnant women in Nigeria.

BACKGROUND: The most recent WHO guideline on antenatal care (ANC) utilization reaffirmed the necessary and compulsory care and services a pregnant woman should receive to maximize the importance and gains of ANC. While most studies focused on the time of initiation and number of ANC contacts, emphasis was rarely placed on the components of ANC offered to women. This study assessed how complete the components of ANC received by pregnant women are as a proxy for the quality of ANC services offered in Nigeria. We also assessed the clustering of the components and state-level differentials and inequalities in the components of ANC received in Nigeria. METHODS: We used nationally representative cross-sectional data from the 2018 Nigeria Demographic Health Survey. We analysed the data of 11,867 women who had at least one ANC contact during the most recent pregnancy within five years preceding the survey. The assessed components were tetanus injection, blood pressure, urine test, blood test, iron supplement, malaria intermittent preventive treatment in pregnancy (IPTp), and told about danger signs. Others are intestinal parasite drugs (IPD)intermittent and HIV/PMTCT counsel. Descriptive statistics, bivariable and multivariable multilevel Bayesian Monte Carlo Poisson models were used. RESULTS: In all, 94% had blood pressure measured, 91% received tetanus injection, had iron supplement-89%, blood test-87%, urine test-86%, IPTp-24%, danger signs-80%, HIV/PMTC-82% and IPD-22%. The overall prevalence of receiving all 9 components was 5% and highest in Ogun (24%) and lowest in Kebbi state (0.1%). The earlier the initiation of ANC, the higher the number of contacts, and the higher the quality of ANC received. Respondents with higher education have a 4% (adjusted incidence risk ratio (aIRR): 1.04, 95% credible interval (CrI): 1.01-1.09) higher risk of receiving more components of ANC relative to those with no education. The risk of receiving more ANC components was 5% (aIRRR: 1.05, 95% CI: 1.01-1.10) higher among pregnant women aged 40 to 49 years than those aged 15 to 19 years. Women who decide their healthcare utilization alone had a 2% higher risk of getting more components than those whose spouses are the only decision taker of healthcare use. Other significant factors were household wealth status, spouse education, ethnicity, place of ANC, and skill of ANC provider. Pregnant women who had their blood pressure measured were very likely to have blood and urine tests, tetanus injections, iron supplements, and HIV talks. CONCLUSIONS: Only one in every 20 pregnant women received all the 9 ANC components with wide disparities and inequalities across the background characteristics and the States of residence in Nigeria. There is a need to ensure that all pregnant women receive adequate components. Stakeholders should increase supplies, train, and create awareness among ANC providers and pregnant women in particular.

An integrate-and-fire approach to Ca2+ signaling. Part I: Renewal model.

In computational neuroscience integrate-and-fire models capture the spike generation by a subthreshold dynamics supplemented by a simple fire-and-reset rule; they allow for a numerically efficient and analytically tractable description of stochastic single cell as well as network dynamics. Stochastic spiking is also a prominent feature of Ca2+ signaling which suggests to adopt the integrate-and-fire approach for this fundamental biophysical process. The model introduced here consists of two components describing 1) activity of clusters of inositol-trisphosphate receptor channels and 2) dynamics of the global Ca2+ concentrations in the cytosol. The cluster dynamics is given in terms of a cyclic Markov chain, capturing the puff, i.e., the punctuated release of Ca2+ from intracellular stores. The cytosolic Ca2+ concentration is described by an integrate-and-fire dynamics driven by the puff current. For the cyclic Markov chain we derive expressions for the statistics of the interpuff interval, the single-puff strength and the puff current assuming constant cytosolic Ca2+. The latter condition is often well approximated because cytosolic Ca2+ varies much slower than the cluster activity does. Furthermore, because the detailed two-component model is numerically expensive to simulate and difficult to treat analytically, we develop an analytical framework to approximate the driving puff current of the stochastic cytosolic Ca2+ dynamics by a temporally uncorrelated Gaussian noise. This approximation reduces our two-component system to an integrate-and-fire model with a nonlinear drift function and a multiplicative Gaussian white noise, a model that is known to generate a renewal spike train, i.e., a point process with statistically independent interspike intervals. The model allows for fast numerical simulations, permits to derive analytical expressions for the rate of Ca2+ spiking and the coefficient of variation of the interspike interval, and to approximate the interspike interval density and the spike train power spectrum. Comparison of these statistics to experimental data is discussed.

Measuring the impact of climate change on potato production in Bangladesh using Bayesian Hierarchical Spatial-temporal modeling.

BACKGROUND: Potato is a staple food and a main crop of Bangladesh. Climate plays an important role in different crop production all over the world. Potato production is influenced by climate change, which is occurring at a rapid pace according to time and space. OBJECTIVE: The main objective of this research is to observe the variation in potato production based on the discrepancy of the variability in the spatial and temporal domains. The research is based on secondary data on potato production from different parts of Bangladesh and five major climate variables for the last 17 years ending with 2020. METHODS: Bayesian Spatial-temporal modelling for linear, analysis of variance (ANOVA), and auto-Regressive models were used to find the best-fitted model compared with the independent Error Bayesian model. The Watanabe-Akaike information criterion (WAIC) and Deviance Information Criterion (DIC) were used as the model choice criteria and the Markov Chain Monte Carlo (MCMC) method was implemented to generate information about the prior and posterior realizations. RESULTS: Findings revealed that the ANOVA model under the Spatial-temporal framework was the best model for all model choice and validation criteria. Results depict that there is a significant impact of spatial and temporal variation on potato yield rate. Besides, the windspeed does not show any influence on potato production, however, temperature, humidity, rainfall, and sunshine are important components of potato yield rate in Bangladesh. CONCLUSION: It is evident that there is a potential impact of climate change on potato production in Bangladesh. Therefore, the authors believed that the findings will be helpful to the policymakers or farmers in developing potato varieties that are resilient to climate change to ensure the United Nations Sustainable Development Goal of zero hunger.

Value of Triage Treatment Strategies to Distribute Hepatitis C Direct-Acting Antiviral Agents in an Integrated Healthcare System: A Cost-Effectiveness Analysis.

OBJECTIVES: This study aimed to assess the cost-effectiveness of fibrosis-based direct-acting antiviral treatment policies for patients with chronic hepatitis C virus at the Kaiser Permanente Mid-Atlantic States health system. METHODS: We used a Markov model to compare the lifetime costs and effects of treating patients with chronic hepatitis C virus at different stages of disease severity, or all stages simultaneously, based on a fibrosis score from the US healthcare sector perspective and societal perspective. The initial distribution of patients across fibrosis scores, the effectiveness of direct-acting antiviral therapy, and follow-up and monitoring protocols were specific to the Kaiser Permanente Mid-Atlantic States health system. Direct and indirect costs, transition probabilities, and utilities were derived from the literature. Deterministic and probabilistic sensitivity analyses were performed to assess the robustness of our results. RESULTS: The "Treat All" option was dominant from both the societal and healthcare sector perspectives. The conclusion was robust in deterministic sensitivity analysis. The range of incremental costs between the less restrictive policies was small-the difference between the "Treat F1+" and the "Treat All" option was only $111 per person. Probabilistic sensitivity analyses showed, at both the $100 000/quality-adjusted life-year and $150 000/quality-adjusted life-year thresholds, there was a 70% chance that the "Treat All" option was more cost-effective than the "Treat F1+" option. CONCLUSIONS: We found that expanded treatment access is cost-effective and, in many cases, cost saving. Although our results are primarily applicable to a regional integrated healthcare system, it offers some direction to any healthcare setting faced with resource constraints in the face of highly priced drugs.

Parameter estimation in high-intensity focused ultrasound therapy.

Hyperthermia using High-Intensity Focused Ultrasound (HIFU) is an acoustic therapy for cancer treatment. This technique consists of an increase in the temperature field of the tumor to achieve coagulative necrosis and immediate cell death. Therefore, for having a successful treatment, the physical problem requires to know several properties due to the high variability from individual to individual, or even for the same individual under different physiological conditions. This article presents a numerical simulation of hyperthermia therapy for cancer treatment using HIFU, as well as the estimation of parameters that influence the physical problem. Two mathematical models were considered to solve the forward problem. The acoustic model based on acoustic pressure performs a frequency-domain study, and the bioheat transfer model a time-dependent study. These models were solved using Comsol Multiphysics® software in a 2D-axisymmetric rectangular domain to determine the temperature field. Parameter estimation was coded in Matlab Mathworks® environment using a Bayesian approach. The Markov Chain Monte Carlo method by the Metropolis-Hastings algorithm was implemented, and the simulated temperature measurements were considered. Results suggest that specific HIFU therapy can be performed for each patient by estimating appropriate parameters for cancer treatment and provides the possibility to define procedures before and during the treatment.

CDX2 Biomarker Testing and Adjuvant Therapy for Stage II Colon Cancer: An Exploratory Cost-Effectiveness Analysis.

OBJECTIVES: Adjuvant chemotherapy is not recommended for patients with average-risk stage II (T3N0) colon cancer. Nevertheless, a subgroup of these patients who are CDX2-negative might benefit from adjuvant chemotherapy. We evaluated the cost-effectiveness of testing for the absence of CDX2 expression followed by adjuvant chemotherapy (fluorouracil combined with oxaliplatin [FOLFOX]) for patients with stage II colon cancer. METHODS: We developed a decision model to simulate a hypothetical cohort of 65-year-old patients with average-risk stage II colon cancer with 7.2% of these patients being CDX2-negative under 2 different interventions: (1) test for the absence of CDX2 expression followed by adjuvant chemotherapy for CDX2-negative patients and (2) no CDX2 testing and no adjuvant chemotherapy for any patient. We derived disease progression parameters, adjuvant chemotherapy effectiveness and utilities from published analyses, and cancer care costs from the Surveillance, Epidemiology, and End Results (SEER)-Medicare data. Sensitivity analyses were conducted. RESULTS: Testing for CDX2 followed by FOLFOX for CDX2-negative patients had an incremental cost-effectiveness ratio of $5500/quality-adjusted life-years (QALYs) compared with no CDX2 testing and no FOLFOX (6.874 vs 6.838 discounted QALYs and $89 991 vs $89 797 discounted US dollar lifetime costs). In sensitivity analyses, considering a cost-effectiveness threshold of $100 000/QALY, testing for CDX2 followed by FOLFOX on CDX2-negative patients remains cost-effective for hazard ratios of <0.975 of the effectiveness of FOLFOX in CDX2-negative patients in reducing the rate of developing a metastatic recurrence. CONCLUSIONS: Testing tumors of patients with stage II colon cancer for CDX2 and administration of adjuvant treatment to the subgroup found CDX2-negative is a cost-effective and high-value management strategy across a broad range of plausible assumptions.

Cost Utility of Lifelong Immunoglobulin Replacement Therapy vs Hematopoietic Stem Cell Transplant to Treat Agammaglobulinemia.

Importance: Lifelong immunoglobulin replacement therapy (IRT) is standard-of-care treatment for congenital agammaglobulinemia but accrues high annual costs ($30 000-$90 000 per year) and decrements to quality of life over patients' life spans. Hematopoietic stem cell transplant (HSCT) offers an alternative 1-time therapy, but has high morbidity and mortality. Objective: To evaluate the cost utility of IRT vs matched sibling donor (MSD) and matched unrelated donor (MUD) HSCT to treat patients with agammaglobulinemia in the US. Design, Setting, and Participants: This economic evaluation used Markov analysis to model the base-case scenario of a patient aged 12 months with congenital agammaglobulinemia receiving lifelong IRT vs MSD or MUD HSCT. Costs, probabilities, and quality-of-life measures were derived from the literature. Microsimulations estimated premature deaths for each strategy in a virtual cohort. One-way sensitivity and probabilistic sensitivity analyses evaluated uncertainty around parameter estimates performed from a societal perspective over a 100-year time horizon. The threshold for cost-effective care was set at $100 000 per quality-adjusted life-year (QALY). This study was conducted from 2020 across a 100-year time horizon. Exposures: Immunoglobulin replacement therapy vs MSD or MUD HSCT for treatment of congenital agammaglobulinemia. Main Outcomes and Measures: The primary outcomes were incremental cost-effectiveness ratio (ICER) expressed in 2020 US dollars per QALY gained and premature deaths associated with each strategy. Results: In this economic evaluation of patients with congenital agammaglobulinemia, lifelong IRT cost more than HSCT ($1 512 946 compared with $563 776 [MSD] and $637 036 [MUD]) and generated similar QALYs (20.61 vs 17.25 [MSD] and 17.18 [MUD]). Choosing IRT over MSD or MUD HSCT yielded ICERs of $282 166 per QALY gained over MSD and $255 633 per QALY gained over MUD HSCT, exceeding the US willingness-to-pay threshold of $100 000/QALY. However, IRT prevented at least 2488 premature deaths per 10 000 microsimulations compared with HSCT. When annual IRT price was reduced from $60 145 to below $29 469, IRT became the cost-effective strategy. Findings remained robust in sensitivity and probabilistic sensitivity analyses. Conclusions and Relevance: In the US, IRT is more expensive than HSCT for agammaglobulinemia treatment. The findings of this study suggest that IRT prevents more premature deaths but does not substantially increase quality of life relative to HSCT. Reducing US IRT cost by 51% to a value similar to IRT prices in countries implementing value-based pricing may render it the more cost-effective strategy.

SC-MEB: spatial clustering with hidden Markov random field using empirical Bayes.

Spatial transcriptomics has been emerging as a powerful technique for resolving gene expression profiles while retaining tissue spatial information. These spatially resolved transcriptomics make it feasible to examine the complex multicellular systems of different microenvironments. To answer scientific questions with spatial transcriptomics and expand our understanding of how cell types and states are regulated by microenvironment, the first step is to identify cell clusters by integrating the available spatial information. Here, we introduce SC-MEB, an empirical Bayes approach for spatial clustering analysis using a hidden Markov random field. We have also derived an efficient expectation-maximization algorithm based on an iterative conditional mode for SC-MEB. In contrast to BayesSpace, a recently developed method, SC-MEB is not only computationally efficient and scalable to large sample sizes but is also capable of choosing the smoothness parameter and the number of clusters. We performed comprehensive simulation studies to demonstrate the superiority of SC-MEB over some existing methods. We applied SC-MEB to analyze the spatial transcriptome of human dorsolateral prefrontal cortex tissues and mouse hypothalamic preoptic region. Our analysis results showed that SC-MEB can achieve a similar or better clustering performance to BayesSpace, which uses the true number of clusters and a fixed smoothness parameter. Moreover, SC-MEB is scalable to large 'sample sizes'. We then employed SC-MEB to analyze a colon dataset from a patient with colorectal cancer (CRC) and COVID-19, and further performed differential expression analysis to identify signature genes related to the clustering results. The heatmap of identified signature genes showed that the clusters identified using SC-MEB were more separable than those obtained with BayesSpace. Using pathway analysis, we identified three immune-related clusters, and in a further comparison, found the mean expression of COVID-19 signature genes was greater in immune than non-immune regions of colon tissue. SC-MEB provides a valuable computational tool for investigating the structural organizations of tissues from spatial transcriptomic data.

IFPTML Mapping of Drug Graphs with Protein and Chromosome Structural Networks vs. Pre-Clinical Assay Information for Discovery of Antimalarial Compounds.

The parasite species of genus Plasmodium causes Malaria, which remains a major global health problem due to parasite resistance to available Antimalarial drugs and increasing treatment costs. Consequently, computational prediction of new Antimalarial compounds with novel targets in the proteome of Plasmodium sp. is a very important goal for the pharmaceutical industry. We can expect that the success of the pre-clinical assay depends on the conditions of assay per se, the chemical structure of the drug, the structure of the target protein to be targeted, as well as on factors governing the expression of this protein in the proteome such as genes (Deoxyribonucleic acid, DNA) sequence and/or chromosomes structure. However, there are no reports of computational models that consider all these factors simultaneously. Some of the difficulties for this kind of analysis are the dispersion of data in different datasets, the high heterogeneity of data, etc. In this work, we analyzed three databases ChEMBL (Chemical database of the European Molecular Biology Laboratory), UniProt (Universal Protein Resource), and NCBI-GDV (National Center for Biotechnology Information-Genome Data Viewer) to achieve this goal. The ChEMBL dataset contains outcomes for 17,758 unique assays of potential Antimalarial compounds including numeric descriptors (variables) for the structure of compounds as well as a huge amount of information about the conditions of assays. The NCBI-GDV and UniProt datasets include the sequence of genes, proteins, and their functions. In addition, we also created two partitions (cassayj = caj and cdataj = cdj) of categorical variables from theChEMBL dataset. These partitions contain variables that encode information about experimental conditions of preclinical assays (caj) or about the nature and quality of data (cdj). These categorical variables include information about 22 parameters of biological activity (ca0), 28 target proteins (ca1), and 9 organisms of assay (ca2), etc. We also created another partition of (cprotj = cpj) including categorical variables with biological information about the target proteins, genes, and chromosomes. These variables cover32 genes (cp0), 10 chromosomes (cp1), gene orientation (cp2), and 31 protein functions (cp3). We used a Perturbation-Theory Machine Learning Information Fusion (IFPTML) algorithm to map all this information (from three databases) into and train a predictive model. Shannon's entropy measure Shk (numerical variables) was used to quantify the information about the structure of drugs, protein sequences, gene sequences, and chromosomes in the same information scale. Perturbation Theory Operators (PTOs) with the form of Moving Average (MA) operators have been used to quantify perturbations (deviations) in the structural variables with respect to their expected values for different subsets (partitions) of categorical variables. We obtained three IFPTML models using General Discriminant Analysis (GDA), Classification Tree with Univariate Splits (CTUS), and Classification Tree with Linear Combinations (CTLC). The IFPTML-CTLC presented the better performance with Sensitivity Sn(%) = 83.6/85.1, and Specificity Sp(%) = 89.8/89.7 for training/validation sets, respectively. This model could become a useful tool for the optimization of preclinical assays of new Antimalarial compounds vs. different proteins in the proteome of Plasmodium.

Clinical and economic impact of an alert system in primary care for the detection of patients with chronic hepatitis C.

INTRODUCTION: Prevalence of chronic hepatitis C (CHC) is higher in patients born between 1955-1975. The aim was to perform an economic evaluation of an age-based electronic health record (EHR) alert in primary care to detect patients with undiagnosed CHC and its treatment in comparison with non-use of the alert system, in Valencian Community, Spain. MATERIALS AND METHODS: Decision trees and Markov model were used to evaluate the diagnosis and progression of the disease, respectively. CHC was diagnosed by serology and viral load in seropositive subjects. Epidemiological data and diagnostic costs were extracted from public sources of the Valencian Community. Probabilities, utilities and costs of model states were obtained from the literature. The impact on mortality and hepatic complications avoided by the implementation of the alert were estimated, and efficiency was measured as an incremental cost-utility ratio (ICUR) based on quality-adjusted life years (QALYs) and the costs of both alternatives. RESULTS: The EHR alert detected 269,548 patients, of whom 1,331 had CHC (vs. 23 patients with non-alert). Over the patients' lifetime, the alert would prevent 93% of decompensated cirrhosis cases, 87% of hepatocellular carcinomas, 90% of liver transplants, and 89% of liver related deaths compared to non-use of the alert system. In addition, it would obtain an additional 3.3 QALY per patient, with an incremental cost of €10,880 and an ICUR of €3,321. CONCLUSIONS: The implementation of an age-based EHR alert in primary care to detect patients with CHC reduces hepatic complications and mortality and is an efficient strategy.

Cost-effectiveness analysis of FOLFIRINOX vs gemcitabine with nab-paclitaxel as adjuvant treatment for resected pancreatic cancer in the United States based on PRODIGE-24 and APACT trials.

BACKGROUND: Pancreatic cancer is associated with low median overall survival. Combination chemotherapy regimens FOLFIRINOX and gemcitabine with nab-paclitaxel (GemNab) are the new adjuvant treatment standards for resectable pancreatic cancer. PRODIGE-24 and APACT trials demonstrated superior clinical outcomes with FOLFIRINOX and GemNab, each vs gemcitabine monotherapy. OBJECTIVE: To evaluate the cost-effectiveness of FOLFIRINOX vs GemNab for resectable pancreatic cancer in adults from the U.S. payer perspective, in order to inform decision makers about which of these treatments is optimal. METHODS: A Markov model with 3 disease states (relapse free, progressive disease, and death) was developed. Cycle length was 1 month, and time horizon was 10 years. Transition probabilities were derived from PRODIGE-24 and APACT survival data. All cost and utility input parameters were obtained from published literature. Cost-effectiveness analysis was performed to obtain total costs, quality-adjusted life-years (QALYs), life-years (LYs), and incremental cost-effectiveness ratio (ICER). A 3% annual discount rate was applied to costs and outcomes. The effect of uncertainty on model parameters was assessed with 1-way and probabilistic sensitivity analysis (PSA). RESULTS: Our analysis estimated that the cost for FOLFIRINOX was $40,831 higher than GemNab ($99,669 vs. $58,837). Despite increased toxicity, FOLFIRINOX was associated with additional 0.18 QALYs and 0.25 LYs compared with GemNab (QALY: 1.65 vs. 1.47; LY: 2.09 vs. 1.84). The ICER for FOLFIRINOX vs GemNab was $226,841 per QALY and $163,325 per LY. FOLFIRINOX was not cost-effective at a willingness-to-pay (WTP) threshold of $200,000 per QALY, and this was confirmed by the PSA. CONCLUSIONS: Total monthly cost for FOLFIRINOX was approximately 1.7 times higher than GemNab. If the WTP threshold increases to or above $250,000 per QALY, FOLFIRINOX then becomes a cost-effective treatment option. DISCLOSURES: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. The authors have no conflicts of interest to declare.

Multi-feature data repository development and analytics for image cosegmentation in high-throughput plant phenotyping.

Cosegmentation is a newly emerging computer vision technique used to segment an object from the background by processing multiple images at the same time. Traditional plant phenotyping analysis uses thresholding segmentation methods which result in high segmentation accuracy. Although there are proposed machine learning and deep learning algorithms for plant segmentation, predictions rely on the specific features being present in the training set. The need for a multi-featured dataset and analytics for cosegmentation becomes critical to better understand and predict plants' responses to the environment. High-throughput phenotyping produces an abundance of data that can be leveraged to improve segmentation accuracy and plant phenotyping. This paper introduces four datasets consisting of two plant species, Buckwheat and Sunflower, each split into control and drought conditions. Each dataset has three modalities (Fluorescence, Infrared, and Visible) with 7 to 14 temporal images that are collected in a high-throughput facility at the University of Nebraska-Lincoln. The four datasets (which will be collected under the CosegPP data repository in this paper) are evaluated using three cosegmentation algorithms: Markov random fields-based, Clustering-based, and Deep learning-based cosegmentation, and one commonly used segmentation approach in plant phenotyping. The integration of CosegPP with advanced cosegmentation methods will be the latest benchmark in comparing segmentation accuracy and finding areas of improvement for cosegmentation methodology.

The Music of Silence: Part II: Music Listening Induces Imagery Responses.

During music listening, humans routinely acquire the regularities of the acoustic sequences and use them to anticipate and interpret the ongoing melody. Specifically, in line with this predictive framework, it is thought that brain responses during such listening reflect a comparison between the bottom-up sensory responses and top-down prediction signals generated by an internal model that embodies the music exposure and expectations of the listener. To attain a clear view of these predictive responses, previous work has eliminated the sensory inputs by inserting artificial silences (or sound omissions) that leave behind only the corresponding predictions of the thwarted expectations. Here, we demonstrate a new alternate approach in which we decode the predictive electroencephalography (EEG) responses to the silent intervals that are naturally interspersed within the music. We did this as participants (experiment 1, 20 participants, 10 female; experiment 2, 21 participants, 6 female) listened or imagined Bach piano melodies. Prediction signals were quantified and assessed via a computational model of the melodic structure of the music and were shown to exhibit the same response characteristics when measured during listening or imagining. These include an inverted polarity for both silence and imagined responses relative to listening, as well as response magnitude modulations that precisely reflect the expectations of notes and silences in both listening and imagery conditions. These findings therefore provide a unifying view that links results from many previous paradigms, including omission reactions and the expectation modulation of sensory responses, all in the context of naturalistic music listening.SIGNIFICANCE STATEMENT Music perception depends on our ability to learn and detect melodic structures. It has been suggested that our brain does so by actively predicting upcoming music notes, a process inducing instantaneous neural responses as the music confronts these expectations. Here, we studied this prediction process using EEGs recorded while participants listen to and imagine Bach melodies. Specifically, we examined neural signals during the ubiquitous musical pauses (or silent intervals) in a music stream and analyzed them in contrast to the imagery responses. We find that imagined predictive responses are routinely co-opted during ongoing music listening. These conclusions are revealed by a new paradigm using listening and imagery of naturalistic melodies.

The Music of Silence: Part I: Responses to Musical Imagery Encode Melodic Expectations and Acoustics.

Musical imagery is the voluntary internal hearing of music in the mind without the need for physical action or external stimulation. Numerous studies have already revealed brain areas activated during imagery. However, it remains unclear to what extent imagined music responses preserve the detailed temporal dynamics of the acoustic stimulus envelope and, crucially, whether melodic expectations play any role in modulating responses to imagined music, as they prominently do during listening. These modulations are important as they reflect aspects of the human musical experience, such as its acquisition, engagement, and enjoyment. This study explored the nature of these modulations in imagined music based on EEG recordings from 21 professional musicians (6 females and 15 males). Regression analyses were conducted to demonstrate that imagined neural signals can be predicted accurately, similarly to the listening task, and were sufficiently robust to allow for accurate identification of the imagined musical piece from the EEG. In doing so, our results indicate that imagery and listening tasks elicited an overlapping but distinctive topography of neural responses to sound acoustics, which is in line with previous fMRI literature. Melodic expectation, however, evoked very similar frontal spatial activation in both conditions, suggesting that they are supported by the same underlying mechanisms. Finally, neural responses induced by imagery exhibited a specific transformation from the listening condition, which primarily included a relative delay and a polarity inversion of the response. This transformation demonstrates the top-down predictive nature of the expectation mechanisms arising during both listening and imagery.SIGNIFICANCE STATEMENT It is well known that the human brain is activated during musical imagery: the act of voluntarily hearing music in our mind without external stimulation. It is unclear, however, what the temporal dynamics of this activation are, as well as what musical features are precisely encoded in the neural signals. This study uses an experimental paradigm with high temporal precision to record and analyze the cortical activity during musical imagery. This study reveals that neural signals encode music acoustics and melodic expectations during both listening and imagery. Crucially, it is also found that a simple mapping based on a time-shift and a polarity inversion could robustly describe the relationship between listening and imagery signals.

Surveillance for gestational trophoblastic neoplasia following molar pregnancy: a cost-effectiveness analysis.

BACKGROUND: Historically, published guidelines for care after molar pregnancy recommended monitoring human chorionic gonadotropin levels for the development of gestational trophoblastic neoplasia until normal and then for 6 months after the first normal human chorionic gonadotropin. However, there are little data underlying such recommendations, and recent evidence has demonstrated that gestational trophoblastic neoplasia diagnosis after human chorionic gonadotropin normalization is rare. OBJECTIVE: We sought to estimate the cost-effectiveness of alternative strategies for surveillance for gestational trophoblastic neoplasia after human chorionic gonadotropin normalization after complete and partial molar pregnancy. STUDY DESIGN: A Markov-based cost-effectiveness model, using monthly cycles and terminating after 36 months/cycles, was constructed to compare alternative strategies for asymptomatic human chorionic gonadotropin surveillance after the first normal (none; monthly testing for 1, 3, 6, and 12 months; or every 3-month testing for 3, 6, and 12 months) for both complete and partial molar pregnancy. The risk of reduced surveillance was modeled by increasing the probability of high-risk disease at diagnosis. Probabilities, costs, and utilities were estimated from peer-reviewed literature, with all cost data applicable to the United States and adjusted to 2020 US dollars. The primary outcome was cost per quality-adjusted life year ($/quality-adjusted life year) with a $100,000/quality-adjusted life year willingness-to-pay threshold. RESULTS: Under base-case assumptions, we found no further surveillance after the first normal human chorionic gonadotropin to be the dominant strategy from both the healthcare system and societal perspectives, for both complete and partial molar pregnancy. After complete mole, this strategy had the lowest average cost (healthcare system, $144 vs maximum $283; societal, $152 vs maximum $443) and highest effectiveness (2.711 vs minimum 2.682 quality-adjusted life years). This strategy led to a slightly higher rate of death from gestational trophoblastic neoplasia (0.013% vs minimum 0.009%), although with high costs per gestational trophoblastic neoplasia death avoided (range, $214,000 to >$4 million). Societal perspective costs of lost wages had a greater impact on frequent surveillance costs than rare gestational trophoblastic neoplasia treatment costs, and no further surveillance was more favorable from this perspective in otherwise identical analyses. No further surveillance remained dominant or preferred with incremental cost-effectiveness ratio of <$100,000 in all analyses for partial mole, and most sensitivity analyses for complete mole. Under the assumption of no disutility from surveillance, surveillance strategies were more effective (by quality-adjusted life year) than no further surveillance, and a single human chorionic gonadotropin test at 3 months was found to be cost-effective after complete mole with incremental cost-effectiveness ratio of $53,261 from the healthcare perspective, but not from the societal perspective (incremental cost-effectiveness ratio, $288,783). CONCLUSION: Largely owing to the rare incidence of gestational trophoblastic neoplasia after human chorionic gonadotropin normalization after molar pregnancy, prolonged surveillance is not cost-effective under most assumptions. It would be reasonable to reduce, and potentially eliminate, current recommendations for surveillance after human chorionic gonadotropin normalization after molar pregnancy, particularly among partial moles. With any reduction in surveillance, patients should be counseled on symptoms of gestational trophoblastic neoplasia and established in routine gynecologic care.

Health and economic impact of the correct diagnosis of transthyretin cardiac amyloidosis in Spain.

Objective: To estimate the health and economic impact of the reduction in mortality and cardiovascular hospitalizations, associated with correct diagnosis of cardiac transthyretin amyloidosis (ATTR-CM), from the Spanish National Health System (NHS) perspective.Methods: A costs and effects analysis were performed (probabilistic Markov model) with time horizons between 1 and 15 years, comparing the correct diagnosis of ATTR-CM versus the non-diagnosis. Transition probabilities were obtained from the ATTR-ACT study (placebo arm) and from the literature. Costs and healthcare resources were obtained from Spanish sources (€ 2019) and from a panel of Spanish clinical experts.Results: After 1, 5, 10 and 15 years, the diagnosis of ATTR-CM would generate a gain of 0.031 (95%CI 0.025; 0.038); 0.387 (95%CI 0.329; 0.435); 0.754 (95%CI 0.678; 0.781) and 0.944 (95%CI 0.905; 0.983) life years per patient, respectively, with savings of € 212 (95%CI € -632; 633), € 2,289 (95%CI € 2,250; 2,517), € 2,859 (95%CI € 2,584; 3,149) and € 2,906 (95%CI € 2,669; 3,450) per patient, respectively, versus the non-diagnosis.Conclusions: Just by correctly diagnosing ATTR-CM, years of life would be gained, cardiovascular hospitalizations would be avoided, and savings would be generated for the NHS, compared to the non-diagnosis of the disease.

Permeability prediction in the beyond-Rule-of 5 chemical space: Focus on cyclic hexapeptides.

Cyclic peptides (CPs) are gaining more and more relevance in drug discovery. Since one of their main drawbacks is poor permeability, the discovery of new orally available CP drugs requires computational tools that predict CP permeability in very early drug discovery. In this study we used a literature dataset of 62 cyclic hexapeptides to evaluate the performances of a number of in silico tools based on different computational theory to model and rationalize PAMPA and Caco-2 permeability values. In particular, we submitted the dataset to a) online calculators, b) QSPR strategies, c) a physics-based tool, d) a mixed approach and e) a kinetic method. This latter is an emergent strategy in which a few relevant conformations retrieved from a set of molecular dynamics (MD) simulations by the Markov State Model (MSM) are used to establish the compounds permeability. Both free and commercial software were used. Results were compared with a model based on experimental physicochemical descriptors. All the computational approaches but online calculators performed quite well and show that lipophilicity and not polarity is the main determinant of the investigated event. A second major outcome of the study is that the impact of flexibility on the permeability of the considered dataset cannot be unambiguously assessed. Finally, our comparative analysis, which also included not common applied strategies, allowed a sound evaluation of the pros and cons of the applied computational approaches.

Effects of different aerobic exercise training on glycemia in patients with type 2 diabetes: A protocol for systematic review and meta analysis.

BACKGROUND: Type 2 diabetes is an emergent worldwide health crisis, and rates are growing globally. Aerobic exercise is an essential measure for patients with diabetes, which has the advantages of flexible time and low cost. Aerobic exercise is a popular method to reduce blood glucose. Due to the lack of randomized trials to compare the effects of various aerobic exercises, it is difficult to judge the relative efficacy. Therefore, we intend to conduct a network meta-analysis to evaluate these aerobic exercises. METHODS: According to the retrieval strategies, randomized controlled trials on different aerobic exercise training will be obtained from China National Knowledge Infrastructure, WanFang, SinoMed, PubMed, Web of Science, EMBASE, and Cochrane Library, regardless of publication date or language. Studies were screened based on inclusion and exclusion criteria, and the Cochrane risk bias assessment tool will be used to evaluate the quality of the literature. The network meta-analysis will be performed in Markov Chain Monte Carlo method and carried out with Stata14 and OpenBUGS software. Ultimately, the evidentiary grade for the results will be evaluated. RESULTS: Eighteen literatures with a total of 1134 patients were included for the meta-analysis. In glycemia assessment, Tennis (standard mean difference = 3.59, credible interval 1.52, 5.65), had significantly better effects than the named control group. Tennis (standard mean difference = 3.50, credible interval 1.05, 5.59), had significantly better effects than the named Taiji group. CONCLUSION: All together, these results suggest that tennis may be the best way to improve blood glucose in patients with type 2 diabetes. This study may provide an excellent resource for future control glycemia and may also serve as a springboard for creative undertakings as yet unknown.

Modelling the Cost-Effectiveness of Implementing a Dietary Intervention in Renal Transplant Recipients.

BACKGROUND: The Dietary Approach to Stop Hypertension (DASH) and potassium supplementation have been shown to reduce the risk of death with a functioning graft (DWFG) and renal graft failure in renal transplant recipients (RTR). Unfortunately, a key problem for patients is the adherence to these diets. The aim of this study is to evaluate the cost-effectiveness and budget impact of higher adherence to either the DASH or potassium supplementation. METHODS: A Markov model was used to simulate the life course of 1000 RTR in the Netherlands. A societal perspective with a lifetime time horizon was used. The potential effect of improvement of dietary adherence was modelled in different scenarios. The primary outcomes are the incremental cost-effectiveness ratio (ICER) and the budget impact. RESULTS: In the base case, improved adherence to the DASH diet saved 27,934,786 and gained 1880 quality-adjusted life years (QALYs). Improved adherence to potassium supplementation saved €1,217,803 and gained 2901 QALYs. Both resulted in dominant ICERs. The budget impact over a five-year period for the entire Dutch RTR population was €8,144,693. CONCLUSION: Improving dietary adherence in RTR is likely to be cost-saving and highly likely to be cost-effective compared to the current standard of care in the Netherlands.