Model Data Research Articles

Deep learning based automatic detection and dipole estimation of epileptic discharges in MEG: a multi-center study.

Magnetoencephalography (MEG) provides crucial information in diagnosing focal epilepsy. However, dipole estimation, a commonly used analysis method for MEG, can be time-consuming since it necessitates neurophysiologists to manually identify epileptic spikes. To reduce this burden, we developed the automatic detection of spikes using deep learning in single center. In this study, we performed a multi-center study using six MEG centers to improve the performance of the automated detection of neuromagnetically recorded epileptic spikes, which we previously developed using deep learning. Data from four centers were used for training and evaluation (internal data), and the remaining two centers were used for evaluation only (external data). We used a five-fold subject-wise cross-validation technique to train and evaluate the models. A comparison showed that the multi-center model outperformed the single-center model in terms of performance. The multi-center model achieved an average ROC-AUC of 0.9929 and 0.9426 for the internal and external data, respectively. The median distance between the neurophysiologist-analyzed and automatically analyzed dipoles was 4.36 and 7.23mm for the multi-center model for internal and external data, respectively, indicating accurate detection of epileptic spikes. By training data from multiple centers, automated analysis can improve spike detection and reduce the analysis workload for neurophysiologists. This study suggests that the multi-center model has the potential to detect spikes within 1cm of a neurophysiologist's analysis. This multi-center model can significantly reduce the number of hours required by neurophysiologists to detect spikes.

A neural network-based ARMA model for fuzzy time series data

A neural network-based ARMA model for fuzzy time series data

Functional excitation-inhibition ratio indicates near-critical oscillations across frequencies

Abstract The concept of excitation/inhibition (E/I) balance plays an important role in understanding brain function in health and disease. We recently introduced an algorithm to determine a functional E/I ratio based on the critical brain dynamics that emerge in neuronal networks balancing between order and disorder. Little, however, is known about the frequency specificity of E/I regulation and how to measure it. Here, we optimized the algorithm for measuring functional excitation-inhibition ratio (fE/I) in narrow frequency ranges and validated it on a computational model of critical oscillations and EEG data. In the computational model, we confirmed that fE/I discriminated E/I connectivity differences across a wide range of frequencies (1–150 Hz). Twin EEG data revealed significant genetic influences on fE/I across frequencies, whereas contrasting eyes-open and -closed EEG indicated functional changes of fE/I restricted to a subset of alpha and beta oscillations and brain regions. We propose that assessing fE/I with finer frequency resolution will prove useful for understanding the functional role of E/I regulation in a spectrally refined fashion in health and disease.

The impact of digital industrialization and industrial digitalization on regional green innovation efficiency in China -From the perspective of the innovation value chain

To systematically detect the influence of digital economy on regional green innovation, this study regards regional green innovation as a two-stage sequential process, and constructs a network SBM model to evaluate the overall and stage green innovation efficiency of 30 provinces in China from 2011 to 2020. Digital economy development is decomposed into digital industrialization and industrial digitalization, and an empirical study based on fixed effects regression model of panel data is conducted to determine their influence on the overall and stage green innovation efficiency. The results suggest the following: (1) The overall efficiency in China remained at a low level, with the efficiency of research and development being slightly higher than that of achievement transformation. (2) From the national perspective, there is no discernible effect of digital industrialization on overall efficiency, but there is a negative impact on research and development and a significant positive impact on achievement transformation. Industrial digitalization significantly improves achievement transformation and overall efficiency but has no discernible effect on the research and development stage. (3) In the eastern region, industrial digitalization promotes the improvement of both stages and overall efficiency, while digital industrialization does not have a significant impact. In the central region, digital industrialization and industrial digitalization have significant positive impacts on both overall efficiency and the efficiency of achievement transformation but have no impact on research and development. In the western region, digital industrialization inhibits the overall and achievement transformation efficiency, and industrial digitalization does not have significant impact on the overall efficiency or the efficiency of each stage. Finally, some relevant policy recommendations are proposed.

Geographically weighted accelerated failure time model for spatial survival data: application to ovarian cancer survival data in New Jersey

BackgroundIn large multiregional cohort studies, survival data is often collected at small geographical levels (such as counties) and aggregated at larger levels, leading to correlated patterns that are associated with location. Traditional studies typically analyze such data globally or locally by region, often neglecting the spatial information inherent in the data, which can introduce bias in effect estimates and potentially reduce statistical power.MethodWe propose a Geographically Weighted Accelerated Failure Time Model for spatial survival data to investigate spatial heterogeneity. We establish a weighting scheme and bandwidth selection based on quasi-likelihood information criteria. Theoretical properties of the proposed estimators are thoroughly examined. To demonstrate the efficacy of the model in various scenarios, we conduct a simulation study with different sample sizes and adherence to the proportional hazards assumption or not. Additionally, we apply the proposed method to analyze ovarian cancer survival data from the Surveillance, Epidemiology, and End Results cancer registry in the state of New Jersey.ResultsOur simulation results indicate that the proposed model exhibits superior performance in terms of four measurements compared to existing methods, including the geographically weighted Cox model, when the proportional hazards assumption is violated. Furthermore, in scenarios where the sample size per location is 20-25, the simulation data failed to fit the local model, while our proposed model still demonstrates satisfactory performance. In the empirical study, we identify clear spatial variations in the effects of all three covariates.ConclusionOur proposed model offers a novel approach to exploring spatial heterogeneity of survival data compared to global and local models, providing an alternative to geographically weighted Cox regression when the proportional hazards assumption is not met. It addresses the issue of certain counties' survival data being unable to fit the model due to limited samples, particularly in the context of rare diseases.

A spatial Durbin model for interval-valued data with t-distribution

Interval-valued data have received much attention due to their wide applications in finance, econometrics, meteorology and medicine. However, most regression models developed for interval-valued data assume that the observations are mutually independent, which can not be adapted to scenarios where individuals are spatially correlated. We propose a new linear model to account for areal-type spatial dependencies present in interval-valued data. Specifically, two spatial Durbin models are separately built for the centers and log-ranges of the interval-valued response. To make the new model more robust to heavy-tailed noise, we assume the error term follows a t-distribution. The parameters are obtained using an expectation-maximization (EM) algorithm. Numerical experiments are designed to examine the performance of the proposed method. We also use a weather dataset to demonstrate the usefulness of our model.

Conditional modeling of recurrent event data with terminal event.

Recurrent event data with a terminal event arise in follow-up studies. The current literature has primarily focused on the effect of covariates on the recurrent event process using marginal estimating equation approaches or joint modeling approaches via frailties. In this article, we propose a conditional model for recurrent event data with a terminal event, which provides an intuitive interpretation of the effect of the terminal event: at an early time, the rate of recurrent events is nearly independent of the terminal event, but the dependence gets stronger as time goes close to the terminal event time. A two-stage likelihood-based approach is proposed to estimate parameters of interest. Asymptotic properties of the estimators are established. The finite-sample behavior of the proposed method is examined through simulation studies. A real data of colorectal cancer is analyzed by the proposed method for illustration.

Bayesian analysis of Box-Cox transformation model for multi-state progression-free survival data

Bayesian analysis of Box-Cox transformation model for multi-state progression-free survival data

The Standardized Spectroscopic Mixture Model

The standardized spectral mixture model combines the specificity of a physically based representation of a spectrally mixed pixel with the generality and portability of a spectral index. Earlier studies have used spectrally and geographically diverse collections of broadband and spectroscopic imagery to show that the reflectance of the majority of ice-free landscapes on Earth can be represented as linear mixtures of rock and soil substrates (S), photosynthetic vegetation (V) and dark targets (D) composed of shadow and spectrally absorptive/transmissive materials. However, both broadband and spectroscopic studies of the topology of spectral mixing spaces raise questions about the completeness and generality of the Substrate, Vegetation, Dark (SVD) model for imaging spectrometer data. This study uses a spectrally diverse collection of 40 granules from the EMIT imaging spectrometer to verify the generality and stability of the spectroscopic SVD model and characterize the SVD topology and plane of substrates to assess linearity of spectral mixing. New endmembers for soil and non-photosynthetic vegetation (NPV; N) allow the planar SVD model to be extended to a tetrahedral SVDN model to better accommodate the 3D topology of the mixing space. The SVDN model achieves smaller misfit than the SVD, but does so at the expense of implausible fractions beyond [0, 1]. However, a refined spectroscopic SVD model still achieves small (<0.03) RMS misfit, negligible sensitivity to endmember variability and strongly linear scaling over more than an order of magnitude range of spatial resolution.

Incomplete ordinal panel responses with initial condition and skew-normal random effect

This paper presents a model for the ordinal categorical panel data with skew-normal random effects, endogenous initial condition, and non ignorable (NI) non monotone missing data. We assumed three separate models for the initial condition, subsequent responses, and missing procedure. The baseline or initial response variable and subsequent response variables are modeled as ordinal logistic, whereas the logistic binary model is considered for the missing process where the maximum likelihood estimates are obtained using Monte Carlo methods. The results show that in the presence of missing data, the skew-normal random effects produce better results compared to the normal one, and considering the endogenous initial conditions is effective in modeling the results of the observed and missing responses such that the patients whose severity of schizophrenia is higher at the beginning of the process have a higher chance of missing on the subsequence times. In this research, schizophrenia data and then simulation studies have been used to check the performance of the proposed model. The results show that the skew-normal random effect using the NI non monotone missing mechanism by increasing the skewness value produces better results in the analysis of simulation studies.

Determination of Moisture Sorption Isotherm using Brunauer-Emmett-Teller (BET) and Guggenheim-Anderson-de Boer (GAB) Models, Germination Capacity and Index Vigor of Soybean during Storage

Understanding the interaction between the equilibrium moisture content of a product and the surrounding air is crucial for guaranteeing product quality and preserving its characteristics throughout storage. The study of this tendency may be conducted utilizing sorption isotherms. This research aimed to study the moisture sorption characteristics of soybeans during storage, identify the most suitable mathematical model for the experimental data, and examine the relationship between the germination capacity and vigor index of soybeans with their moisture content and water activity. Moisture sorption characteristics of soybean, Anjasmara variety, were investigated at temperatures of 30ᵒC, 40ᵒC, and 50ᵒC, and water activities of 0.08 to 0.80, and were modelled using two different mathematical models, Brunauer-Emmett-Teller (BET) and Guggenheim-Anderson-de Boer (GAB). Both two mathematical models, BET and GAB, were found to effectively describe the moisture sorption data based on regression analyses and goodness of fit. Each model was statistically evaluated by coefficient of determination and deviation standard. The moisture sorption behaviour of soybean seeds displayed a sigmoidal pattern. The GAB models have been suggested to estimate the moisture content at certain humidity levels at 30ᵒC, 40ᵒC, and 50ᵒC temperatures. The BET models were suitable for temperatures between 30ᵒC and 40oC. The vigor and germination indicated exhibited a progressive and optimal trend at a water activity level of 0.32, followed by a decline at all three temperatures.

CGC and saturation approach: Impact-parameter dependent model of perturbative QCD and combined HERA data

In this paper we confront the color glass condensate and saturation approach of Contreras [Non-linear equation in the re-summed next-to-leading order of perturbative QCD: The leading twist approximation, ] with the experimental combined HERA data and obtain its parameters. The model includes two features that are in accordance with our theoretical knowledge of deep inelastic scattering. These consist of: (i) the use of analytical solution for the nonlinear Balitsky-Kovchegov evolution equation and () the exponential behavior of the saturation momentum on the impact parameter b-dependence, characterized by Qs∝exp(−mb) which reproduces the correct behavior of the scattering amplitude at large b in accord with Froissart theorem. The model results are then compared to data at small-x for the structure function of the proton F2, the longitudinal structure function FL, the charm structure function F2cc¯, the exclusive vector meson (J/ψ,ϕ,ρ) production and deeply virtual Compton scattering. We obtain a good agreement for the processes in a wide kinematic range of Q2 at small-x. Our results provide a strong guide for finding an approach, based on color glass condensate and saturation effective theory for high-energy QCD, to make reliable predictions from first principles and for forthcoming experiments like the Electron Ion Collider and the LHeC. Published by the American Physical Society 2024

Derivation of outcome-dependent dietary patterns for low-income women obtained from survey data using a supervised weighted overfitted latent class analysis.

Poor diet quality is a key modifiable risk factor for hypertension and disproportionately impacts low-income women. Analyzing diet-driven hypertensive outcomes in this demographic is challenging due to the complexity of dietary data and selection bias when the data come from surveys, a main data source for understanding diet-disease relationships in understudied populations. Supervised Bayesian model-based clustering methods summarize dietary data into latent patterns that holistically capture relationships among foods and a known health outcome but do not sufficiently account for complex survey design. This leads to biased estimation and inference and lack of generalizability of the patterns. To address this, we propose a supervised weighted overfitted latent class analysis (SWOLCA) based on a Bayesian pseudo-likelihood approach that integrates sampling weights into an exposure-outcome model for discrete data. Our model adjusts for stratification, clustering, and informative sampling, and handles modifying effects via interaction terms within a Markov chain Monte Carlo Gibbs sampling algorithm. Simulation studies confirm that the SWOLCA model exhibits good performance in terms of bias, precision, and coverage. Using data from the National Health and Nutrition Examination Survey (2015-2018), we demonstrate the utility of our model by characterizing dietary patterns associated with hypertensive outcomes among low-income women in the United States.

Systematic review of clinical prediction models for psychosis in individuals meeting At Risk Mental State criteria.

This study aims to review studies developing or validating a prediction model for transition to psychosis in individuals meeting At Risk Mental State (ARMS) criteria focussing on predictors that can be obtained as part of standard clinical practice. Prediction of transition is crucial to facilitating identification of patients who would benefit from cognitive behavioural therapy and, conversely, those that would benefit from less costly and less-intensive regular mental state monitoring. The review aims to determine whether prediction models rated as low risk of bias exist and, if not, what further research is needed within the field. Bibliographic databases (PsycINFO, Medline, EMBASE, CINAHL) were searched using index terms relating to the clinical field and prognosis from 1994, the initial year of the first prospective study using ARMS criteria, to July 2024. Screening of titles, abstracts, and subsequently full texts was conducted by two reviewers independently using predefined criteria. Study quality was assessed using the Prediction model Risk Of Bias ASessment Tool (PROBAST). Studies in any setting were included. The primary outcome for the review was the identification of prediction models considering transition risk and a summary of their risk of bias. Forty-eight unique prediction models considering risk of transition to psychosis were identified. Variables found to be consistently important when predicting transition were age, gender, global functioning score, trait vulnerability, and unusual thought content. PROBAST criteria categorised four unique prediction models as having an overall low-risk bias. Other studies were insufficiently powered for the number of candidate predictors or lacking enough information to draw a conclusion regarding risk of bias. Two of the 48 identified prediction models were developed using current best practice statistical methodology, validated their model in independent data, and presented low risk of bias overall in line with the PROBAST guidelines. Any new prediction model built to evaluate the risk of transition to psychosis in people meeting ARMS criteria should be informed by the latest statistical methodology and adhere to the TRIPOD reporting guidelines to ensure that clinical practice is informed by the best possible evidence. External validation of such models should be carefully planned particularly considering generalisation across different countries. https://www.crd.york.ac.uk/PROSPEROFILES/108488_PROTOCOL_20191127.pdf, identifier CRD42018108488.

The impact of environmental shocks due to climate change on intimate partner violence: A structural equation model of data from 156 countries

The impact of climate change on human societies is now well recognised. However, little is known about how climate change alters health conditions over time. National level data around climate shocks and subsequent rates of intimate partner violence (IPV) could have relevance for resilience policy and programming. We hypothesise that climate shocks are associated with a higher national prevalence of IPV two years following a shock, and that this relationship persists for countries with different levels of economic development. We compiled national data for the prevalence of IPV from 363 nationally representative surveys from 1993 to 2019. These representative data from ever-partnered women defined IPV incidence as any past-year act of physical and/or sexual violence. We also compiled data from the Emergency Events Database (EM DAT) on the national frequency of eight climate shocks from 1920 to 2022 within 190 countries. Using exploratory factor analysis, we fit a three-factor latent variable composed of climate shock variables. We then fit a structural equation model from climate shocks (lagged by two years) and IPV incidence, controlling for (log) national gross domestic product (GDP). National data representing 156 countries suggest a significant relationship between IPV and a climate factor (Hydro-meteorological) composed of storms, landslides and floods (standardised estimate = 0·32; SE = 0·128; p = 0·012). GDP has a moderately large cross-sectional association with IPV (estimate = -0·529; SE = 0·047; p = 0·0001). Other climate shocks (Geological: earthquakes/volcanos; Atmospheric: wildfire/droughts/extreme temperature) had no measurable association with IPV. Model fit overall was satisfactory (RMSEA = 0·064 (95%CI: 0·044–0·084); CFI = 0·91; SRMR = 0·063). Climate shocks have a longitudinal association with IPV incidence in global population-based data. This suggests an urgent need to address the higher prevalence of IPV likely to come about through climate shocks due to climate change. Our analysis offers one way policy makers could track national progress using existing data.

Bayesian feedback in the framework of ecological sciences

In ecological studies, it is not uncommon to encounter scenarios where the same phenomenon (e.g., species occurrence, species abundance) is observed using two different types of samplers. For example, species data can be collected from scientific sampling with a completely random sample pattern, but also from opportunistic sampling (e.g., whale watching from commercial fishing vessels or bird watching from citizen science), where observers tend to look for particular species in areas where they expect to find them.Species Distribution Models (SDMs) are widely used tools for analysing this type of ecological data. In particular, two models are available for the aforementioned data: a geostatistical model (GM) for data collected where the sampling design is not directly related to the observations, and a preferential model (PM) for data obtained from opportunistic sampling.The integration of information from disparate sources can be addressed through the use of expert elicitation and integrated models. This paper focuses on a sequential Bayesian procedure for linking two models by updating prior distributions. The Bayesian paradigm is implemented together with the integrated nested Laplace approximation (INLA) methodology, which is an effective approach for making inference and predictions in spatial models with high performance and low computational cost. This sequential approach has been evaluated through the simulation of various scenarios and the subsequent comparison of the results from sharing information between models using a variety of criteria. The procedure has also been exemplified on a real dataset.The primary findings indicate that, in general, it is preferable to transfer information from the independent (with a completely random sampling) model to the preferential model rather than in the alternative direction. However, this depends on several factors, including the spatial range and the spatial arrangement of the sampling locations.

Transoral snare removal of subepiglottic cysts.

The objective of this demonstration was to describe and simulate a surgical technique for removing subepiglottic cysts in horses via an oral approach under endoscopic guidance using a cadaver model for simulation and clinical data. The technique aims to provide a minimally invasive method for the removal of these cysts. 2 clinical case images from the Washington State University Teaching Hospital were used in the video. In a third horse euthanized for reasons unrelated to the study, the procedure was simulated after the head was frozen and transected transversally. Files from 2 horses were reviewed, and the relevant parts were selected. The horse head was set on a stand, and a simulated cyst was implanted under the mucosa, made of the fingertip from a glove filled with carboxymethyl cellulose gel, and sutured. The procedure was performed by one of the authors (CAR) and recorded. A bronchoesophageal grasping forceps and cautery snare were used to simulate a clinical situation in dorsal recumbency. The cyst was successfully removed as in the real procedure, and the demonstration was recorded for educational use. The review of the videos shows that the simulated procedure is also achievable in a real clinical environment. This technique provides a minimally invasive method for the removal of subepiglottic cysts in horses. The demonstration of this procedure is crucial for the training of surgeons, as it allows the visualization of the procedure in a controlled setting, free from the complications of real clinical situations.

Prioritising Conservation Area in Species Management Strategy for The Edible Bornean Giant River Frog Limnonectes leporinus Anderson 1923.

Limnonectes leporinus, the endemic giant river frog, is a riparian (stream dwelling) species that lives along streams with moderate to steep gradients. The most serious threats to the species are deforestation caused by severe clear cutting, which fragments its distribution, and overhunting for local consumption. Excessive landscape modification alters habitat, making it critical for an organism to maintain heterozygozity for the population to be fit to adapt to a changing environment. The goal of this research was to project suitable habitats and predict the potential for habitat connectivity to allow gene flow across the Sarawak landscape. The presence data file and environmental layers were converted into ASCII format using ArcGIS and then used in MaxEnt modelling to generate the map of suitable habitats. To perform the connectivity model, the potential habitat model and genetic attributes of haplotype data were computerised in Circuitscape software. The findings revealed that suitable habitats corresponded to species distribution in lowland areas with sustainable stream networks as breeding sites, while higher elevations were identified as unsuitable habitats. Gene flow data, on the other hand, revealed that connectivity circuits are mostly found in unprotected forest, which includes development areas and private lands. As a result, prioritising conservation areas should include local governments and landowners in proper landscape management as well as species management strategies. This indirectly sustains and protects Borneo's forests, flora and fauna.

Predicting the outcome of psychotherapy for chronic depression by person-specific symptom networks.

Psychotherapies are efficacious in the treatment of depression, albeit only with a moderate effect size. It is hoped that personalization of treatment can lead to better outcomes. The network theory of psychopathology offers a novel approach suggesting that symptom interactions as displayed in person-specific symptom networks could guide treatment planning for an individual patient. In a sample of 254 patients with chronic depression treated with either disorder-specific or non-specific psychotherapy for 48 weeks, we investigated if person-specific symptom networks predicted observer-rated depression severity at the end of treatment and one and two years after treatment termination. Person-specific symptom networks were constructed based on a time-varying multilevel vector autoregressive model of patient-rated symptom data. We used statistical parameters that describe the structure of these person-specific networks to predict therapy outcome. First, we used symptom centrality measures as predictors. Second, we used a machine learning approach to select parameters that describe the strength of pairwise symptom associations. We found that information on person-specific symptom networks strongly improved the accuracy of the prediction of observer-rated depression severity at treatment termination compared to common covariates recorded at baseline. This was also shown for predicting observer-rated depression severity at one- and two-year follow-up. Pairwise symptom associations were better predictors than symptom centrality parameters for depression severity at the end of therapy and one year later. Replication and external validation of our findings, methodological developments, and work on possible ways of implementation are needed before person-specific networks can be reliably used in clinical practice. Nevertheless, our results indicate that the structure of person-specific symptom networks can provide valuable information for the personalization of treatment for chronic depression.

An almost infinite sites model

Motivation:A main challenge in molecular evolution is to find computationally efficient mutation models with flexible assumptions that properly reflect genetic variation. The infinite sites model assumes that each mutation event occurs at a site never previously mutant, i.e. it does not allow recurrent mutations. This is reasonable for low mutation rates and makes statistical inference much more tractable. However, recurrent mutations are common enough to be observable from genetic variation data, even in species with low per-site mutation rates such as humans. The finite sites model on the other hand allows for recurrent mutations but is computationally unfeasible to work with in most cases. In this work, we bridge these two approaches by developing a novel molecular evolution model, the almost infinite sites model, that both admits recurrent mutations and is tractable. We provide a recursive characterisation of the likelihood of our proposed model under complete linkage and outline a parsimonious approximation scheme for computing it. Results:We show the usefulness of our model in simulated and human mitochondrial data. Our results show that the AISM, in combination with a constraint on the total number of mutation events, can recover accurate approximations to the maximum likelihood estimator of the mutation rate. Availability and implementation:An implementation of our model is freely available along with code for reproducing our computational experiments at https://github.com/Cronjaeger/almost-infinite-sites-recursions.