Variable Model Research Articles

Improved prognostication of overall survival after radiotherapy in lung cancer patients by an interpretable machine learning model integrating lung and tumor radiomics and clinical parameters.

Accurate prognostication of overall survival (OS) for non-small cell lung cancer (NSCLC) patients receiving definitive radiotherapy (RT) is crucial for developing personalized treatment strategies. This study aims to construct an interpretable prognostic model that combines radiomic features extracted from normal lung and from primary tumor with clinical parameters. Our model aimed to clarify the complex, nonlinear interactions between these variables and enhance prognostic accuracy. We included 661 stage III NSCLC patients from three multi-national datasets: a training set (N = 349), test-set-1 (N = 229), and test-set-2 (N = 83), all undergoing definitive RT. A total of 104 distinct radiomic features were separately extracted from the regions of interest in the lung and the tumor. We developed four predictive models using eXtreme gradient boosting and selected the top 10 features based on the Shapley additive explanations (SHAP) values. These models were the tumor radiomic model (Model-T), lung radiomic model (Model-L), a combined radiomic model (Model-LT), and an integrated model incorporating clinical parameters (Model-LTC). Model performance was evaluated through Harrell's concordance index, Kaplan-Meier survival curves, time-dependent area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis. Interpretability was assessed using the SHAP framework. Model-LTC exhibited superior performance, with notable predictive accuracy (C-index: training set, 0.87; test-set-2, 0.76) and time-dependent AUC above 0.75. Complex nonlinear relationships and interactions were evident among the model's variables. The integration of radiomic and clinical factors within an interpretable framework significantly improved OS prediction. The SHAP analysis provided insightful interpretability, enhancing the model's clinical applicability and potential for aiding personalized treatment decisions.

Modeling longitudinal change patterns of self-regulation from early to middle childhood: Methodological innovations and individual differences.

Patterns of change in self-regulation from the ages of 2.5 to 12 years were modeled using repeated measures of self-regulation for a sample of 399 African American (n = 180, 45%) and Latinx (n = 219, 55%) children from families experiencing low income. Measures included both direct assessment and parent report. Results confirmed four components of self-regulation: working memory, inhibitory control, complex response inhibition, and set shifting. Furthermore, these components of self-regulation were more differentiated at younger ages but grew increasingly integrated as children developed. During early childhood, Latinx children displayed greater levels of working memory and higher levels of inhibitory control, and African American children displayed greater complex response inhibition and set shifting, but these ethnic differences reversed by early elementary school. By late middle childhood, ethnic differences in self-regulation had virtually disappeared altogether. Few differences by child gender or family poverty status were identified. Recommendations are provided to facilitate the modeling of self-regulation over extended periods of development including (a) utilizing measures that overlap time points, (b) standardizing the measurement scales, and (c) utilizing a flexible latent variable model. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A prognostic nomogram for patients with III-IV nasopharyngeal carcinoma based on dynamic changes in the inflammatory and nutrition index.

The purpose of the study was to explore the value of dynamic changes in inflammatory and nutritional index after comprehensive treatment in patients with stage III-IVA nasopharyngeal carcinoma (NPC). A prognostic model was also established and validated for progression-free survival (PFS) of patients. We retrospectively selected 279 NPC patients with stage III-IVA. Their general clinical data and hematological index were collected and then calculated the changes during treatment. X-tile software was used to determine the optimal cut-off value. COX regression, Lasso method, and Boruta method were used to variable selection and model establishment. Using the bootstrap internal validation method, concordance index (C-index), calibration plot, and Kaplan-Meier curves were used to evaluate the model. To test the prognostic value of the model, we have also evaluated the performance of the nomogram against a conventional tumor metastasis staging system (TNM). Multivariable COX regression analysis demonstrated that clinical staging, delta lymphocyte, delta monocyte, delta albumin, delta platelet-to-lymphocyte ratio and delta systemic immune inflammation index were related to the PFS of NPC patients. The C-index of the model was 0.712, and the calibration curve indicated that the model had good consistency. The C-index of the TNM staging system was 0.597, which was considerably lower compared to our model (P = 0.015). We demonstrated the predictive value of dynamic changes in inflammatory and nutritional indices for the prognosis of NPC by successfully establishing and validating a prognostic model for predicting 1- and 3-year PFS after comprehensive treatment in patients with stage III-IVA NPC.

New aspects of the development of Kantorovich’s one-product dynamic model of replacement of production funds

In the context of the development of scientific and technological progress and the growth of the capital stock, an important task is modeling and optimization of the terms of operation of production funds. The development of technologies and automation of production lead to a reduction in the workforce employed in production. In the article, for the previously developed single-product dynamic model of the replacement of production assets, taking into account the inertial properties of the funds being introduced, the case of a reduction in labor resources under the condition of an increase in output and capital investment is investigated. A solution was obtained that allows to determine the optimal strategy for the withdrawal of obsolete funds and the introduction of the new ones in case of decrease in labor resources. As the optimization criterion the principle of differential optimization is used. The theorem of equivalence of a trajectory satisfying the principle of differential optimization, the obtained solution is given. A system of functional differential equations is presented, which should be satisfied by variable models both in terms of growth and reduction of labor resources. Numerical methods are used to solve such a system. The variants of the system development are considered under various assumptions regarding changes in the total amount of labor resources. The results of numerical implementation of the model are presented.

A prediction model of echocardiographic variables to screen for potentially correctable shunts in adult ASD-PAH patients.

Atrial septal defect (ASD) is a prevalent congenital heart condition in adults that leads to pulmonary hypertension (PAH) and right heart failure if left untreated. During a routine follow-up of adult ASD-PAH patients, the suitability of shunt closure depends on the invasive right heart catheterization (RHC).Nevertheless, performing RHC at every follow-up is impractical and may be harmful. The present retrospective cross-sectional study was designed to investigate which echocardiographic variables are related to pulmonary vascular resistance (PVR) in adult ASD-PAH patients and proposed a model using these variables to screen for patients with a correctable shunt. A total of 530 adult ASD-PAH patients with pulmonary arterial systolic pressure (PASP) of ≥60 mmHg measured using transthoracic echocardiogram (TTE) were included in the study. All RHCs were performed within 3 months after TTE. The correctable shunt was defined as PVR ≤3 wood units (WU). Multivariate regressions were performed utilizing echocardiographic variables. A scoring system was constructed based on the predictors of PVR ≤3 WU using multivariate logistic regression analysis. The scoring system was then examined using a receiver operating characteristic (ROC) analysis. In addition, clinical utility of the model was determined based on decision curve analysis and a calibration curve was used to evaluate model conformity. Estimated pulmonary arterial systolic pressure, velocity through the pulmonary valve, tricuspid annulus early diastolic velocity, and maximum defect dimension were identified as independent predictors. The area under the ROC curve of the predictive value in the model was 0.905 [95% confidence interval (CI): 0.878-0.931, sensitivity: 84.3%, specificity: 83.6%]. The net benefit of the model was notable in terms of a wide-range probability threshold in decision curve analysis, indicating that the prediction model had good clinical applicability. The model's calibration curve was close to an ideal diagonal line, showing good predictive accuracy between the actual and predictive probabilities. The study model was demonstrated to be valuable in predicting adult ASD-PAH patients with a correctable shunt, which may help clinicians to make appropriate treatment decision for follow-up patients.

Data‐sparse price indexes by spatio‐temporal regularization and PCA: An application to the Australian housing market

AbstractWe present two novel approaches to overcome the limitations of data‐sparse local house price indexes and combine them into a single model pipeline that is simple, computationally efficient, and interpretable. The first contribution is a new spatio‐temporal regularization of least squares dummy variable models, such as the repeat sales regression used here. This regularization encodes prior knowledge of the proximity of houses in space and their sales in time. It handles missing values in a natural way. The second is nonlocal regularization using truncated principal component analysis (PCA) applied to the resulting national collection of local price indexes. The PCA loadings show that there are important underlying socioeconomic factors that can be leveraged in the construction of Australian market indexes. This PCA reveals important socioeconomic factors, showing that many local markets can be described by a few broad aspects of the national market, consisting of a general trend that contrasts regions influenced by the mining industry with Sydney and Melbourne, and another trend that highlights lifestyle.

De jure powersharing 1975–2019: Updating the Inclusion, Dispersion, and Constraints Dataset

Powersharing institutions are often prescribed to enhance civil peace, democratic survival, and the equitable provision of public services, and these institutions have become more prevalent over time. Nonetheless, the past decade has seen a rise in democratic backsliding and competitive authoritarianism, raising questions about how the relationship between powersharing, democracy, and civil peace may be evolving. This article introduces an update to the Inclusion, Dispersion, and Constraints (IDC) powersharing dataset that adds nine years of data, up through 2019. These new data include enhanced intercoder reliability checks, a significant reduction in missing values, and the documentation and correction of some coding errors in the original data. We also employ latent variable models to estimate each of three types of powersharing, allowing scholars to account for measurement uncertainty in analyses of the causes and consequences of powersharing. This dataset allows scholars to address urgent questions about whether previously observed relationships between powersharing and democracy and powersharing and civil peace still hold in this new era, and in what contexts powersharing institutions remain advisable.

Does alcohol consumption cause poverty? Evidence from Vietnam

ABSTRACT This paper examines the impacts of alcohol expenditure on poverty. A main challenge of doing so is to remove the endogeneity arising from the bidirectional relationship between the two variables that seriously distorts the estimated correlation coefficients. This paper proves that an instrumental variable (IV) probit model outperforms the treatment effect and the panel probit models in terms of purging this endogeneity. When this IV probit model is applied to the Vietnamese data, the results show that alcohol expenditure increases the likelihood of consumers becoming poor; therefore, it is crucial to prioritize the regulation of alcohol spending as a fundamental element in the execution of poverty alleviation initiatives.

Predictive combination model for CH4 separation and CO2 sequestration with CO2 injection into coal seams: VMD-STA-BiLSTM-ELM hybrid neural network modeling

In the experimental process of separating coal seam gas using the CO2 displacement method, establishing a predictive model for key variables is essential to optimize displacement parameters, increase coal seam gas recovery, and improve CO2 sequestration efficiency. Traditional modeling methods often struggle with the complex nature of industrial data and are susceptible to overfitting due to multicollinearity caused by long-term datasets. This paper presents a hybrid predictive model based on variational mode decomposition (VMD), a spatiotemporal attention mechanism (STA), a bidirectional long short-term memory network (BiLSTM), and an extreme learning machine (ELM). During the offline phase, VMD is used to decompose raw data into intrinsic mode functions (IMFs). The hidden state from the last time step of the STA-BiLSTM is then added to the original data to enrich the features for ELM training. In the online prediction phase, the outputs from the VMD-STA-BiLSTM and VMD-STA-BiLSTM-ELM models are combined using an error reciprocal method to generate the final prediction. The proposed model is validated with experimental datasets from CO2 displacement for coal seam CH4 under various conditions, as well as with N2-ECBM and CO2-ECBM engineering datasets. The results show that the hybrid model surpasses VMD-ELM, STA-BiLSTM-ELM, BiLSTM, STA-BiLSTM, ELM, TCN, and Attention-TCN models in predictive accuracy. Even in multi-step and rolling predictions, the model exhibits minimal impact from cumulative errors, maintaining accurate forecasts with strong generalization and robustness. It effectively captures feature patterns across different datasets and accurately predicts unknown data. The model shows potential for application in diverse scenarios and complex environments, offering reliable support and decision-making for the field application of CO2 displacement in coal seam CH4 separation. It is an effective and promising predictive approach to enhance coal seam gas recovery and CO2 sequestration efficiency.

Actor Sensemaking and its Role in Implementation of the Decentralized Drug-Resistant TB Policy in South Africa.

South Africa has a high burden of drug-resistant tuberculosis (DR-TB). A policy to decentralize DR-TB treatment from specialized central hospitals to more accessible district facilities was introduced in 2011, but to date implementation has been suboptimal, with variable pace, coverage and models of care emerging. This study explored multilevel policy implementation of DR-TB decentralization in two provinces of South Africa, Western Cape and KwaZulu-Natal. Applying interpretive policy analysis, this paper describes how actors across health system levels and geographies made sense of the DR-TB policy and how this shaped implementation. In an embedded qualitative case study, districts of the two provinces were compared, through data collected in 94 in-depth interviews, and analysed using Vickers' framework of reality, value and action judgements. Five district cases characterise variation in the pace of implementation and models of DR-TB care that emerged. Individual and collective attitudes for and against the policy were underpinned by different systems of meaning for interpreting policy problems and making decisions. These meaning systems were reflected in actor stances on whether DR-TB care needed to be specialized or generalized, nurse- or doctor-led, and institutionalized or ambulatory. Actors' stances influenced their actions and implementation strategies adopted. Resistance to decentralized DR-TB care related to perceived threats of budget cuts to and loss of authority of central facilities, and was often justified in fears of increased transmission, poor quality of care and inadequate resources at lower levels. New advances in diagnosis and treatment to address the growing burden of DR-TB in South Africa will have little impact unless implementation dynamics are better understood, and attention paid to the mindsets, interests and interpretations of policy by actors tasked with implementation. Deliberative policy implementation processes will enhance the quality of discourse, communication and cross-learning between policy actors, critical for reaching synthesis of meaning systems.

Performance of Risk Models for Antimicrobial Resistance in Adult Patients With Sepsis

The results of prediction models that stratify patients with sepsis and risk of resistant gram-negative bacilli (GNB) infections inform treatment guidelines. However, these models do not extrapolate well across hospitals. To assess whether patient case mix and local prevalence rates of resistance contributed to the variable performance of a general risk stratification GNB sepsis model for community-onset and hospital-onset sepsis across hospitals. This was a retrospective cohort study conducted from January 2016 and October 2021. Adult patients with sepsis at 10 acute-care hospitals in rural and urban areas across Missouri and Illinois were included. Inclusion criteria were blood cultures indicating sepsis, having received 4 days of antibiotic treatment, and having organ dysfunction (vasopressor use, mechanical ventilation, increased creatinine or bilirubin levels, and thrombocytopenia). Analyses were completed in April 2024. The model included demographic characteristics, comorbidities, vital signs, laboratory values, procedures, and medications administered. Culture results were stratified for ceftriaxone-susceptible GNB (SS), ceftriaxone-resistant but cefepime-susceptible GNB (RS), and ceftriaxone- and cefepime-resistant GNB (RR). Negative cultures and other pathogens were labeled SS. Deep learning models were developed separately for community-onset (patient presented with sepsis) and hospital-onset (sepsis developed ≥48 hours after admission) sepsis. The models were tested across hospitals and patient subgroups. Models were assessed using area under the receiver operating characteristic curve (AUROC) and area under precision recall curve (AUPRC). A total of 39 893 patients with 85 238 sepsis episodes (43 207 [50.7%] community onset; 42 031 [48.3%] hospital onset) were included. Median (IQR) age was 65 (54-74) years, 21 241 patients (53.2%) were male, and 18 830 (47.2%) had a previous episode of sepsis. RS contributed to 3.9% (1667 episodes) and 5.7% (2389 episodes) of community-onset and hospital-onset sepsis episodes, respectively, and RR contributed to 1.8% (796 episodes) and 3.9% (1626 episodes), respectively. Previous infections and exposure to antibiotics were associated with the risk of resistant GNB. For example, in community-onset sepsis, 375 RR episodes (47.1%), 420 RS episodes (25.2%) and 3483 of 40 744 (8.5%) SS episodes were among patients with resistance to antimicrobial drugs (P < .001). The AUROC and AUPRC results varied across hospitals and patient subgroups for both community-onset and hospital-onset sepsis. AUPRC values correlated with the prevalence rates of resistant GNB (R = 0.79; P = .001). In this cohort study of 39 893 patients with sepsis, variable model performance was associated with prevalence rates of antimicrobial resistance rather than patient case mix. This variability suggests caution is needed when using generalized models for predicting resistant GNB etiologies in sepsis.

Extending the Scope of Instrumental Variable Methods

Abstract Since Philip Wright’s (1928) proposal of the linear instrumental variables (IV) estimator the scope of application of IV models has greatly expanded. There are now nonlinear IV models in use with both additive and nonadditive latent variables, employing both semiparametric and nonparametric specifications. A notable feature of Wright’s linear IV estimator is that it is applicable to incomplete models that leave the genesis of endogenous explanatory variables unspecified. On the other hand, inversion of the linear model enables the value of unobservable heterogeneity to be expressed as a function of observable variables. Only recently has the application of incomplete IV models been extended to cases in which this inversion produces a non-singleton set of values of unobservable variables. This situation arises in many models such as those featuring discrete choice, counts, or censored outcomes, or when there are multiple sources of heterogeneity as in random coefficient models, or when economic considerations result in inequality restrictions on values taken by observed and unobserved variables. This paper provides an introduction to the methods set out for analyzing such generalized instrumental variable (GIV) models in Chesher and Rosen (2017). An illustrative example is provided in which an outcome is determined by a random coefficients linear model with an endogenous explanatory variable and instrumental variable exclusion and independence restrictions.

Biomechanical perspectives on college students’ psychological well-being and their impact on academic performance

Academic performance among college students is prejudiced by a complex interplay of psychological well-being, physical activity, and biomechanical health. While previous research has often focused on individual factors such as mental health or physical activity, there is a growing need for an integrative approach to understand how these elements interact and collectively impact academic performance. This study aims to explore these relationships among 126 students from four universities in China, using a comprehensive methodology that includes psychological assessments (GHQ-28, PSS, MHI), wearable activity trackers for physical activity and sleep monitoring, and biomechanical evaluations through motion capture and electromyography (EMG). The study adopts a multi-faceted analytical approach, including hierarchical regression, path analysis, and latent variable modelling. Hierarchical regression revealed that psychological factors alone explained 21.1% of the variance in GPA (R2 = 0.211), while adding biomechanical and physical activity factors increased the explanatory power to 35.0% (R2 = 0.350). Path analysis indicated that psychological well-being had a direct negative effect on academic performance (β = −0.41, p &lt; 0.001) and an indirect effect mediated through physical activity (indirect β = −0.089, p = 0.0021). Latent variable modelling demonstrated that psychological well-being, physical activity, and biomechanical health constructs collectively explained 52% of the variance in academic performance.

Center of mass kinematic reconstruction during steady-state walking using optimized template models.

Template models, such as the Bipedal Spring-Loaded Inverted Pendulum and the Virtual Pivot Point, have been widely used as low-dimensional representations of the complex dynamics in legged locomotion. Despite their ability to qualitatively match human walking characteristics like M-shaped ground reaction force (GRF) profiles, they often exhibit discrepancies when compared to experimental data, notably in overestimating vertical center of mass (CoM) displacement and underestimating gait event timings (touchdown/ liftoff). This paper hypothesizes that the constant leg stiffness of these models explains the majority of these discrepancies. The study systematically investigates the impact of stiffness variations on the fidelity of model fittings to human data, where an optimization framework is employed to identify optimal leg stiffness trajectories. The study also quantifies the effects of stiffness variations on salient characteristics of human walking (GRF profiles and gait event timing). The optimization framework was applied to 24 subjects walking at 40% to 145% preferred walking speed (PWS). The findings reveal that despite only modifying ground forces in one direction, variable leg stiffness models exhibited a >80% reduction in CoM error across both the B-SLIP and VPP models, while also improving prediction of human GRF profiles. However, the accuracy of gait event timing did not consistently show improvement across all conditions. The resulting stiffness profiles mimic walking characteristics of ankle push-off during double support and reduced CoM vaulting during single support.

Virtual Mental Health Care and Suicide-Related Events

The rising suicide rates in the US emphasize the need for effective prevention. While telehealth has transformed access to mental health care, the impact of telehealth on suicide outcomes is unknown. To evaluate the association of virtual mental health services with individual-level suicide-related events (SREs). This retrospective cohort study using broadband access as an instrumental variable assessed a national sample of Veterans Health Administration patients who received mental health care between March 1, 2020, and December 31, 2021. Participants were recently separated (ie, discharged or released from active duty) veterans who completed their active duty service between March 1, 2019, and December 31, 2020, and who received at least 2 outpatient or inpatient diagnoses related to major depressive disorder, substance use disorder, or posttraumatic stress disorder within the year before their most recent separation date. Data were analyzed May 1 to October 31, 2023. Percentage of a patient's total mental health visits that were conducted virtually by psychiatrists, psychologists, or social workers within a calendar month. Binary measure indicating whether the patient had experienced an SRE (defined as a nonfatal suicide attempt, intentional self-harm, or suicide death) in a specific month and year as evaluated an instrumental variable probit model. The sample included 66 387 data points from 16 236 unique recently separated veterans. Among these entries, 44 766 were for male veterans (67.4%), the mean (SD) age across the sample was 32.9 (8.9) years, and the sample was representative of the US veteran population. There were 929 SREs (1.4%). Virtual mental health visits comprised a mean (SD) of 44.6% (46.1%) of all mental health visits. In instrumental variable probit analyses accounting for factors simultaneously associated with use of virtual mental health care and SRE risk, a 1% increase in the probability of virtual mental health visits was associated with a 2.5% decrease in SREs. Findings from this cohort study using a retrospective quasi-experimental design found that an increase in virtual mental health visits relative to total visits was associated with a statistically significant decrease in SREs, suggesting that providing virtual mental health services may reduce suicide-related outcomes.

Psychometric Properties of the Danish International Trauma Questionnaire-Child and Adolescent Version (ITQ-CA).

With the introduction of the ICD-11 diagnostic manual, the need for developing and validating new assessment instruments has become urgent. The International Trauma Questionnaire-Child and Adolescent version(ITQ-CA) assesses posttraumatic stress disorder (PTSD) and complex PTSD (CPTSD) based on the ICD-11 diagnostic definition. This study examines the psychometric properties of the Danish version of the ITQ-CA in a non-clinical sample of 226 adolescents. Hierarchical multiple regression analyses were performed to study the effects of gender, age, trauma exposure, family dysfunction, and SDQ variables on PTSD and Disturbances in Self-Organization (DSO). Results showed that 16 participants (7%) met the ITQ-CA criteria for CPTSD and 11 (5.7%) for PTSD. The full model of gender, age, family dysfunction, trauma exposure, and SDQ variables was found to predict PTSD and DSO. The present study supports the validity of the Danish version of the ITQ-CA and represents an important step of establishing and applying validated tools for assessing PTSD and CPTSD in children and adolescents. The results also underscore the need for a broader trauma definition. Future research should examine the sensitivity of the ITQ-CA.

Numerical investigation of turbulent flow over a randomly packed sediment bed using a variable porosity continuum model

A large eddy simulation (LES) is performed for a turbulent open channel flow over a porous sediment bed at permeability Reynolds number of ReK∼2.56 (Reτ = 270) representative of aquatic systems. A continuum approach based on the upscaled, volume-averaged Navier−Stokes (VaNS) equations is used by defining smoothly varying porosity across the sediment water interface (SWI) and modeling the drag force in the porous bed using a modified Ergun equation with Forchheimer corrections for inertial terms. The results from the continuum approach are compared with a pore-resolved direct numerical simulation (PR-DNS) in which turbulent flow over a randomly packed sediment bed of monodispersed particles is investigated [Karra et al.,J. Fluid Mech. 971, A23 (2023)] A spatially varying porosity profile generated from the pore-resolved DNS is used in the continuum approach. Mean flow, Reynolds stress statistics, and net momentum exchange between the freestream and the porous bed are compared between the two studies, showing reasonably good agreement. Small deviations within the transitional region between the sediment bed and the freestream as compared to the PR-DNS results are attributed to the local protrusions of particles in a randomly packed bed that are absent in the continuum approach but are present in the PR-DNS. A better representation of the effective permeability in the top transition layer that accounts for roughness effect of exposed particles is necessary. The continuum approach significantly reduces the computational cost, thereby making it suitable to study hyporheic exchange of mass and momentum in large scale aquatic domains with combined influence of bedform and bed roughness.

Differential neural network based adaptive average output feedback control design for dosage determination on cancer based immunotherapy treatment

Immunotherapy involves natural and synthetic substances to stimulate the body’s immune response. This treatment approach is practical not only for addressing immune deficiencies but also for combating malignancies. This paper describes a non-parametric approximated adaptive control process for managing cancer dynamics under immunotherapy treatment, utilizing a combination of a differential neural network (DNN) observer and nonlinear control techniques such as sliding mode and local optimal strategies. By employing the state estimation and control methods, close tracking between the estimated states provided by the neural network and the cancer model dynamics is possible. Internal model reconstruction and an observer provided by a variable structure model are essential for controlling unknown plants. Furthermore, the control design has successfully reduced tumor cells despite uncertainties and external perturbations affecting cancer dynamics. This robustness enhances the reliability of the proposed design. A virtual real-time scheme was developed to demonstrate this controller’s feasibility in real clinical scenarios. In this scheme, a simulated patient generates variables of immunotherapy dynamics as electrical signals, which are then analyzed by a real-time project.

Association between infant sleep and neurodevelopment in a prospective birth cohort study

ObjectivesTo investigate the association of infant sleep and sleep trajectories through the first year of life with infant neurodevelopment. MethodsThis study was conducted with 3251 infants in China. Sleep parameters were evaluated by the Brief Infant Sleep Questionnaire at 42days, 6months, and 1year of age. Neurodevelopment was evaluated at 1year of age using the Bayley Scales of Infant and Toddler Development, Third Edition. The latent variable growth curve model was used to evaluate the developmental trajectories of infant sleep, including total sleep duration trajectories, night awakening trajectories and sleep onset latency trajectories. Poisson regression was applied to assess the association between sleep parameters and sleep trajectories and infant neurodevelopment. ResultsInfants with frequent night awakenings at 6months had a higher risk of nonoptimal gross motor development. Additionally, infants with prolonged sleep onset latency at 1year had an increased risk of nonoptimal fine and gross motor development. A consistent frequent night-awakening trajectory increased the risk of nonoptimal gross motor development (adjusted relative risk, 1.52; 95% confidence interval, 1.09 to 2.10). Furthermore, an increasing trajectory in sleep onset latency was associated with an increased risk of nonoptimal fine (adjusted relative risk, 2.70; 95% confidence interval, 1.12 to 6.51) and gross motor development (adjusted relative risk, 2.76; 95% confidence interval, 1.70 to 4.48). However, no significant association was observed between total sleep duration, or its trajectory, and infant neurodevelopment. ConclusionsSleep problems or specific sleep trajectories during the initial year of life may increase risk of compromised neurodevelopment.

The Determinants of Financial Development: Evidence from Bayesian Model Averaging

We examine the determinants of financial development using our global sample and employing different measures of financial development that assess the degree of depth and efficiency of financial intermediaries. We use instrumental variable Bayesian model averaging to test competing theories with this unifying framework. After examining nearly 20 potential determinants of financial development, we find that the rule of law, as well as some of its components, is the most important. In addition, our results suggest that wealth inequality is irrelevant to banking sector development but positively associated with stock market development.