Population food intake clusters and cardiovascular disease incidence: a Bayesian quantifying of a prospective population-based cohort study in a low and middle-income country.

Aims: This study was designed to explore the relationship between cardiovascular disease incidence and population clusters, which were established based on daily food intake.

Methods: The current study examined 5,396 Iranian adults (2,627 males and 2,769 females) aged 35 years and older, who participated in a 10-year longitudinal population-based study that began in 2001. The frequency of food group consumption over the preceding year (daily, weekly, or monthly) was assessed using a 49-item qualitative food frequency questionnaire (FFQ) administered a face-to-face interview conducted by an expert dietitian.

Correction to: Spike Sorting of Non-Stationary Data in Successive Intervals Based on Dirichlet Process Mixtures.

[This corrects the article DOI: 10.1007/s11571-022-09781-7.].

Spike Sorting of Non-Stationary Data in Successive Intervals Based on Dirichlet Process Mixtures.

This paper proposes a new automatic method for spike sorting and tracking non-stationary data based on the Dirichlet Process Mixture (DPM). Data is divided into non-overlapping intervals and mixtures are applied to individual frames rather than to the whole data. In this paper, we have used the information of the previous frame to estimate the cluster parameters of the current interval.

Risk factors associated with intensive care unit (ICU) admission and in-hospital death among adults hospitalized with COVID-19: a two-center retrospective observational study in tertiary care hospitals.

Background: The COVID-19 pandemic is straining the health care systems worldwide. Therefore, health systems should make strategic shifts to ensure that limited resources provide the highest benefit for COVID-19 patients.

Objective: This study aimed to describe the risk factors associated with poor in-hospital outcomes to help clinicians make better patient care decisions.

Longitudinal Modeling of Non-Pharmacological Factors Related to Frequency, Severity and Duration in Both Migraine and Tension-Type Headaches.

Background: Frequency, severity, and duration of attacks are some major parameters in headache management, affected by some other factors. Ignoring these factors in headache-related studies can lead to incorrect results. We aimed to model both socio-demographic characteristics and headache-associated symptoms related to frequency, severity and duration of headache attacks.

Effects of time-invariant covariates on the estimation of longitudinal trends for transition mixed models.

In this paper, we investigate the impact of time-invariant covariates when fitting transition mixed models. This is carried out by emphasizing on the role of baseline responses on the estimation process. Transition models are allowed for two cases of exogenous and endogenous baseline responses.