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Abstract Background Despite the fact that studies indicate that earthquake trauma is associated with numerous psychological consequences, the mediating mechanisms leading to these outcomes have not been well-studied. Therefore, this study investigates the relationship between trauma exposure with substance use tendency, depression, and suicidal thoughts, with the mediating role of peritraumatic dissociation and experiential avoidance. Methods The descriptive-correlational approach was employed in this study. The participants were people who had experienced the Kermanshah earthquake in 2017. A total of 324 people were selected by convenient sampling method. The Traumatic Exposure Severity Scale, the Peritraumatic Dissociative Experiences Questionnaire, the Acceptance and Action Questionnaire, the Iranian Addiction Potential Scale, Beck’s Depression Inventory [BDI-II], and Beck’s Suicidal Thoughts Scale were used to collect data. The gathered data was analyzed‌ using structural equation modeling in ‌SPSS Ver. 24 and LISREL Ver. 24. Results The study findings indicated that the intensity of the trauma exposure is directly and significantly associated with depression symptoms, peritraumatic dissociation, and experiential avoidance. The severity of exposure to trauma had a significant indirect effect on the tendency to use substances through experiential avoidance. This is while the severity of the trauma experience did not directly correlate with substance use and suicidal thoughts. In addition, peritraumatic dissociation did not act as a mediator in the relationship between the severity of trauma exposure with substance use, depression, and suicidal thoughts. Conclusions The severity of exposure to the earthquake was associated with symptoms of depression and these findings indicate the importance of experiential avoidance in predicting the tendency to use drugs. Hence, it is essential to design and implement psychological interventions that target experiential avoidance to prevent drug use tendencies and to establish policies that lower depression symptoms following natural disasters.

Abstract Background Single-cell gene expression measurements offer opportunities in deriving mechanistic understanding of complex diseases, including cancer. However, due to the complex regulatory machinery of the cell, gene regulatory network (GRN) model inference based on such data still manifests significant uncertainty. Results The goal of this paper is to develop optimal classification of single-cell trajectories accounting for potential model uncertainty. Partially-observed Boolean dynamical systems (POBDS) are used for modeling gene regulatory networks observed through noisy gene-expression data. We derive the exact optimal Bayesian classifier (OBC) for binary classification of single-cell trajectories. The application of the OBC becomes impractical for large GRNs, due to computational and memory requirements. To address this, we introduce a particle-based single-cell classification method that is highly scalable for large GRNs with much lower complexity than the optimal solution. Conclusion The performance of the proposed particle-based method is demonstrated through numerical experiments using a POBDS model of the well-known T-cell large granular lymphocyte (T-LGL) leukemia network with noisy time-series gene-expression data.

Abstract Background Gene regulatory networks govern the function of key cellular processes, such as control of the cell cycle, response to stress, DNA repair mechanisms, and more. Boolean networks have been used successfully in modeling gene regulatory networks. In the Boolean network model, the transcriptional state of each gene is represented by 0 (inactive) or 1 (active), and the relationship among genes is represented by logical gates updated at discrete time points. However, the Boolean gene states are never observed directly, but only indirectly and incompletely through noisy measurements based on expression technologies such as cDNA microarrays, RNA-Seq, and cell imaging-based assays. The Partially-Observed Boolean Dynamical System (POBDS) signal model is distinct from other deterministic and stochastic Boolean network models in removing the requirement of a directly observable Boolean state vector and allowing uncertainty in the measurement process, addressing the scenario encountered in practice in transcriptomic analysis. Results BoolFilter is an R package that implements the POBDS model and associated algorithms for state and parameter estimation. It allows the user to estimate the Boolean states, network topology, and measurement parameters from time series of transcriptomic data using exact and approximated (particle) filters, as well as simulate the transcriptomic data for a given Boolean network model. Some of its infrastructure, such as the network interface, is the same as in the previously published R package for Boolean Networks BoolNet, which enhances compatibility and user accessibility to the new package. Conclusions We introduce the R package BoolFilter for Partially-Observed Boolean Dynamical Systems (POBDS). The BoolFilter package provides a useful toolbox for the bioinformatics community, with state-of-the-art algorithms for simulation of time series transcriptomic data as well as the inverse process of system identification from data obtained with various expression technologies such as cDNA microarrays, RNA-Seq, and cell imaging-based assays.