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Design is an inseparable part of most scientific and engineering tasks, including real and simulation-based experimental design processes and parameter/hyperparameter tuning/optimization. Several model-based experimental design techniques have been developed for design in domains with partial available knowledge about the underlying process. This article focuses on a powerful class of model-based experimental design called the mean objective cost of uncertainty (MOCU). The MOCU-based techniques are objective-based, meaning that they take the main objective of the process into account during the experimental design process. However, the lack of scalability of MOCU-based techniques prevents their application to most practical problems, including large discrete or combinatorial spaces. To achieve a scalable objective-based experimental design, this article proposes a graph-based MOCU-based Bayesian optimization framework. The correlations among samples in the large design space are accounted for using a graph-based Gaussian process, and an efficient closed-form sequential selection is achieved through the well-known expected improvement policy. The proposed framework's performance is assessed through the structural intervention in gene regulatory networks, aiming to make the network away from the states associated with cancer.

State-space models (SSMs) are a rich class of dynamical models with a wide range of applications in economics, healthcare, computational biology, robotics, and more. Proper analysis, control, learning, and decision-making in dynamical systems modeled by SSMs depend on the accuracy of the inferred/learned model. Most of the existing inference techniques for SSMs are capable of dealing with very small systems, unable to be applied to most of the large-scale practical problems. Toward this, this article introduces a two-stage Bayesian optimization (BO) framework for scalable and efficient inference in SSMs. The proposed framework maps the original large parameter space to a reduced space, containing a small linear combination of the original space. This reduced space, which captures the most variability in the inference function (e.g., log likelihood or log a posteriori), is obtained by eigenvalue decomposition of the covariance of gradients of the inference function approximated by a particle filtering scheme. Then, an exponential reduction in the search space of parameters during the inference process is achieved through the proposed two-stage BO policy, where the solution of the first-stage BO policy in the reduced space specifies the search space of the second-stage BO in the original space. The proposed framework's accuracy and speed are demonstrated through several experiments, including real metagenomics data from a gut microbial community.

Background: A significant portion of studies are mainly focused on the formation of post-traumatic stress disorder (PTSD) after earthquake trauma. This is while earthquakes are associated with a wide range­ of ­psychological reactions­. In addition, other ­factors play­ a critical role in the formation of a diverse range of mental problems - personal characteristics, the severity of exposure to earthquake, and post-earthquake psychological distress (e.g., dissociation and avoidance). Therefore, This study investigates the relationship between earthquake intensity experience with substance use tendency, depression, and suicidal thoughts with the mediating role of peritraumatic dissociation and experiential avoidance. Methods: The participants consist of 323 individuals who had experienced an earthquake disaster four years ago. A set of valid and reliable questionnaires­ was administered to the participants. The gathered data were analyzed‌ using structural equation modeling in ‌SPSS Ver. 24 and LISREL Ver. 24. Results: The results showed that the intensity of the trauma experience is directly and significantly associated with depression symptoms, peritraumatic dissociation, and experiential avoidance. The experience of trauma had a significant­ indirect effect on the tendency to use substance through experimental­ 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 mediate the relationship between the severity of trauma experience with substance use, depression, and suicidal thoughts. Conclusions­: It seems ­that both the experience (severity) of earthquake trauma and some reactions by individuals after a trauma experience can contribute to the prediction of some psychological consequences. These findings indicate the significance of experiential avoidance­ in predicting substance use tendencies. Nonetheless, to draw a clearer picture of­ peritraumatic dissociation, future research should ­focus on­ measuring ­this component more precisely and the inclusion of constructs­ that are naturally dissociative­.

Studies on the theory of mind (TOM) and alexithymia in borderline personality disorder (BPD) have yielded inconsistent results. Also, the relationship between TOM abilities and alexithymia facets as two domains of social cognition has not been studied in BPD. This study aimed to fill this gap. Participants were 50 outpatients with BPD and 50 age and gender-matched healthy controls. Assessments performed using Reading the Mind in Eyes Task (RMET), Toronto Alexithymia Scale (TAS-20), Faux Pas Task (FPT), and Digit Span subtest of Wechsler Adult Intelligence Scale. Results showed that BPD patients scored lower on overall FPT (p .001) and its cognitive (p .001) and affective TOM (p .001) subtests but were comparable with healthy controls in emotion recognition ability assessed by RMET (p = .241). The BPD group also scored significantly lower in overall alexithymia (p .001) and subscales of difficulty identifying feelings (DIF; p .001) and difficulty describing emotions (DDF; p = .001). However, they performed similarly to the healthy control group in externally oriented thinking (EOT; p = .164). Correlation analysis revealed a significant negative correlation between EOT and RMET in the BPD group (r = -0.33, p .05). No association, however, was found between FPT and RMET. This study suggests that BPD patients are impaired in the complex TOM abilities and have lower self-awareness of emotions, but their recognition of others' emotions is intact. Also, the results demonstrate that a heightened level of EOT is associated with difficulties in facial emotion recognition in BPD patients.

Fused Filament Fabrication (FFF) is an extrusion-based additive manufacturing process that utilizes a filament material melted through a hot end extruder to generate a component. Despite the great potential of the process to drastically reduce time-to-produce, cost and material waste for the creation of geometrically complex components, the presence of diverse defects deteriorate the quality of the final build. Defects in FFF (e.g., voids, stringing, and varying track width) are primarily linked to improper calibration of parameters, including feed speed, extrusion speed, extruder temperature, and build plate temperature. Trial and error is the most common practice implemented to manually offset baseline parameters using an array of components generated with varying process parameters. However, fabrication with manual adjustment not only is time consuming, but also leads to a suboptimal solution that jeopardizes the strength and integrity of the generated components. We propose a novel Bayesian Optimization (BO) methodology in conjunction with heterogeneous sensing to determine optimal process parameters with a minimum number of experiments. BO consists of two steps: First, a Gaussian Process as a surrogate model that maps the relationship between controllable parameters (e.g., feed rate/flow rate ratio, extrusion temperature, and layer height) and build quality (i.e., the objective function that is derived from sensing data). Second, an acquisition function is defined from this surrogate to decide where to sample. We design build quality characterization model that formulated as an objective-scoring algorithm that returns the proportion of the effective specimen sensor measurements divided by the desired values. The experimental results on real-world case study shows that the proposed BO is capable of determining the values for parameters in just 7 steps with quality improvement of 0.036 from the best trial quality.

Background: Accurate quantification of bone, muscle, and their components is still an unmet need in the musculoskeletal field. Current methods to quantify tissue volumes in 3D images are expensive, labor-intensive, and time-consuming; thus, a reliable, valid, and quick application is highly needed.Methods: Tissue Compass is a standalone software for semiautomatic segmentation and automatic quantification of musculoskeletal organs. To validate the software, cross-sectional micro-CT scans images of rat femur (n=19), and CT images of hip and abdomen (n=100) from the Osteoporotic Fractures in Men (MrOS) Study were used to quantify bone, hematopoietic marrow (HBM), and marrow adipose tissue (MAT) using commercial manual software as a comparator. Also, abdominal CT scans (n=100) were used to quantify psoas muscle volumes and intermuscular adipose tissue (IMAT) using the same software. We calculated Pearson's correlation coefficients, individual intra-class correlation coefficients (ICC), and Bland-Altman limits of agreement together with Bland-Altman plots to show the inter- and intra-observer agreement between Tissue Compass and commercially available software.Results: In the animal study, the agreement between Tissue Compass and commercial software was r>0.93 and ICC>0.93 for rat femur measurements. Bland-Altman limits of agreement was -720.89 (-1.5e+04, 13074.00) for MAT, 4421.11 (-1.8e+04, 27149.73) for HBM and -6073.32 (-2.9e+04, 16388.37) for bone. The inter-observer agreement for QCT human study between two observers was r>0.99 and ICC>0.99. Bland-Altman limits of agreement was 0.01 (-0.07, 0.10) for MAT in hip, 0.02 (-0.08, 0.12) for HBM in hip, 0.05 (-0.15, 0.25) for bone in hip, 0.02 (-0.18, 0.22) for MAT in L1, 0.00 (-0.16, 0.16) for HBM in L1, 0.02 (-0.23, 0.27) for bone in L1. The intra-observer agreement for QCT human study between two applications was r>0.997 and ICC>0.99. Bland-Altman limits of agreement was 0.03 (-0.13, 0.20) for MAT in hip, 0.05 (-0.08, 0.18) for HBM in hip, 0.05 (-0.24, 0.34) for bone in hip, -0.02 (-0.34, 0.31) for MAT in L1, -0.14 (-0.44, 0.17) for HBM in L1, -0.29 (-0.62, 0.05) for bone in L1, 0.03 (-0.08, 0.15) for IMAT in psoas, and 0.02 (-0.35, 0.38) for muscle in psoas. Conclusion: Compared to a conventional application, Tissue Compass demonstrated high accuracy and non-inferiority while also facilitating easier analyses. Tissue Compass could become the tool of choice to diagnose tissue loss/gain syndromes in the future by requiring a small number of CT sections to detect tissue volumes and fat infiltration.

The significance and roles of marrow adipose tissue (MAT) are increasingly known, and it is no more considered a passive fat storage but a tissue with significant paracrine and endocrine activities that can cause lipotoxicity and inflammation. Changes in the MAT volume and fatty acid composition appear to drive bone and hematopoietic marrow deterioration, and studying it may open new horizons to predict bone fragility and anemia development. MAT has the potential to negatively impact bone volume and strength through several mechanisms that are partially described by inflammaging and lipotoxicity terminology. Evidence indicates paramount importance of MAT in age-associated decline of bone and red marrow structure and function. Currently, MAT measurement is being tested and validated by several techniques. However, purpose-specific adaptation of existing imaging technologies and, more importantly, development of new modalities to quantitatively measure MAT are yet to be done.

Purpose The objective of this study was to compare the effects of 12 weeks of resistance training combined with either 5:2 intermittent fasting or continuous energy restriction on body composition, muscle size and quality, and upper and lower body strength. Methods Untrained individuals undertook 12 weeks of resistance training plus either continuous energy restriction [20% daily energy restriction (CERT)] or 5:2 intermittent fasting [~ 70% energy restriction 2 days/week, euenergetic consumption 5 days/week (IFT)], with both groups prescribed a mean of ≥ 1.4 g of protein per kilogram of body weight per day. Participants completed 2 supervised resistance and 1 unsupervised aerobic/resistance training combination session per week. Changes in lean body mass (LBM), thigh muscle size and quality, strength and dietary intake were assessed. Results Thirty-four participants completed the study (CERT = 17, IFT = 17). LBM was significantly increased (+ 3.7%, p p p Conclusion When combined with resistance training and moderate protein intake, continuous energy restriction and 5:2 intermittent fasting resulted in similar improvements in body composition, muscle quality, and strength. ACTRN: ACTRN12620000920998, September 2020, retrospectively registered.

This paper introduces a new signal model called partially-observed discrete dynamical systems (PODDS). This signal model is a special case of the hidden Markov model (HMM), where the state is a vector containing the information of different components of the system, and each component takes its value from a finite real-valued set. This signal model is currently treated as a finite-state HMM, where maximum a posteriori (MAP) criterion is used for state estimator purpose. This paper takes advantage of the discrete structure of the state variables in PODDS and develops the optimal componentwise MAP (CMAP) state estimator, which yields the MAP solution in each state variable. A fully-recursive process is provided for computation of this optimal estimator, followed by introducing a specific instance of the PODDS model suitable for regulatory networks observed through noisy time series data. The high performance of the proposed estimator is demonstrated by numerical experiments with a PODDS model of random regulatory networks.

Nonlinear state-space models are ubiquitous in modeling real-world dynamical systems. Sequential Monte Carlo (SMC) techniques, also known as particle methods, are a well-known class of parameter estimation methods for this general class of state-space models. Existing SMC-based techniques rely on excessive sampling of the parameter space, which makes their computation intractable for large systems or tall data sets. Bayesian optimization techniques have been used for fast inference in state-space models with intractable likelihoods. These techniques aim to find the maximum of the likelihood function by sequential sampling of the parameter space through a single SMC approximator. Various SMC approximators with different fidelities and computational costs are often available for sample-based likelihood approximation. In this paper, we propose a multi-fidelity Bayesian optimization algorithm for the inference of general nonlinear state-space models (MFBO-SSM), which enables simultaneous sequential selection of parameters and approximators. The accuracy and speed of the algorithm are demonstrated by numerical experiments using synthetic gene expression data from a gene regulatory network model and real data from the VIX stock price index.

Control of gene regulatory networks (GRNs) to shift gene expression from undesirable states to desirable ones has received much attention in recent years. Most of the existing methods assume that the cost of intervention at each state and time point, referred to as the immediate cost function, is fully known. In this paper, we employ the Partially-Observed Boolean Dynamical System (POBDS) signal model for a time sequence of noisy expression measurement from a Boolean GRN and develop a Bayesian Inverse Reinforcement Learning (BIRL) approach to address the realistic case in which the only available knowledge regarding the immediate cost function is provided by the sequence of measurements and interventions recorded in an experimental setting by an expert. The Boolean Kalman Smoother (BKS) algorithm is used for optimally mapping the available gene-expression data into a sequence of Boolean states, and then the BIRL method is efficiently combined with the Q-learning algorithm for quantification of the immediate cost function. The performance of the proposed methodology is investigated by applying a state-feedback controller to two GRN models: a melanoma WNT5A Boolean network and a p53-MDM2 negative feedback loop Boolean network, when the cost of the undesirable states, and thus the identity of the undesirable genes, is learned using the proposed methodology.

Analysis of genetic diversity and population structure in bread wheat is an essential step in their conservation, utilization, and breeding. Retrotransposons are ubiquitous and abundant a throughout the plant genomes, therefore extensively used as ideal molecular markers for genetic variability, DNA fingerprinting and genetic mapping studies in plant species. In the current research, we used two retrotransposon-based marker systems, inter-retrotransposon amplified polymorphisms (IRAPs), and the retrotransposon-microsatellite amplified polymorphisms (REMAPs) markers to evaluate the genetic diversity and survey activity of long terminal repeat retrotransposon (LTR-retrotransposon) elements in a collection of 49 bread wheat (Triticum aestivum L.) cultivars that mainly bred in Iran. In general, 90 and 126 loci were amplified using 9 IRAP and 20 REMAP primers, respectively. Both techniques produced a satisfactory number of bands for cultivar analysis; however, the technique IRAP, particularly single primer Nikita generated a large number of bands, indicating the wide activity of Nikita family under various environmental conditions of bread wheat. The percentage of polymorphic loci (PPL) in the studied collection for IRAP and REMAP markers was 81.78 and 86.40%, respectively. A model-based Bayesian method, Principal coordinate analysis (PCoA) and cluster analysis using Minimum Evolution (ME) algorithm hinted of the existence of two groups. This grouping was in agreement with the growing season and conformed by the high within-group bootstrap value. These results demonstrated that these markers developed using transpositionally active retrotransposons (RTNs) are efficient and reliable markers in determining level of genetic diversity and population structure in bread wheat in breeding programs.

This paper proposes a methodology to monitor and control gene regulatory networks (GRNs) via noisy measurements in an infinite observation space. Toward this end, we employ the partially observed Boolean dynamical system (POBDS) signal model. The proposed methodology consists of offline and online steps. In the offline step, a family of point-based methods is applied to the POBDS model to gather the necessary control policy prior to the online (execution) step. This is accomplished by developing efficient backup and belief expansion processes to make the computation scale with the log of the number of states, as opposed to the complexity of existing point-based methods, which grows with the number of states. In the online step, simultaneous monitoring and control is achieved by a one-step look-ahead search procedure using the optimal state estimation algorithm for the POBDS model, known as the Boolean Kalman filter (BKF), as well as the information gathered in the offline step. The online one-step look-ahead process confers robustness to changes in system dynamics, possibility of starting the execution process before the completion of the offline step. The use of the BKF for simultaneous monitoring and control during the online stage can be key in assessing possible side effects of intervention. The performance of the proposed methodology is investigated through a comprehensive set of numerical experiments using synthetic gene expression data generated from a melanoma GRN.

Data in many practical problems are acquired according to decisions or actions made by users or experts to achieve specific goals. For instance, policies in the mind of biologists during the intervention process in genomics and metagenomics are often reflected in available data in these domains, or data in cyber-physical systems are often acquired according to actions/decisions made by experts/engineers for purposes, such as control or stabilization. Quantification of experts' policies through available data, which is also known as reward function learning, has been discussed extensively in the literature in the context of inverse reinforcement learning (IRL). However, most of the available techniques come short to deal with practical problems due to the following main reasons: 1) lack of scalability: arising from incapability or poor performance of existing techniques in dealing with large systems and 2) lack of reliability: coming from the incapability of the existing techniques to properly learn the optimal reward function during the learning process. Toward this, in this brief, we propose a multifidelity Bayesian optimization (MFBO) framework that significantly scales the learning process of a wide range of existing IRL techniques. The proposed framework enables the incorporation of multiple approximators and efficiently takes their uncertainty and computational costs into account to balance exploration and exploitation during the learning process. The proposed framework's high performance is demonstrated through genomics, metagenomics, and sets of random simulated problems.

Design has become a salient part of most of the scientific and engineering tasks, embracing a wide range of domains including real experimental settings (e.g., material discovery or drug design), simulation-based design, and hyperparameter tuning. Model-based experimental design refers to a broad class of techniques, applicable to domains that a partial knowledge about the underlying process exists. Unlike entropy- based techniques which aim to reduce the whole uncertainty in the process, the mean objective cost of uncertainty (MOCU) is a rigorous statistically-oriented experimental design framework which takes the main objective into account during the decision making. However, the lack of scalability of this framework has restricted its application to domains with very small design spaces. This paper proposes a framework using the combination of Bayesian optimization and MOCU policy, which enables experimental design to much larger design spaces and systems. The reliability, scalability and efficiency of the proposed framework are investigated through experimental design for optimal structural intervention in gene regulatory networks.

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. Electronic supplementary material The online version of this article (10.1186/s12864-019-5720-3) contains supplementary material, which is available to authorized users.

Partially-observed Boolean dynamical systems (POBDS) are a general class of nonlinear state-space models that provide a rich framework for modeling many complex dynamical systems. The model consists of a hidden Boolean state process, observed through an arbitrary noisy mapping to a measurement space. The optimal minimum mean-square error (MMSE) POBDS state estimators are the Boolean Kalman Filter and Smoother. However, in many practical problems, the system parameters are not fully known and must be estimated. In this paper, for POBDS under model uncertainty, we derive an optimal Bayesian estimator for state and parameter estimation. The exact algorithms are derived for the case of discrete and finite parameter space, and for general parameter spaces, an approximate Markov-Chain Monte-Carlo (MCMC) implementation is introduced. We demonstrate the performance of the proposed methodology by means of numerical experiments with POBDS models of gene regulatory networks observed through noisy measurements.

In this paper, morphological effects of electric fields on avian erythrocytes (nucleated red blood cells) have been studied in detail. Morphological changes include rounding and cytoplasm transparency. It has been shown that the effect is non-thermal. Careful imaging and image analyses have been carried out to show that the degree of this effect is frequency-dependent, and has a higher conversion rate at higher temperatures. Furthermore, to better understand the mechanisms behind the morphological changes, we investigated the dedifferentiation hypothesis and performed a series of tests on avian erythrocytes including fluorescence spectroscopy for hemoglobin, and tests on human umbilical cord blood, mesenchymal stem cells, and bone marrow mesenchymal stem cells including flow-cytometry analysis for expression of certain markers and calcium staining. Bioelectromagnetics. 2019;40:375-390. © 2019 Bioelectromagnetics Society.

Background: According to research, ones well-being is an important factor for mental health and satisfaction in one’s personal life and people with more well-being are more satisfied with their lives. Objectives: The purpose of this study is to find a relationship between self-compassion, emotional quotient, and maternal styles with the well-being in Shiraz school students. Methods: Elementary school students and high school students in the academic year of 2018 were the research population. A total of 510 students who were chosen by multistage sampling method consisted this study sample so that based on the number of schools in Shiraz, 24 primary and high schools were chosen. Two classes from each school were selected and 9 to 11 students from each class participate in study. The tools used were maternal style questionnaire, self-compassion scale (SCS), emotional quotient scale (TMMS), and subjective well being inventory (SWB). Pearson correlation coefficient and multiple regression were applied for analyzing data. Results: According to the analysis, authoritative maternal style, self-compassion, and emotional quotient were important predictors of ones well-being and they can predict 81% of well-being in this study (P = 0.003). In addition, significant correlations were found between authoritative maternal style and self-compassion (correlation coefficient = 0.88, P = 0.007), authoritative maternal style and emotional quotient (correlation coefficient = 0.86, P = 0.008), and authoritative maternal style and subjective well-being (correlation coefficient = 0.94, P = 0.005). Conclusions: According to this study, maternal style, emotional quotient, and self-compassion play an important role in the society and individuals mental health and well-being, therefore, these factors should be attended, especially the authoritative maternal style, which has the most correlation with well-being.

Scientists are attempting to use models of ever-increasing complexity, especially in medicine, where gene-based diseases such as cancer require better modeling of cell regulation. Complex models suffer from uncertainty and experiments are needed to reduce this uncertainty. Because experiments can be costly and time-consuming, it is desirable to determine experiments providing the most useful information. If a sequence of experiments is to be performed, experimental design is needed to determine the order. A classical approach is to maximally reduce the overall uncertainty in the model, meaning maximal entropy reduction. A recently proposed method takes into account both model uncertainty and the translational objective, for instance, optimal structural intervention in gene regulatory networks, where the aim is to alter the regulatory logic to maximally reduce the long-run likelihood of being in a cancerous state. The mean objective cost of uncertainty (MOCU) quantifies uncertainty based on the degree to which model uncertainty affects the objective. Experimental design involves choosing the experiment that yields the greatest reduction in MOCU. This article introduces finite-horizon dynamic programming for MOCU-based sequential experimental design and compares it with the greedy approach, which selects one experiment at a time without consideration of the full horizon of experiments. A salient aspect of the article is that it demonstrates the advantage of MOCU-based design over the widely used entropy-based design for both greedy and dynamic programming strategies and investigates the effect of model conditions on the comparative performances.

Most of the existing methodologies for control of Gene Regulatory Networks (GRNs) assume that the immediate cost function at each state and time point is fully known. In this paper, we introduce an optimal control strategy for control of GRNs with unknown or partially-known immediate cost function. Toward this, we adopt a partially-observed Boolean dynamical system (POBDS) model for the GRN and propose an Inverse Reinforcement Learning (IRL) methodology for quantifying the imperfect behavior of experts, obtained via prior biological knowledge or experimental data. The constructed cost function then is used in finding the optimal infinite-horizon control strategy for the POBDS. The application of the proposed method using a single sequence of experimental data is investigated through numerical experiments using a melanoma gene regulatory network.

Background Advancements in gene expression technology allow acquiring cheap and abundant data for analyzing cell behavior. However, these technologies produce noisy, and often correlated, measurements on the transcriptional states of genes. The Boolean network model has been shown to be effective in capturing the complex dynamics of gene regulatory networks (GRNs). It is important in many applications, such as anomaly detection and optimal intervention, to be able to track the evolution of the Boolean states of a gene regulatory network using noisy time-series transcriptional measurements, which may be correlated in time. Results We propose efficient estimators for the Boolean states of GRNs using correlated time-series transcriptional measurements, where the nature of the correlation and of the measurements themselves are entirely arbitrary. More specifically, we propose new algorithms based on a hypothesis tree to compute optimal minimum mean square error (MMSE) filtering and smoothing state estimators for a Partially-Observed Boolean Dynamical System (POBDS) with correlated measurements. The algorithms are exact but may be computationally expensive for large state spaces or long time horizons, in which case a process for pruning the hypothesis tree is employed to obtain an approximation of the optimal MMSE estimators, while keeping computation tractable. Performance is assessed through a comprehensive set of numerical experiments based on the p53-MDM2 negative-feedback loop Boolean regulatory network, where the standard Boolean Kalman Filter (BKF) and Boolean Kalman Smoother (BKS) for uncorrelated measurements are compared to the corresponding new estimators for correlated measurements, called BKF-CORR and BKS-CORR, respectively.

Changes in population distributions over time are common in many applications. However, the vast majority of statistical learning theory takes place under the assumption that all points in the training data are identically distributed (and independent), that is, non-stationarity of the data is disregarded. In this paper, a version of the classic Linear Discriminant Analysis (LDA) classification rule is proposed for nonstationary data, using a linear-Gaussian state space model. This Nonstationary LDA (NSLDA) classification rule is based on the Kalman Smoother algorithm to estimate the evolving population parameters. In case the dynamics of the system are not fully known, a combination of the Expectation-Maximization (EM) algorithm and the Kalman Smoother is employed to simultaneously estimate population and statespace equation parameters. Performance is assessed in a set of numerical experiments using simulated data, where the average error rates obtained by NSLDA are compared to the error produced by a naive application of LDA to the pooled nonstationary data. Results demonstrate the promise of the proposed NSLDA classification rule.

Aim of the studyThe aim of present study was to investigate the role of job burnout and depression in self-destructive behavior in army personnel of Islamic Republic of Iran. To this end up, a conceptual structural model was examined.Subject or material and methodsParticipants were 215 active duty personnel of the Islamic Republic of Iran Army (age mean=31.07 years; SD=4.71) which they completed Suicide Behavior Questionnaire-Revised (SBQ-R), Self-Harm Inventory (SHI), Beck Depression Inventory-Second Edition (BDI-II-Persian), Maslach Burnout Inventory (MBI).ResultsThe measurement model revealed that all indicators, except self-efficacy, had a significant factor weight on their latent variable, therefore the model revised by omitting it and the modified measurement model showed good fitness according to GFI, AGFI, CFI, IFI, and RMSEA. Structural model results showed burnout has a direct effect on self-destructive behavior (β=0.24), depression has a direct effect on self-destructive behavior (β=0.56); burnout influenced depression indirectly (β=0.56); burnout has indirect (β=0.35) and total effects (β=0.59) on self-destructive behavior. Finally the fitness indices of the structural model showed relatively good fitness.DiscussionBurnout and depression directly increase the tendency to self-destruction among militants. Moreover, findings revealed that job burnout influence self-destruction by intensifying depression.Conclusionssome intervention must be considered to prevent self-destruction among militants

This paper is concerned with developing an adaptive controller for Partially-Observed Boolean Dynamical Systems (POBDS). Assuming that partial knowledge about the system can be modeled by a finite number of candidate models, then simultaneous identification and control of a POBDS is achieved using the combination of a state-feedback controller and a Multiple-Model Adaptive Estimation (MMAE) technique. The proposed method contains two main steps: first, in the offline step, the stationary control policy for the underlying Boolean dynamical system is computed for each candidate model. Then, in the online step, an optimal Bayesian estimator is modeled using a bank of Boolean Kalman Filters (BKFs), each tuned to a candidate model. The result of the offline step along with the estimated state by the bank of BKFs specify the control input that should be applied at each time point. The performance of the proposed adaptive controller is investigated using a Boolean network model constructed from melanoma gene expression data observed through RNA-seq measurements.

Partially-observed Boolean dynamical systems (POBDS) are a general class of nonlinear models with application in estimation and control of Boolean processes based on noisy and incomplete measurements. The optimal minimum mean square error (MMSE) algorithms for POBDS state estimation, namely, the Boolean Kalman filter (BKF) and Boolean Kalman smoother (BKS), are intractable in the case of large systems, due to computational and memory requirements. To address this, we introduce approximate MMSE filtering and smoothing algorithms based on the auxiliary particle filter (APF) method, which are called APF–BKF and APF–BKS, respectively. For joint state and parameter estimation, the APF–BKF is used jointly with maximum-likelihood (ML) methods for simultaneous state and parameter estimation in POBDS models. In the case the unknown parameters are discrete, the proposed ML adaptive filter consists of multiple APF–BKFs running in parallel, in a manner reminiscent of the Multiple Model Adaptive Estimation (MMAE) method in classical linear filtering theory. In the presence of continuous parameters, the proposed ML adaptive filter is based on an efficient particle-based expectation maximization (EM) algorithm for the POBDS model, which is based on a modified Forward Filter Backward Simulation (FFBSi) in combination with the APF–BKS. The performance of the proposed particle-based adaptive filters is assessed through numerical experiments using a POBDS model of the well-known cell cycle gene regulatory network observed through noisy RNA-Seq time series data.

High rates of comorbid depression and anxiety often complicate psychological interventions. The current preliminary study was designed to examine the efficacy of a unified and transdiagnostic model based on emotion regulation skills for patients with comorbid depression and anxiety. Forty-three participants with a diagnosis of major depression and varying levels of comorbid anxiety symptoms were randomly assigned to either a unified and transdiagnostic treatment group (UP; n = 20) or a wait-list control group (n = 23). The treatment group received 14 weekly UP sessions. Scores on the Beck Depression Inventory-II, Beck Anxiety Inventory, and Emotion Regulation Questionnaire were used as outcome measures. Data provided preliminary evidence that UP can be effective in reducing depressive and anxiety symptoms and in improving the use of effective emotion regulation strategies up to 3 months. Present results support the implementation of UP as a beneficial group treatment for patients with comorbid depressive ...

This paper is concerned with obtaining the infinite-horizon control policy for partially-observed Boolean dynamical systems (POBDS) when measurements take place in a finite observation space, with application to Boolean gene regulatory networks. The goal of control is to reduce the steady-state mass of undesirable states, which might be associated with disease. The idea behind the proposed method is to transfer the partially-observed Boolean states into a continuous observed state space known as belief space, and then employ the well-known value iteration method based on Point-Based Value Iteration (PBVI). The performance of the method is investigated using a Boolean network model constructed from melanoma gene-expression data observed through Bernoulli noise.