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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.

A major goal of systems biology is to model accurately the complex dynamical behavior of gene regulatory networks (GRNs). Despite several advancements that have been made in inference of GRNs, two main issues continue to make the problem challenging: 1) nonidentifiability of parameters and 2) limited amounts of data. Thus, it becomes necessary to experimentally perturb or excite the system into different states. This perturbation process disrupts the expression of genes from active to inactive, or vice versa, at each time point. Another issue is the partial observability of the gene states, which must be inferred indirectly from noisy gene expression measurements. In this article, this latter issue is accounted for by employing the partially observed Boolean dynamical system signal model for the data and applying optimal state estimation. Then, the optimal finite-horizon perturbation policy is derived to achieve the highest possible expected performance for the maximum a posteriori estimator under a small perturbation cost. Performance is assessed through numerical experiments using the well-known p53-MDM2 negative-feedback loop regulatory model and synthetic GRNs.

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.

Partially-Observed Boolean dynamical systems (POBDS) are a general class of nonlinear state-space models consisting of a hidden Boolean state process observed through an arbitrary noisy mapping to a measurement space. The huge uncertainty present in systems/processes, along with the time-limit constraints, necessitate real-time or online joint state and parameter estimation of POBDS. In this manuscript, we present a real-time joint state and parameter estimation framework for POBDS. The proposed framework relies on a complete-sufficient statistic of parameters, where a joint state and parameter estimation is achieved based on the combination of online expectation-maximization method and the optimal MMSE state estimator for POBDS, called Boolean Kalman filter. The proposed method’s performance is assessed through a POBDS model for Boolean gene regulatory networks observed through noisy measurements.

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.

Engineering systems are often composed of many subsystems that interact with each other. These subsystems, referred to as disciplines, contain many types of uncertainty and in many cases are feedback-coupled with each other. In designing these complex systems, one needs to assess the stationary behavior of these systems for the sake of stability and reliability. This requires the system level uncertainty analysis of the multidisciplinary systems, which is often computationally intractable. To overcome this issue, techniques have been developed for capturing the stationary behavior of the coupled multidisciplinary systems through available data of individual disciplines. The accuracy and convergence of the existing techniques depend on a large amount of data from all disciplines, which are not available in many practical problems. Toward this, we have developed an adaptive methodology that adds the minimum possible number of samples from individual disciplines to achieve an accurate and reliable uncertainty propagation in coupled multidisciplinary systems. The proposed method models each discipline function via Gaussian process (GP) regression to derive a closed-form policy. This policy sequentially selects a new sample point that results in the highest uncertainty reduction over the distribution of the coupling design variables. The effectiveness of the proposed method is demonstrated in the uncertainty analysis of an aerostructural system and a coupled numerical example.

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.

Stabilization of complex cyber-physical systems is extremely important in keeping the critical infrastructure and the environment safe. This is, in particular, critical in coupled multidisciplinary systems with several subsystems interacting with each other in an uncertain environment. The design of stabilized complex systems depends on a proper set of inputs to these subsystems, in such a way that the best stationary behavior of these systems is achieved. Despite several attempts for stabilizing the coupled multidisciplinary systems, the existing techniques still have their critical limitations and issues due to the unrealistic deterministic assumption in some cases as well as inability in handling large-scale systems. In this paper, we introduce a Bayesian framework using the combination of Bayesian optimization technique and Gibbs sampling method, which enables scalable, efficient and fast learning of the best input to achieve the best design of multidisciplinary systems. The accuracy and speed of the proposed framework will be demonstrated in numerical experiments using an aerodynamics- structures system and a mathematical example.

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.

This paper proposes an approach for finite-horizon control of partially-observed Boolean dynamical systems (POBDS) with uncertain continuous control input and infinite observation space. To cope with the partial observability of states, the proposed method first maps the POBDS to an unnormalized belief space. The nonlinear dynamics in this continuous belief space are linearized over a nominal trajectory. Then, the optimal feedback controller is derived, based on the well-known linear quadratic regulator (LQR), to push the system to follow the nominal trajectory. This nominal trajectory is computed in a planning stage before starting execution, and updated efficiently during execution, whenever the system is found to deviate from the nominal trajectory. We prove that, under mild regularization conditions, the proposed controller approaches the cost of the nominal trajectory as the linearization error approaches zero. The performance of the proposed controller is demonstrated by numerical experiments with a Melanoma gene regulatory network observed through noisy gene expression measurements.

In a previous contribution, a method was proposed for on-line fault detection in Boolean gene regulatory networks based on the Boolean Kalman Filter and hypothesis testing on next-generation sequencing (NGS) data. In this paper, we present a related methodology, which differs in two main points: (1) it uses the Boolean Kalman Smoother on the entire data set stored off-line; (2) it employs a maximum-likelihood approach for simultaneous change detection and categorization. The efficacy of the proposed methodology is assessed using a p53-MDM2 negative feedback loop Boolean network with stuck-at faults that model molecular events commonly found in cancer.

We present a framework for the simultaneous estimation of state and parameters of partially observed Boolean dynamical systems (POBDS). Simultaneous state and parameter estimation is achieved through the combined use of the Boolean Kalman filter and Boolean Kalman smoother, which provide the minimum mean-square error state estimators for the POBDS model, and maximum-likelihood (ML) parameter estimation; in the presence of continuous parameters, ML estimation is performed using the expectation–maximization algorithm. The performance of the proposed ML adaptive filter is demonstrated by numerical experiments with a POBDS model of gene regulatory networks observed through noisy next-generation sequencing (RNA-seq) time series data using the well-known p53-MDM2 negative-feedback loop gene regulatory model.

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.

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.

Partially-observed Boolean dynamical systems (POBDS) are large and complex dynamical systems capable of being monitored through various sensors. However, time, storage, and economical constraints may impede the use of all sensors for estimation purposes. Thus, developing a procedure for selecting a subset of sensors is essential. The optimal minimum mean-square error (MMSE) POBDS state estimator is the Boolean Kalman Filter (BKF) and Smoother (BKS). Naturally, the performance of these estimators strongly depends on the choice of sensors. Given a finite subsets of sensors, for a POBDS with a finite observation space, we introduce the optimal procedure to select the best subset which leads to the smallest expected mean-square error (MSE) of the BKF over a finite horizon. The performance of the proposed sensor selection methodology is demonstrated by numerical experiments with a p53-MDM2 negative-feedback loop gene regulatory network observed through Bernoulli noise.

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.

This paper is concerned with optimal estimation of the state of a Boolean dynamical systems observed through correlated noisy Boolean measurements. The optimal Minimum Mean-Square Error (MMSE) state estimator for general Partially-Observed Boolean Dynamical Systems (POBDS) can be computed via the Boolean Kalman Filter (BKF). However, thus far in the literature only the case of white observation noise has been considered. In this paper, we develop the optimal MMSE filter for a class of POBDS with correlated Boolean measurements. The performance of the proposed method is subsequently investigated using the p53-MDM2 negative feedback loop genetic network model.

This paper is concerned with the problem of stochastic control of gene regulatory networks (GRNs) observed indirectly through noisy measurements and with uncertainty in the intervention inputs. The partial observability of the gene states and uncertainty in the intervention process are accounted for by modeling GRNs using the partially observed Boolean dynamical system (POBDS) signal model with noisy gene expression measurements. Obtaining the optimal infinite-horizon control strategy for this problem is not attainable in general, and we apply reinforcement learning and Gaussian process techniques to find a near-optimal solution. The POBDS is first transformed to a directly observed Markov decision process in a continuous belief space, and the Gaussian process is used for modeling the cost function over the belief and intervention spaces. Reinforcement learning then is used to learn the cost function from the available gene expression data. In addition, we employ sparsification, which enables the control of large partially observed GRNs. The performance of the resulting algorithm is studied through a comprehensive set of numerical experiments using synthetic gene expression data generated from a melanoma gene regulatory network.

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.

We propose a novel methodology for fault detection and diagnosis in partially-observed Boolean dynamical systems (POBDS). These are stochastic, highly nonlinear, and derivativeless systems, rendering difficult the application of classical fault detection and diagnosis methods. The methodology comprises two main approaches. The first addresses the case when the normal mode of operation is known but not the fault modes. It applies an innovations filter (IF) to detect deviations from the nominal normal mode of operation. The second approach is applicable when the set of possible fault models is finite and known, in which case we employ a multiple model adaptive estimation (MMAE) approach based on a likelihood-ratio (LR) statistic. Unknown system parameters are estimated by an adaptive expectation-maximization (EM) algorithm. Particle filtering techniques are used to reduce the computational complexity in the case of systems with large state-spaces. The efficacy of the proposed methodology is demonstrated by numerical experiments with a large gene regulatory network (GRN) with stuck-at faults observed through a single noisy time series of RNA-seq gene expression measurements.

Objective: Toxoplasma gondii is a widespread protozoan parasite that infects a broad range of warm blooded animals and humans. The present study was investigated to evaluate testosterone, alkaline phosphatase activity and malondialdehyde in male rats experimentally infected by Toxoplasma gondii, RH strain. Material & Methods: Male Wistar rats (n=20) were allocated into two groups, group one (n=10) that received 0.6 cc tachyzoites of T. gondii intraperitoneally (I.P.), and control group (n=10) that received nothing. Animals were kept in standard conditions. 30 days after inducing Toxoplasma infection, 5cc blood was collected for assessment of serum testosterone, alkaline phosphatase and malondialdehyde levels. Epididymis tissues of Rats in whole groups were removed and prepared for analysis. Results: Alkaline phosphatase, and Testosterone were significantly increased in group that was infected by T.gondii in comparison to control group (P 0.05). Epididymis weights in toxoplasmosis group was significantly decreased in comparison to control group (P

External control of a genetic regulatory network is used for the purpose of avoiding undesirable states, such as those associated with disease. This paper proposes a strategy for state-feedback infinite-horizon control of Partially-Observed Boolean Dynamical Systems (POBDS) using a single time series of Next-Generation Sequencing (NGS) RNA-seq data. A separation principle is assumed, whereby first the optimal stationary policy is obtained offline by solving Bellman's equation, and then an optimal MMSE observer, the Boolean Kalman Filter, is employed for online implementation of the policy using the RNA-seq observations of the evolving system. Performance is investigated using a Boolean network model of the mutated mammalian cell cycle and simulated RNA-seq observations.

This paper is concerned with state estimation at a fixed time point in a given time series of observations of a Boolean dynamical system. Towards this end, we introduce the Boolean Kalman Smoother, which provides an efficient algorithm to compute the optimal MMSE state estimator for this problem. Performance is investigated using a Boolean network model of the p53-MDM2 negative feedback loop gene regulatory network observed through time series of Next-Generation Sequencing (NGS) data.