A subset of megakaryocytes regulates development of hematopoietic stem cell precursors.

Understanding the regulatory mechanisms facilitating hematopoietic stem cell (HSC) specification during embryogenesis is important for the generation of HSCs in vitro. Megakaryocyte emerged from the yolk sac and produce platelets, which are involved in multiple biological processes, such as preventing hemorrhage. However, whether megakaryocytes regulate HSC development in the embryonic aorta-gonad-mesonephros (AGM) region is unclear. Here, we use platelet factor 4 (PF4)-Cre;Rosa-tdTomato+ cells to report presence of megakaryocytes in the HSC developmental niche. Further, we use the PF4-Cre;Rosa-DTA (DTA) depletion model to reveal that megakaryocytes control HSC specification in the mouse embryos. Megakaryocyte deficiency blocks the generation and maturation of pre-HSCs and alters HSC activity at the AGM. Furthermore, megakaryocytes promote endothelial-to-hematopoietic transition in a OP9-DL1 coculture system. Single-cell RNA-sequencing identifies megakaryocytes positive for the cell surface marker CD226 as the subpopulation with highest potential in promoting the hemogenic fate of endothelial cells by secreting TNFSF14. In line, TNFSF14 treatment rescues hematopoietic cell function in megakaryocyte-depleted cocultures. Taken together, megakaryocytes promote production and maturation of pre-HSCs, acting as a critical microenvironmental control factor during embryonic hematopoiesis.

Ultrasound-targeted microbubble destruction (UTMD)-mediated miR-150-5p attenuates oxygen and glucose deprivation-induced cardiomyocyte injury by inhibiting TTC5 expression.

BACKGROUND: Cardiomyocyte injury is a typical feature in cardiovascular diseases. Changes in cardiomyocytes strongly affect the progression of cardiovascular diseases. This work aimed to investigate the biological function and potential mechanism of action of miR-150-5p in cardiomyocytes. METHODS AND RESULTS: A myocardial ischemia (MI) injury rat model was constructed to detect miR-150-5p and tetratricopeptide repeat domain 5 (TTC5) expression during heart ischemia injury. Primary cardiomyocytes were isolated for in vitro study. CCK-8 assays were used to detect cardiomyocyte viability. Western blots were used to detect TTC5 and P53 expression. qPCR was utilized to measure RNA expression of miR-150-5p and TTC5. The TUNEL assay was used to determine cell apoptosis. ELISA was used to determine cytokine (TNF-α, IL-1ß, IL-6, and IL-8) levels in heart tissues and cell culture supernatants. A dual-luciferase reporter assay was carried out to verify the binding ability between miR-150-5p and TTC5. Oxygen-glucose deprivation (OGD) treatment significantly inhibited cell viability. Ultrasound-targeted microbubble destruction (UTMD)-mediated uptake of miR-150-5p inverted these results. Additionally, UTMD-mediated uptake of miR-150-5p retarded the effects of OGD treatment on cell apoptosis. Besides, UTMD-mediated uptake of miR-150-5p counteracted the effects of OGD treatment on the inflammatory response by regulating cytokine (TNF-α, IL-1ß, IL-6, and IL-8) levels. For the mechanism of the protective effect on the heart, we predicted and confirmed that miR-150-5p bound to TTC5 and inhibited TTC5 expression. CONCLUSIONS: UTMD-mediated uptake of miR-150-5p attenuated OGD-induced primary cardiomyocyte injury by inhibiting TTC5 expression. This discovery contributes toward further understanding the progression of primary cardiomyocyte injury.

Preliminary study on the function of TMEM50A and its correlation with the RH genes.

OBJECTIVES: The purpose of this study was to investigate the association and impact of TMEM50A on RH genes activity and function. BACKGROUND: SMP1 is located on chromosome 1p36.11 in the RH gene locus, between the RHD and RHCE gene, where its position may be linked to RH haplotypes and contribute to selective pressures regarding certain RH haplotypes. TMEM50A is encoded by the SMP1 located in the intergenic region of RH, its influence on the function of the RH genes remains unclear. METHODS: The expression of TMEM50A was regulated by transfection of plasmid and siRNA in K562 cell model. Western blot and real-time PCR were used to detect possible expression changes in the RH. The ammonium transport function of cells was monitored using pH-sensitive dye, while transcriptome sequencing was used to predict the potential function of TMEM50A. RESULTS: The overexpression of TMEM50A significantly up-regulated RHCE gene activity (63.56%). The inhibition of TMEM50A resulted in significantly decreased RHCE (41.82%) and RHD expression (27.35%). Compared to control group, there was no significant change in the NH4 + transport function of cells in the overexpressed TMEM50A group. Transcriptome analysis showed that TMEM50A not only affected the transcription of target gene through splicing activities, but also played a role in the development of embryonic nervous system. CONCLUSIONS: TMEM50A may regulate the expression of RH gene by affecting the stability of RH mRNA through splicing function. It speculates that TMEM50A may play an important role in the development of embryonic nervous system.

A Tutorial on Hardware-Implemented Fault Injection and Online Fault Diagnosis for High-Speed Trains.

Electrical drive systems are the core of high-speed trains, providing energy transmission from electric power to traction force. Therefore, their safety and reliability topics are always active in practice. Among the current research, fault injection (FI) and fault diagnosis (FD) are representative techniques, where FI is an important way to recur faults, and FD ensures the recurring faults can be successfully detected as soon as possible. In this paper, a tutorial on a hardware-implemented (HIL) platform that blends FI and FD techniques is given for electrical drive systems of high-speed trains. The main contributions of this work are fourfold: (1) An HIL platform is elaborated for realistic simulation of faults, which provides the test and verification environment for FD tasks. (2) Basics of both the static and dynamic FD methods are reviewed, whose purpose is to guide the engineers and researchers. (3) Multiple performance indexes are defined for comprehensively evaluating the FD approaches from the application viewpoints. (4) It is an integrated platform making the FI and FD work together. Finally, a summary of FD research based on the HIL platform is made.

A Review of Intelligent Fault Diagnosis for High-Speed Trains: Qualitative Approaches.

For ensuring the safety and reliability of high-speed trains, fault diagnosis (FD) technique plays an important role. Benefiting from the rapid developments of artificial intelligence, intelligent FD (IFD) strategies have obtained much attention in the field of academics and applications, where the qualitative approach is an important branch. Therefore, this survey will present a comprehensive review of these qualitative approaches from both theoretical and practical aspects. The primary task of this paper is to review the current development of these qualitative IFD techniques and then to present some of the latest results. Another major focus of our research is to introduce the background of high-speed trains, like the composition of the core subsystems, system structure, etc., based on which it becomes convenient for researchers to extract the diagnostic knowledge of high-speed trains, where the purpose is to understand how to use these types of knowledge. By reasonable utilization of the knowledge, it is hopeful to address various challenges caused by the coupling among subsystems of high-speed trains. Furthermore, future research trends for qualitative IFD approaches are also presented.

Sensors Information Fusion System with Fault Detection Based on Multi-Manifold Regularization Neighborhood Preserving Embedding.

Electrical drive systems play an increasingly important role in high-speed trains. The whole system is equipped with sensors that support complicated information fusion, which means the performance around this system ought to be monitored especially during incipient changes. In such situation, it is crucial to distinguish faulty state from observed normal state because of the dire consequences closed-loop faults might bring. In this research, an optimal neighborhood preserving embedding (NPE) method called multi-manifold regularization NPE (MMRNPE) is proposed to detect various faults in an electrical drive sensor information fusion system. By taking locality preserving embedding into account, the proposed methodology extends the united application of Euclidean distance of both designated points and paired points, which guarantees the access to both local and global sensor information. Meanwhile, this structure fuses several manifolds to extract their own features. In addition, parameters are allocated in diverse manifolds to seek an optimal combination of manifolds while entropy of information with parameters is also selected to avoid the overweight of single manifold. Moreover, an experimental test based on the platform was built to validate the MMRNPE approach and demonstrate the effectiveness of the fault detection. Results and observations show that the proposed MMRNPE offers a better fault detection representation in comparison with NPE.

Performance-Based Hierarchical Fault-Tolerant Control for Closed-Loop Systems With Multiplicative Faults: A Data-Driven Design Method.

Fault-tolerant control (FTC) is vital for the safety and reliability of automatic systems. Most of the existing FTC methods are developed for open-loop systems subject to additive faults, regardless of the widely present control loops and multiplicative faults within systems. In this article, a performance-based FTC strategy is proposed for the closed-loop systems with multiplicative faults. Considering the high efforts in modeling complex systems, the proposed FTC strategy is realized in the data-driven context. Specifically, a nominal feedback-feedforward controller is first established for the fault-free systems. By selecting the system stability and reference tracking behavior as the key performance indices, two performance evaluators are constructed to detect and classify the occurred multiplicative faults based on the fault-induced effects on the system performance. Then, with the aid of the coprime factorization technique, the multiplicative faults, in the form of additive perturbations to the system coprime factors, are estimated utilizing the closed-loop process data. Furthermore, based on the fault knowledge, a hierarchical fault-tolerant tracking controller is developed according to the levels of system performance degradations, where the functional controller parameters are reconfigured with different priorities. Finally, case studies are provided to validate the effectiveness of the proposed method.

Transfer Learning-Motivated Intelligent Fault Diagnosis Designs: A Survey, Insights, and Perspectives.

Over the last decade, transfer learning has attracted a great deal of attention as a new learning paradigm, based on which fault diagnosis (FD) approaches have been intensively developed to improve the safety and reliability of modern automation systems. Because of inevitable factors such as the varying work environment, performance degradation of components, and heterogeneity among similar automation systems, the FD method having long-term applicabilities becomes attractive. Motivated by these facts, transfer learning has been an indispensable tool that endows the FD methods with self-learning and adaptive abilities. On the presentation of basic knowledge in this field, a comprehensive review of transfer learning-motivated FD methods, whose two subclasses are developed based on knowledge calibration and knowledge compromise, is carried out in this survey article. Finally, some open problems, potential research directions, and conclusions are highlighted. Different from the existing reviews of transfer learning, this survey focuses on how to utilize previous knowledge specifically for the FD tasks, based on which three principles and a new classification strategy of transfer learning-motivated FD techniques are also presented. We hope that this work will constitute a timely contribution to transfer learning-motivated techniques regarding the FD topic.

Time-varying gain extended state observer-based adaptive optimal control for disturbed unmanned helicopter.

In this paper, the robust adaptive optimal tracking control problem is addressed for the disturbed unmanned helicopter based on the time-varying gain extended state observer (TVGESO) and adaptive dynamic programming (ADP) methods. Firstly, a novel TVGESO is developed to tackle the unknown disturbance, which can overcome the drawback of initial peaking phenomenon in the traditional linear ESO method. Meanwhile, compared with the nonlinear ESO, the proposed TVGESO possesses easier and rigorous stability analysis process. Subsequently, the optimal tracking control issue for the original unmanned helicopter system is transformed into an optimization stabilization problem. By means of the ADP and neural network techniques, the feedforward controller and optimal feedback controller are skillfully designed. Compared with the conventional backstepping approach, the designed anti-disturbance optimal controller can make the unmanned helicopter accomplish the tracking task with less energy. Finally, simulation comparisons demonstrate the validity of the developed control scheme.

Large-Scale Data-Driven Optimization in Deep Modeling With an Intelligent Decision-Making Mechanism.

This study focuses on building an intelligent decision-making attention mechanism in which the channel relationship and conduct feature maps among specific deep Dense ConvNet blocks are connected to each other. Thus, develop a novel freezing network with a pyramid spatial channel attention mechanism (FPSC-Net) in deep modeling. This model studies how specific design choices in the large-scale data-driven optimization and creation process affect the balance between the accuracy and effectiveness of the designed deep intelligent model. To this end, this study presents a novel architecture unit, which is termed as the "Activate-and-Freeze" block on popular and highly competitive datasets. In order to extract informative features by fusing spatial and channel-wise information together within local receptive fields and boost the representation power, this study constructs a Dense-attention module (pyramid spatial channel (PSC) attention) to perform feature recalibration, and through the PSC attention to model the interdependence among convolution feature channels. We join the PSC attention module in the activating and back-freezing strategy to search for one of the most important parts of the network for extraction and optimization. Experiments on various large-scale datasets demonstrate that the proposed method can achieve substantially better performance for improving the ConvNets representation power than the other state-of-the-art deep models.

A Segmented Iterative Learning Scheme-Based Distributed Fault Estimation for Switched Interconnected Nonlinear Systems.

In this article, a distributed fault estimation (DFE) approach for switched interconnected nonlinear systems (SINSs) with time delays and external disturbances is proposed using a novel segmented iterative learning scheme (SILS). First, through the utilization of interrelated information among subsystems, a distributed iterative learning observer is developed to enhance the accuracy of fault estimation results, which can realize the fault estimation of all subsystems under time delays and external disturbances. Simultaneously, to facilitate rapid fault information tracking and significantly reduce sensitivity to interference, a new SILS-based fault estimation law is constructed by combining the idea of segmented design with the method of variable gain. Then, an assessment of the convergence of the established fault estimation methodology is conducted, and the configurations of observer gain matrices and iterative learning gain matrices are duly accomplished. Finally, simulation results are showcased to demonstrate the superiority and feasibility of the developed fault estimation approach.

Autophagy regulates the maturation of hematopoietic precursors in the embryo.

An understanding of the mechanisms regulating embryonic hematopoietic stem cell (HSC) development would facilitate their regeneration. The aorta-gonad-mesonephros region is the site for HSC production from hemogenic endothelial cells (HEC). While several distinct regulators are involved in this process, it is not yet known whether macroautophagy (autophagy) plays a role in hematopoiesis in the pre-liver stage. Here, we show that different states of autophagy exist in hematopoietic precursors and correlate with hematopoietic potential based on the LC3-RFP-EGFP mouse model. Deficiency of autophagy-related gene 5 (Atg5) specifically in endothelial cells disrupts endothelial to hematopoietic transition (EHT), by blocking the autophagic process. Using combined approaches, including single-cell RNA-sequencing (scRNA-seq), we have confirmed that Atg5 deletion interrupts developmental temporal order of EHT to further affect the pre-HSC I maturation, and that autophagy influences hemogenic potential of HEC and the formation of pre-HSC I likely via the nucleolin pathway. These findings demonstrate a role for autophagy in the formation/maturation of hematopoietic precursors.

[Effects of age on left ventricular twist-displacement loop assessed by velocity vector imaging].

OBJECTIVE: To assess effect of age on the characteristic of left ventricular (LV) twist-displacement loop in health volunteers by velocity vector imaging (VVI) and to provide a new method for LV function evaluation in clinic. METHODS: After obtaining basal and apical LV short-axis images in 98 healthy volunteers (18-75 years old) by 2-dimensional echocardiography, we use VVI software to analysis LV twist motion and radial displacement at each plane off-line. The peak LV twist (Ptw), the peak untwist velocity (PutwV), the proportion of untwist in isovolumetric relaxation period (Iutw%) and LV radial displacement (Dis) were measured and calculated. Then we constructed LV twist-displacement loop and compared the characteristic of them among different groups. RESULTS: Ptw increased gradually with the increase in age. The biggest PutwV was in the group of 30-60 years old. Iutw% increased gradually before 60 years old, then decreased after that. Dis was not obviously different among the three groups. The characteristic of LV twist-displacement loop was like the configuration of 8. There was a linear relation between twist and displacement during systole, and the slope increased gradually with the increase in age. During early diastole, the relatively small radial expanding displacement displayed with untwisting, resulting in a much steeper twist-displacement relationship curve occurred in each group, which was getting smooth gradually when the radial expanding displacement increased during mid to late diastole. CONCLUSIONS: VVI can be used to effectively and noninvasively assess LV twist-displacement loop with change in age and provide important information for LV function. The effect of age must take into account when evaluate the LV function by the twist-displacement loop.

A Latent Representation Generalizing Network for Domain Generalization in Cross-Scenario Monitoring.

Cross-scenario monitoring requires domain generalization (DG) for changed knowledge when auxiliary information is unavailable and only one source scenario is involved. In this article, a latent representation generalizing network (LRGN) is proposed to learn transferable knowledge through generalizing the latent representations for cross-scenario monitoring in perimeter security. LRGN is composed of a sequential-variational generative adversarial network (SVGAN), a coupled SVGAN (Co-SVGAN), and a knowledge-aggregated SVGAN. First, the Co-SVGAN can learn domain-invariant latent representations to model dual-domain joint distribution of background data, which is usually sufficient in the source and target scenarios. Deceptive domain shifts are generated based on the domain-invariant latent representations without auxiliary information. Then, SVGAN models the changing knowledge by estimating the distribution of domain shifts. Furthermore, the knowledge-aggregated SVGAN can transfer the learned domain-invariant knowledge from Co-SVGAN for generalizing the latent representations through approximating the distribution of domain shifts. Accordingly, LRGN is trained by a four-phase optimization strategy for DG through generating target-scenario samples of concerned events based on the generalized latent representations. The feasibility and effectiveness of the proposed method are validated through real-field experiments of perimeter security applications in two scenarios.

Fault Detection for Nonlinear Dynamic Systems With Consideration of Modeling Errors: A Data-Driven Approach.

This article is concerned with data-driven realization of fault detection (FD) for nonlinear dynamic systems. In order to identify and parameterize nonlinear Hammerstein models using dynamic input and output data, a stacked neural network-aided canonical variate analysis (SNNCVA) method is proposed, based on which a data-driven residual generator is formed. Then, the threshold used for FD purposes is obtained via quantiles-based learning, where both estimation errors and approximation errors are considered. Compared with the existing work, the main novelties of this study include: 1) SNNCVA provides a new parameterization strategy for nonlinear Hammerstein systems by utilizing input and output data only; 2) the associated residual generator can ensure FD performance where both the system model and its nonlinearity are unknown; and 3) with consideration of modeling-induced errors, the quantiles are invoked and used to provide a reliable FD threshold in situations where only limited samples are available. Studies on a nonlinear hot rolling mill process demonstrate the effectiveness of the proposed method.

Imputation of Missing Values in Time Series Using an Adaptive-Learned Median-Filled Deep Autoencoder.

Missing values are ubiquitous in industrial data sets because of multisampling rates, sensor faults, and transmission failures. The incomplete data obstruct the effective use of data and degrade the performance of data-driven models. Numerous imputation algorithms have been proposed to deal with missing values, primarily based on supervised learning, that is, imputing the missing values by constructing a prediction model with the remaining complete data. They have limited performance when the amount of incomplete data is overwhelming. Moreover, many methods have not considered the autocorrelation of time-series data. Thus, an adaptive-learned median-filled deep autoencoder (AM-DAE) is proposed in this study, aiming to impute missing values of industrial time-series data in an unsupervised manner. It continuously replaces the missing values by the median of the input data and its reconstruction, which allows the imputation information to be transmitted with the training process. In addition, an adaptive learning strategy is adopted to guide the AM-DAE paying more attention to the reconstruction learning of nonmissing values or missing values in different iteration periods. Finally, two industrial examples are used to verify the superior performance of the proposed method compared with other advanced techniques.

A Hybrid Design of Fault Detection for Nonlinear Systems Based on Dynamic Optimization.

To ensure the safety of an automation system, fault detection (FD) has become an active research topic. With the development of artificial intelligence, model-free FD strategies have been widely investigated over the past 20 years. In this work, a hybrid FD design approach that combines data-driven and model-based is developed for nonlinear dynamic systems whose information is not known beforehand. With the aid of a Takagi-Sugeno (T-S) fuzzy model, the nonlinear system can be identified through a group of least-squares-based optimization. The associated modeling errors are taken into account when designing residual generators. In addition, statistical learning is adopted to obtain an upper bound of modeling errors, based on which an optimization problem is formulated to determine a reliable FD threshold. In the online FD decision, an event-triggered strategy is also involved in saving computational costs and network resources. The effectiveness and feasibility of the proposed hybrid FD method are illustrated through two simulation studies on nonlinear systems.

A complex-valued slow independent component analysis based incipient fault detection and diagnosis method with applications to wastewater treatment processes.

Multivariate statistical process monitoring are the essential approaches to achieve better prognostics and health management (PHM) of process industries. However, incipient faults and complex behaviors (such as nonlinearity and dynamics) always render the traditional multivariate statistical process monitoring approaches inadequate. Thus, a complex-valued slow independent component analysis (CSICA) is proposed, which is able to extract optimized features from a complex-valued matrix containing both of raw data and their changing rates by resorting to a complex-valued independent component analysis operation and a batch of phase shifts. These features, named slow independent components (SICs), not only guarantee the statistical independence but also capture slowly-changing patterns, thus refining both dynamic and non-Gaussian information mostly related with incipient faults. The proposed algorithm together with novel statistics, Is2, If2 and SPE, as well as their control limits can sequentially detect incipient faults effectively. Then, together with the novel differential mapping reconstructed contribution plot (DM-RCP) and Granger causality analysis, the proposed method can accurately locate rooting causes of incipient faults. Finally, the proposed framework of process monitoring is validated through two data sets from a simulation platform and an oxidation-ditch-based wastewater treatment plant, respectively. The results demonstrate that the proposed method can achieve more accurate and efficient performances than conventional methods.

Kernel-based PMP structure for nonlinear industrial quality-related process monitoring.

Quality-related process monitoring as a supervised technology has increasingly attracted attention in complex industries. Various approaches have been studied to cope with this issue. Nevertheless, these methods cannot reasonably decompose the process variable space, resulting in deficiencies in monitoring quality-related faults. To handle this issue, this paper presents an orthogonal kernel partial least squares improved kernel least squares with a preprocessing-modeling-postprocessing (PMP) structure to implement quality-related process monitoring with more proper decomposition and more straightforward monitoring logic. Compared with the previous approaches, a nonlinear preprocessing technology is presented to eliminate the quality-unrelated knowledge of process variables, enormously enhancing the interpretability of modeling and improving the monitoring efficiency. Then, a proper decomposition is presented to decompose the kernel matrix into two orthogonal parts, significantly improving the monitoring performance. The theoretical analysis of the proposed method is provided in this paper. Finally, two cases indicate the validity and superiority of the proposed method.

Safe Adaptive Policy Transfer Reinforcement Learning for Distributed Multiagent Control.

Multiagent reinforcement learning (RL) training is usually difficult and time-consuming due to mutual interference among agents. Safety concerns make an already difficult training process even harder. This study proposes a safe adaptive policy transfer RL approach for multiagent cooperative control. Specifically, a pioneer and follower off-policy policy transfer learning (PFOPT) method is presented to help follower agents acquire knowledge and experience from a single well-trained pioneer agent. Notably, the designed approach can transfer both the policy representation and sample experience provided by the pioneer policy in the off-policy learning. More importantly, the proposed method can adaptively adjust the learning weight of prior experience and exploration according to the Wasserstein distance between the policy probability distributions of the pioneer and the follower. Case studies show that the distributed agents trained by the proposed method can complete a collaborative task and acquire the maximum rewards while minimizing the violation of constraints. Moreover, the proposed method can also achieve satisfactory performance in terms of learning speed and success rate.