Embryonic stem cells (ESCs) are promising resources for clinical therapies due to their potential to generate multiple cell types. The dynamic expression of de novo methyltransferases (Dnmt3a and ...Dnmt3b) is essential to ESCs; however, the regulatory mechanism of Dnmt3a or Dnmt3b expression in ESCs is still poorly understood. Here, we reported that decreased expression of microRNA-495 (miR-495) in the first 2days of embryoid body (EB) formation was required for mouse embryonic stem cell (mESC) differentiation because repressed mesoderm and endoderm lineages were detected in ectopic miR-495 expression mESCs. This effect was reversed by the function blockade of miR-495. We identified Dnmt3a as a functional target of miR-495 and showed that endogenous miR-495 repressed the expression of Dnmt3a in mESCs. Furthermore, the effect of miR-495 on mESCs could be eliminated by Dnmt3a overexpression. Moreover, miR-495 had no effect on the expression of Dnmt3b despite the findings obtained from previous studies that mainly focused on the common characteristics of the regulatory mechanisms of Dnmt3a and Dnmt3b expression. Thus, our studies not only uncovered a previously uncharacterized function of miR-495 in mESC differentiation but also generated a new idea to explore the mechanisms governing the functional difference between Dnmt3a and Dnmt3b.
•MiR-495 expression displays a temporary decreasing during mESC differentiation.•Function blockade of miR-495 increases mesendoderm lineage specification.•Dnmt3a is proved to be a functional target of miR-495 in mESCs.•MiR-495 suppresses mesendoderm differentiation via direct targeting of Dnmt3a.
Existing network embedding algorithms based on generative adversarial networks (GANs) improve the robustness of node embeddings by selecting high-quality negative samples with the generator to play ...against the discriminator. Since most of the negative samples can be easily discriminated from positive samples in graphs, their poor competitiveness weakens the function of the generator. Inspired by the sales skills in the market, in this article, we present tripartite adversarial training for network embeddings (TriATNE), a novel adversarial learning framework for learning stable and robust node embeddings. TriATNE consists of three players: 1) producer; 2) seller; and 3) customer. The producer strives to learn the representation of each sample (node pair), making it easy for the customer to differentiate between the positive and the negative, while the seller tries to confuse the customer by selecting realistic-looking samples. The customer, a biased evaluation metric, provides feedback for training the producer and the seller. To further enhance the robustness of node embedding, we model the customer as a two-layer neural network, where each unit in the hidden layer can be regarded as a customer with different preferences. TriATNE also plays against the producer by adjusting the weight of each customer. We test the performance of TriATNE on two common tasks: classification as well as link prediction. The experimental results on various publicly available datasets show that TriATNE can exploit the network structure well.
This article investigates the fault detection problem of unmanned marine vehicles (UMVs) under the influence caused by replay attacks. First, the dynamics of UMV are modeled by a Takagi--Sugeno ...(T--S) fuzzy system with an unknown membership function, which includes the nonlinear coupling of the internal state of the system, the environmental multisource disturbance as well as the potential thruster failure on UMV. Then, the possible replay attack from the sensor to the shore-based center is considered, and a switching-type attack tolerant fault detection filter is designed. Sufficient conditions are given to ensure that the filtering augmented system is stable and with stochastic finite frequency <inline-formula><tex-math notation="LaTeX">H_{\infty }</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">H_{-}</tex-math></inline-formula> performances, which reflect the robustness to the disturbance and sensitivity to the fault. On this basis, through a series of mathematical processing, the linear solvable conditions for the design of fault detection filters are obtained. Finally, the effectiveness of the proposed algorithm is verified by simulations.
Postpartum depression (PPD), the depressive episodes following delivery, is a serious and frequent psychiatric disorder. While numerous screening tools existed for depressive episodes, e.g., the ...Edinburgh Postnatal Depression Scale (EPDS), there are no objective biological measures for predicting PPD. Despite several studies done to identify biomarkers in PPD, there has been limited exploration into cerebrospinal fluid (CSF) which directly interfaces with the brain. Consequently, novel potential biomarkers of CSF are required to predict PPD, so as to target specific preventive interventions.
Seventy-five parturients undergoing caesarean delivery were enrolled for CSF collection at delivery. Of the twenty-eight subjects who didn't meet any exclusion criteria, the number of the healthy parturients whose score of EPDS 6-weeks postpartum (6-wpp) < 5 and PPD patients whose EPDS 6-wpp ≥ 13 was ten respectively. Gas chromatography–mass spectrometry (GC–MS) analysis of CSF was used for metabolomic assessments.
We found that capric acid, dodecanoic acid, arachidic acid and behenic acid in CSF were significantly negatively correlated with PPD symptoms, meanwhile L-tryptophan had an obvious positive correlation. Moreover, these five biomarkers can be used as effective predictive biomarkers for PPD.
The main limitations are the inclusion of only parturients who underwent caesarean sections and a small sample size.
This study innovatively investigated potential predictive biomarkers of PPD before the onset through intrapartum maternal CSF metabolomics, which offered a more objective approach to predict and diagnose PPD, leading to help identify high-risk parturients for early initiation of secondary prevention to reduce global PPD burden.
•This is the first study to investigate the PPD predictive biomarkers via intrapartum maternal CSF metabolomics.•The CSF level of capric acid, dodecanoic acid, arachidic acid, behenic acid, and L-tryptophan correlated with PPD symptoms.•These metabolites can be effective predictive biomarkers for PPD by virtue of their excellent discriminatory performance.
This paper addresses a secure bipartite tracking control problem for a class of nonlinear multi-agents (MASs) with nonsymmetric input constraints. In the presence of adversarial sensor attacks, a ...secure measurement preselector, along with an explicit sufficient condition, and a neural network (NN) secure state observer are introduced for achieving secure state estimation. Then, a secure bipartite tracking control strategy is proposed, where observation predictors are designed to reconstruct prediction errors in such a way as to improve control performance. Furthermore, an auxiliary system is presented to eliminate influence from nonsymmetric input saturations. It is theoretically proved that the proposed control strategy not only guarantees bipartite tracking of the MAS but also preserves the stability of the resulting closed-loop system in spite of senor attacks. Finally, two illustrative examples are presented to verify the effectiveness of the obtained results.
Session-based recommendation (SBR) is to predict the next item, given an anonymous interaction sequence. Recently, many advanced SBR models have shown great recommending performance, but few studies ...note that they suffer from popularity bias seriously: the model tends to recommend popular items and fails to recommend long-tail items. The only few debias works relieve popularity bias indeed. However, they ignore individual’s conformity toward popular items and thus decrease recommending performance on popular items. Besides, conformity is always entangled with individual’s real interest, which hinders extracting one’s comprehensive preference. To tackle the problem, we propose an SBR framework with Disentangling InteRest and Conformity for eliminating popularity bias in SBR. In this framework, two groups of item encoders and session modeling modules are devised to extract interest and conformity, respectively, and a fusion module is designed to combine these two types of preference. Also, a discrepancy loss is utilized to disentangle the representation of interest and conformity. Besides, our devised framework can integrate with several SBR models seamlessly. We conduct extensive experiments on three real-world datasets with four advanced SBR models. The results show that our framework outperforms other state-of-the-art debias methods consistently.
This paper researches the event-triggered forward immersion and invariant (I&I) tracking control problem for a class of strict feedback nonlinear systems. A forward I&I-based control method is ...developed for the tracking problem with an dynamic event-triggered mechanism. Since I&I-based method does not require the introduction of Lyapunov functions in the controller design, the design complexity is greatly reduced. Since the I&I-based method can be used to decouple the design by constructing two manifolds separately, it avoids the need of the traditional backstepping method to combine the Lyapunov function coupled design control law of radial basis function neural networks (RBFNNs). I&I adaptive technique is introduced to improve the weight update in RBFNNs. It can improve the learning performance and convergence speed of neural networks under the event-triggered mechanism. Furthermore, finite-time technique is employed to improve the error convergence time of the event-triggered forward I&I control method. For stability analysis, an event-triggered control system is denoted as a nonlinear impulsive dynamical system, and a Lyapunov theorem is then used to represent the stability of the closed-loop system without Zeno behavior. Finally, the validity of the theoretical results is illustrated by simulation examples and experiments. Note to Practitioners -The motivation of this paper is to present an event-triggered forward I&I tracking control method in finite time for a class strict feedback nonlinear system. The use of I&I technique can reduce the complexity of control method design. To reduce the computational resources, the event-triggered mechanism is introduced in the forward I&I technique. The I&I technique is introduced to construct two manifolds separately and decouple the design of the control law and the RBFNNs weight update law. Moreover, the I&I adaptive technique with the event-triggered mechanism is employed to improve the approximation effect of RBFNNs. Finally, the finite-time technique is introduced to reduce the convergence time of the tracking error under the event-triggered mechanism. This proposed method can be simply and efficiently applied in industrial applications.
In this article, a novel secure fault-tolerant control (FTC) strategy is proposed to deal with the impact of multiple threats such as sparse sensor attacks, system faults, and unknown disturbances on ...T-S fuzzy cyber-physical systems (CPSs). First, under the assumption of 2 s -detectability, a set of robust local unknown input observers is designed. Specifically, each observer can decouple partial disturbances and perform targeted suppression on undecoupling disturbances simultaneously. Next, the residual-based attack detection strategy and secure global estimation fusion mechanism are developed, leading to the estimation of the state and concerned fault with smaller estimation error. Ulteriorly, a secure fault tolerant controller is proposed to ensure that the system can recover satisfactory performance in time subjected to multiple threats. Finally, the proposed secure FTC method is applied to the control scenario of autonomous vehicles in the network environment, which proves the effectiveness of the developed technology.
•The surface potential of the secondary phases was characterized by SKPFM.•The less noble Al2Gd phase improved corrosion resistance of as extruded LA83 alloys.•The secondary cathode (Mg2Sn) shows ...weakly effects on the micro-galvanic corrosion.•Uniform corrosion was formed in the surface of the LA83-Gd and LA83-Sn alloys.•Local serious corrosion was induced by the nobler AlCuMg phase (∼720 mV).
The effects of Gd, Sn and Cu on the corrosion behavior of as extruded Mg–8Li–3Al (LA83) alloy were characterized by scanning Kelvin probe force microscopy, weight loss, hydrogen evolution, and electrochemical measurements. Result revealed that many Al2Gd, Mg2Sn, and AlCuMg particles were discovered in modified alloys, respectively. The combined experimental results of weight loss, hydrogen evolution, and electrochemical analysis indicated that the addition of Gd and Sn improve the corrosion resistance of as extruded LA83 alloy while the addition of Cu weaken its corrosion resistance. The corrosion mechanisms of different elements in as extruded samples were discussed.
PDF
