Modeling and Analysis of Bifurcation in a Delayed Worm Propagation Model

A delayed worm propagation model with birth and death rates is formulated. The stability of the positive equilibrium is studied. Through theoretical analysis, a critical value τ0 of Hopf bifurcation is derived. The worm propagation system is locally asymptotically stable when time delay is less than τ0. However, Hopf bifurcation appears when time delay τ passes the threshold τ0, which means that the worm propagation system is unstable and out of control. Consequently, time delay should be adjusted to be less than τ0 to ensure the stability of the system stable and better prediction of the scale and speed of Internet worm spreading. Finally, numerical and simulation experiments are presented to simulate the system, which fully support our analysis

Policy Regularization with Dataset Constraint for Offline Reinforcement Learning

We consider the problem of learning the best possible policy from a fixed dataset, known as offline Reinforcement Learning (RL). A common taxonomy of existing offline RL works is policy regularization, which typically constrains the learned policy by distribution or support of the behavior policy. However, distribution and support constraints are overly conservative since they both force the policy to choose similar actions as the behavior policy when considering particular states. It will limit the learned policy's performance, especially when the behavior policy is sub-optimal. In this paper, we find that regularizing the policy towards the nearest state-action pair can be more effective and thus propose Policy Regularization with Dataset Constraint (PRDC). When updating the policy in a given state, PRDC searches the entire dataset for the nearest state-action sample and then restricts the policy with the action of this sample. Unlike previous works, PRDC can guide the policy with proper behaviors from the dataset, allowing it to choose actions that do not appear in the dataset along with the given state. It is a softer constraint but still keeps enough conservatism from out-of-distribution actions. Empirical evidence and theoretical analysis show that PRDC can alleviate offline RL's fundamentally challenging value overestimation issue with a bounded performance gap. Moreover, on a set of locomotion and navigation tasks, PRDC achieves state-of-the-art performance compared with existing methods. Code is available at https://github.com/LAMDA-RL/PRDCComment: Accepted to ICML 202

Co 3

Co3O4 nanoparticles were prepared from cobalt nitrate that was accommodated in the pores of a metal-organic framework (MOF) ZIF-8 (Zn(MeIM)2, MeIM = 2-methylimidazole) by using a simple liquid-phase method. Analysis by scanning electron microscopy (SEM) and transmission electron microscopy (TEM) showed that the obtained Co3O4 was composed of separate nanoparticles with a mean size of 30 nm. The obtained Co3O4 nanoparticles exhibited superior electrochemical property. Co3O4 electrode exhibited a maximum specific capacitance of 189.1 F g−1 at the specific current of 0.2 A g−1. Meanwhile, the Co3O4 electrode possessed the high specific capacitance retention ratio at the current density ranging from 0.2 to 1.0 A g−1, thereby indicating that Co3O4 electrode suited high-rate charge/discharge

Gene therapy with tumor-specific promoter mediated suicide gene plus IL-12 gene enhanced tumor inhibition and prolonged host survival in a murine model of Lewis lung carcinoma

<p>Abstract</p> <p>Background</p> <p>Gene therapy is a promising therapeutic approach for cancer. Targeted expression of desired therapeutic proteins within the tumor is the best approach to reduce toxicity and improve survival. This study is to establish a more effective and less toxic gene therapy of cancer.</p> <p>Methods</p> <p>Combined gene therapy strategy with recombinant adenovirus expressing horseradish peroxidase (HRP) mediated by human telomerase reverse transcriptase (hTERT) promoter (AdhTERTHRP) and murine interleukin-12 (mIL-12) under the control of Cytomegalovirus (CMV) promoter (AdCMVmIL-12) was developed and evaluated against Lewis lung carcinoma (LLC) both <it>in vivo </it>and <it>in vitro</it>. The mechanism of action and systemic toxicities were also investigated.</p> <p>Results</p> <p>The combination of AdhTERTHRP/indole-3-acetic acid (IAA) treatment and AdCMVmIL-12 resulted in significant tumor growth inhibition and survival improvement compared with AdhTERTHRP/IAA alone (tumor volume, 427.4 ± 48.7 mm³<it>vs </it>581.9 ± 46.9 mm³, <it>p </it>= 0.005 on day 15; median overall survival (OS), 51 d <it>vs </it>33 d) or AdCMVmIL-12 alone (tumor volume, 362.2 ± 33.8 mm³<it>vs </it>494.4 ± 70.2 mm³, <it>p </it>= 0.046 on day 12; median OS, 51 d <it>vs </it>36 d). The combination treatment stimulated more CD4⁺and CD8⁺T lymphocyte infiltration in tumors, compared with either AdCMVmIL-12 alone (1.3-fold increase for CD4⁺T cells and 1.2-fold increase for CD8⁺T cells, <it>P </it>< 0.01) or AdhTERTHRP alone (2.1-fold increase for CD4⁺T cells and 2.2-fold increase for CD8⁺T cells, <it>P </it>< 0.01). The apoptotic cells in combination group were significantly increased in comparison with AdCMVmIL-12 alone group (2.8-fold increase, <it>P </it>< 0.01) or AdhTERTHRP alone group (1.6-fold increase, <it>P </it>< 0.01). No significant systematic toxicities were observed.</p> <p>Conclusions</p> <p>Combination gene therapy with AdhTERTHRP/IAA and AdCMVmIL-12 could significantly inhibit tumor growth and improve host survival in LLC model, without significant systemic adverse effects.</p

Modeling and Bifurcation Research of a Worm Propagation Dynamical System with Time Delay

Both vaccination and quarantine strategy are adopted to control the Internet worm propagation. By considering the interaction infection between computers and external removable devices, a worm propagation dynamical system with time delay under quarantine strategy is constructed based on anomaly intrusion detection system (IDS). By regarding the time delay caused by time window of anomaly IDS as the bifurcation parameter, local asymptotic stability at the positive equilibrium and local Hopf bifurcation are discussed. Through theoretical analysis, a threshold τ0 is derived. When time delay is less than τ0, the worm propagation is stable and easy to predict; otherwise, Hopf bifurcation occurs so that the system is out of control and the containment strategy does not work effectively. Numerical analysis and discrete-time simulation experiments are given to illustrate the correctness of theoretical analysis

Disentangling Policy from Offline Task Representation Learning via Adversarial Data Augmentation

Offline meta-reinforcement learning (OMRL) proficiently allows an agent to tackle novel tasks while solely relying on a static dataset. For precise and efficient task identification, existing OMRL research suggests learning separate task representations that be incorporated with policy input, thus forming a context-based meta-policy. A major approach to train task representations is to adopt contrastive learning using multi-task offline data. The dataset typically encompasses interactions from various policies (i.e., the behavior policies), thus providing a plethora of contextual information regarding different tasks. Nonetheless, amassing data from a substantial number of policies is not only impractical but also often unattainable in realistic settings. Instead, we resort to a more constrained yet practical scenario, where multi-task data collection occurs with a limited number of policies. We observed that learned task representations from previous OMRL methods tend to correlate spuriously with the behavior policy instead of reflecting the essential characteristics of the task, resulting in unfavorable out-of-distribution generalization. To alleviate this issue, we introduce a novel algorithm to disentangle the impact of behavior policy from task representation learning through a process called adversarial data augmentation. Specifically, the objective of adversarial data augmentation is not merely to generate data analogous to offline data distribution; instead, it aims to create adversarial examples designed to confound learned task representations and lead to incorrect task identification. Our experiments show that learning from such adversarial samples significantly enhances the robustness and effectiveness of the task identification process and realizes satisfactory out-of-distribution generalization

Multiple mesodermal lineage differentiation of Apodemus sylvaticus embryonic stem cells in vitro

<p>Abstract</p> <p>Background</p> <p>Embryonic stem (ES) cells have attracted significant attention from researchers around the world because of their ability to undergo indefinite self-renewal and produce derivatives from the three cell lineages, which has enormous value in research and clinical applications. Until now, many ES cell lines of different mammals have been established and studied. In addition, recently, AS-ES1 cells derived from <it>Apodemus sylvaticus </it>were established and identified by our laboratory as a new mammalian ES cell line. Hence further research, in the application of AS-ES1 cells, is warranted.</p> <p>Results</p> <p>Herein we report the generation of multiple mesodermal AS-ES1 lineages via embryoid body (EB) formation by the hanging drop method and the addition of particular reagents and factors for induction at the stage of EB attachment. The AS-ES1 cells generated separately in vitro included: adipocytes, osteoblasts, chondrocytes and cardiomyocytes. Histochemical staining, immunofluorescent staining and RT-PCR were carried out to confirm the formation of multiple mesodermal lineage cells.</p> <p>Conclusions</p> <p>The appropriate reagents and culture milieu used in mesodermal differentiation of mouse ES cells also guide the differentiation of in vitro AS-ES1 cells into distinct mesoderm-derived cells. This study provides a better understanding of the characteristics of AS-ES1 cells, a new species ES cell line and promotes the use of Apodemus ES cells as a complement to mouse ES cells in future studies.</p

A Stem Cell-Based Tool for Small Molecule Screening in Adipogenesis

Techniques for small molecule screening are widely used in biological mechanism study and drug discovery. Here, we reported a novel adipocyte differentiation assay for small molecule selection, based on human mesenchymal stem cells (hMSCs) transduced with fluorescence reporter gene driven by adipogenic specific promoter - adipocyte Protein 2 (aP2; also namely Fatty Acid Binding Protein 4, FABP4). During normal adipogenic induction as well as adipogenic inhibition by Ly294002, we confirmed that the intensity of green fluorescence protein corresponded well to the expression level of aP2 gene. Furthermore, this variation of green fluorescence protein intensity can be read simply through fluorescence spectrophotometer. By testing another two small molecules in adipogenesis –Troglitazone and CHIR99021, we proved that this is a simple and sensitive method, which could be applied in adipocyte biology, drug discovery and toxicological study in the future