In vivo therapeutic efficacy of frog skin-derived peptides against Pseudomonas aeruginosa-induced pulmonary infection

Pseudomonas aeruginosa is an opportunistic and frequently drug-resistant pulmonary pathogen especially in cystic fibrosis sufferers. Recently, the frog skin-derived antimicrobial peptide (AMP) Esc(1-21) and its diastereomer Esc(1-21)-1c were found to possess potent in vitro antipseudomonal activity. Here, they were first shown to preserve the barrier integrity of airway epithelial cells better than the human AMP LL-37. Furthermore, Esc(1-21)-1c was more efficacious than Esc(1-21) and LL-37 in protecting host from pulmonary bacterial infection after a single intra-tracheal instillation at a very low dosage of 0.1 mg/kg. The protection was evidenced by 2-log reduction of lung bacterial burden and was accompanied by less leukocytes recruitment and attenuated inflammatory response. In addition, the diastereomer was more efficient in reducing the systemic dissemination of bacterial cells. Importantly, in contrast to what reported for other AMPs, the peptide was administered at 2 hours after bacterial challenge to better reflect the real life infectious conditions. To the best of our knowledge, this is also the first study investigating the effect of AMPs on airway-epithelia associated genes upon administration to infected lungs. Overall, our data highly support advanced preclinical studies for the development of Esc(1-21)-1c as an efficacious therapeutic alternative against pulmonary P. aeruginosa infection

Stability of Gorenstein flat categories with respect to a semidualizing module

In this paper, we first introduce $\mathcal {W}_F$-Gorenstein modules to establish the following Foxby equivalence: \xymatrix@C=80pt{\mathcal {G}(\mathcal {F})\cap \mathcal {A}_C(R) \ar@[r]^{C\otimes_R-} & \mathcal {G}(\mathcal {W}_F) \ar@[l]^{\textrm{Hom}_R(C,-)}} where $\mathcal {G}(\mathcal {F})$, $\mathcal {A}_C(R)$ and $\mathcal {G}(\mathcal {W}_F)$ denote the class of Gorenstein flat modules, the Auslander class and the class of $\mathcal {W}_F$-Gorenstein modules respectively. Then, we investigate two-degree $\mathcal {W}_F$-Gorenstein modules. An $R$-module $M$ is said to be two-degree $\mathcal {W}_F$-Gorenstein if there exists an exact sequence \mathbb{G}_\bullet=\indent ...\longrightarrow G_1\longrightarrow G_0\longrightarrow G^0\longrightarrow G^1\longrightarrow... in $\mathcal {G}(\mathcal {W}_F)$ such that $M \cong$ \im(G_0\rightarrow G^0) and that $\mathbb{G}_\bullet$ is Hom$_R(\mathcal {G}(\mathcal {W}_F),-)$ and $\mathcal {G}(\mathcal {W}_F)^+\otimes_R-$ exact. We show that two notions of the two-degree $\mathcal {W}_F$-Gorenstein and the $\mathcal {W}_F$-Gorenstein modules coincide when R is a commutative GF-closed ring.Comment: 18 page

Modeling and Analysis of MPTCP Proxy-based LTE-WLAN Path Aggregation

Long Term Evolution (LTE)-Wireless Local Area Network (WLAN) Path Aggregation (LWPA) based on Multi-path Transmission Control Protocol (MPTCP) has been under standardization procedure as a promising and cost-efficient solution to boost Downlink (DL) data rate and handle the rapidly increasing data traffic. This paper aims at providing tractable analysis for the DL performance evaluation of large-scale LWPA networks with the help of tools from stochastic geometry. We consider a simple yet practical model to determine under which conditions a native WLAN Access Point (AP) will work under LWPA mode to help increasing the received data rate. Using stochastic spatial models for the distribution of WLAN APs and LTE Base Stations (BSs), we analyze the density of active LWPA-mode WiFi APs in the considered network model, which further leads to closed-form expressions on the DL data rate and area spectral efficiency (ASE) improvement. Our numerical results illustrate the impact of different network parameters on the performance of LWPA networks, which can be useful for further performance optimization.Comment: IEEE GLOBECOM 201

A Framework of Hybrid Force/Motion Skills Learning for Robots

Human factors and human-centred design philosophy are highly desired in today’s robotics applications such as human-robot interaction (HRI). Several studies showed that endowing robots of human-like interaction skills can not only make them more likeable but also improve their performance. In particular, skill transfer by imitation learning can increase usability and acceptability of robots by the users without computer programming skills. In fact, besides positional information, muscle stiffness of the human arm, contact force with the environment also play important roles in understanding and generating human-like manipulation behaviours for robots, e.g., in physical HRI and tele-operation. To this end, we present a novel robot learning framework based on Dynamic Movement Primitives (DMPs), taking into consideration both the positional and the contact force profiles for human-robot skills transferring. Distinguished from the conventional method involving only the motion information, the proposed framework combines two sets of DMPs, which are built to model the motion trajectory and the force variation of the robot manipulator, respectively. Thus, a hybrid force/motion control approach is taken to ensure the accurate tracking and reproduction of the desired positional and force motor skills. Meanwhile, in order to simplify the control system, a momentum-based force observer is applied to estimate the contact force instead of employing force sensors. To deploy the learned motion-force robot manipulation skills to a broader variety of tasks, the generalization of these DMP models in actual situations is also considered. Comparative experiments have been conducted using a Baxter Robot to verify the effectiveness of the proposed learning framework on real-world scenarios like cleaning a table