Effects of intratracheal administration of nuclear factor-kappaB decoy oligodeoxynucleotides on long-term cigarette smoke-induced lung inflammation and pathology in mice

To determine if nuclear factor-κB (NF-κB) activation may be a key factor in lung inflammation and respiratory dysfunction, we investigated whether NF-κB can be blocked by intratracheal administration of NF-κB decoy oligodeoxynucleotides (ODNs), and whether decoy ODN-mediated NF-κB inhibition can prevent smoke-induced lung inflammation, respiratory dysfunction, and improve pathological alteration in the small airways and lung parenchyma in the long-term smoke-induced mouse model system. We also detected changes in transcriptional factors. In vivo, the transfection efficiency of NF-κB decoy ODNs to alveolar macrophages in BALF was measured by fluorescein isothiocyanate (FITC)-labeled NF-κB decoy ODNs and flow cytometry post intratracheal ODN administration. Pulmonary function was measured by pressure sensors, and pathological changes were assessed using histology and the pathological Mias software. NF-κB and activator protein 1(AP-1) activity was detected by the electrophoretic motility shift assay (EMSA). Mouse cytokine and chemokine pulmonary expression profiles were investigated by enzyme-linked immunosorbent assay (ELISA) in bronchoalveolar lavage fluid (BALF) and lung tissue homogenates, respectively, after repeated exposure to cigarette smoke. After 24 h, the percentage of transfected alveolar macrophages was 30.00 ± 3.30%. Analysis of respiratory function indicated that transfection of NF-κB decoy ODNs significantly impacted peak expiratory flow (PEF), and bronchoalveolar lavage cytology displayed evidence of decreased macrophage infiltration in airways compared to normal saline-treated or scramble NF-κB decoy ODNs smoke exposed mice. NF-κB decoy ODNs inhibited significantly level of macrophage inflammatory protein (MIP) 1α and monocyte chemoattractant protein 1(MCP-1) in lung homogenates compared to normal saline-treated smoke exposed mice. In contrast, these NF-κB decoy ODNs-treated mice showed significant increase in the level of tumor necrosis factor-α(TNF-α) and pro-MMP-9(pro-matrix metalloproteinase-9) in mice BALF. Further measurement revealed administration of NF-κB decoy ODNs did not prevent pathological changes. These findings indicate that NF-κB activation play an important role on the recruitment of macrophages and pulmonary dysfunction in smoke-induced chronic lung inflammation, and with the exception of NF-κB pathway, there might be complex mechanism governing molecular dynamics of pro-inflammatory cytokines expression and structural changes in small airways and pulmonary parenchyma in vivo

Thermodynamic analysis of a dual-loop organic Rankine cycle (ORC) for waste heat recovery of a petrol engine

Huge amounts of low-grade heat energy are discharged to the environment by vehicular engines. Considering the large number of vehicles in the world, such waste energy has a great impact on our environment globally. The Organic Rankine Cycle (ORC), which uses an organic fluid with a low boiling point as the working medium, is considered to be the most promising technology to recover energy from low-grade waste heat. In this study, a dual-loop ORC is presented to simultaneously recover energy from both the exhaust gases and the coolant of a petrol engine. A high-temperature (HT) ORC loop is used to recover heat from the exhaust gases, while a low-temperature (LT) ORC loop is used to recover heat from the coolant and the condensation heat of the HT loop. Figure 1 shows the schematic of the dual-loop ORC. Differing from previous research, two more environmentally friendly working fluids are used, and the corresponding optimisation is conducted. First, the system structure and operating principle are described. Then, a mathematical model of the designed dual-loop ORC is established. Next, the performance of the dual-loop cycle is analysed over the entire engine operating region. Furthermore, the states of each point along the cycle and the heat load of each component are compared with the results of previous research. The results show that the dual-loop ORC can effectively recover the waste heat from the petrol engine, and that the effective thermal efficiency can be improved by about 20 ~ 24%, 14~20%, and 30% in the high-speed, medium-speed, and low-speed operation regions, respectively. The designed dual-loop ORC can achieve a higher system efficiency than previous ORCs of this structure. Therefore, it is a good choice for waste heat recovery from vehicle engines

On the Evaluation of Generative Models in Distributed Learning Tasks

The evaluation of deep generative models including generative adversarial networks (GANs) and diffusion models has been extensively studied in the literature. While the existing evaluation methods mainly target a centralized learning problem with training data stored by a single client, many applications of generative models concern distributed learning settings, e.g. the federated learning scenario, where training data are collected by and distributed among several clients. In this paper, we study the evaluation of generative models in distributed learning tasks with heterogeneous data distributions. First, we focus on the Fr\'echet inception distance (FID) and consider the following FID-based aggregate scores over the clients: 1) FID-avg as the mean of clients' individual FID scores, 2) FID-all as the FID distance of the trained model to the collective dataset containing all clients' data. We prove that the model rankings according to the FID-all and FID-avg scores could be inconsistent, which can lead to different optimal generative models according to the two aggregate scores. Next, we consider the kernel inception distance (KID) and similarly define the KID-avg and KID-all aggregations. Unlike the FID case, we prove that KID-all and KID-avg result in the same rankings of generative models. We perform several numerical experiments on standard image datasets and training schemes to support our theoretical findings on the evaluation of generative models in distributed learning problems.Comment: 17 pages, 10 figure

WW-representations of two-matrix models with infinite set of variables

The Hermitian, complex and fermionic two-matrix models with infinite set of variables are constructed. We show that these two-matrix models can be realized by the $W$-representations. In terms of the $W$-representations, we derive the compact expressions of correlators for these two-matrix models.Comment: 12 page

Concept Tree Based Clustering Visualization with Shaded Similarity Matrices

One of the problems with existing clustering methods is that the interpretation of clusters may be difficult. Two different approaches have been used to solve this problem: conceptual clustering in machine learning and clustering visualization in statistics and graphics. The purpose of this paper is to investigate the benefits of combining clustering visualization and conceptual clustering to obtain better cluster interpretations. In our research we have combined concept trees for conceptual clustering with shaded similarity matrices for visualization. Experimentation shows that the two interpretation approaches can complement each other to help us understand data better

Blockchain Sharding and Incentive Mechanism for 6G Dependable Intelligence

The sixth generation(6G) wireless communication network will become the base of endogenous intelligence,ubiquitous connectivity,and full-scene interconnection.It is an important basis to realize dependable intelligence in the future.Blockchain is considered as the key decentralized-enabled technology to improve the performance of 6G networks.In the future,the consensus nodes of the blockchain will be composed of massive edge devices and connected through wireless networks.However,motivating self-interest edge devices to participate in the consensus process still faces the challenges of information asymmetry,resource constraints and heterogeneous wireless communication environment.To solve these challenges,a blockchain sharding framework and an incentive mechanism for trusted and dependable intelligence in 6G are proposed.Firstly,an incentive mechanism is presented based on contract theory,which aims to maximize the benefits and reliability of the blockchain sharding.By analyzing the practical byzantine fault tolerance (PBFT) based intrashard consensus mechanism,this paper design energy consumption model for auditing and transmitting the blocks in wireless networks.Secondly,in order to improve the system reliability,it proposes a reputation mechanism based on subjective logic.Finally,a set of optimal contracts under complete information and asymmetric information scnearios are abtained,which could optimize the block revenue for blockchain service requester,while ensuring some desired economic properties,i.e.,budget feasibility,individual rationality and incentive compatibility.Simulation results show that the proposed contract-based incentive mechanism can motivate edge devices to participate in the blockchain consensus process and maintain the operation of blockchain from the perspective of economics more efficiently

Interim estimates of divergence date and vaccine strain match of human influenza A(H3N2) virus from systematic influenza surveillance (2010–2015) in Hangzhou, southeast of China

SummaryObjectivesIn the post-pandemic period 2010–2015, seasonal influenza A(H3N2) virus predominated in Hangzhou, southeast of China, with an increased activity and semi-annual seasons. This study utilized HA virus gene segment sequences to analyze the divergence date and vaccine strain match of human influenza A(H3N2) virus from systematic influenza surveillance in Hangzhou.MethodsVirological and serological analyses of 124 representative A(H3N2) viruses from prospective studies of systematic surveillance samples were conducted to quantify the genetic and antigenic characteristics and their vaccine strain match.ResultsBayesian phylogenetic inference showed that two separate subgroups 3C.3 and 3C.2 probably diverged from group 3C in early 2012 and then evolved into groups 3C.3a and 3C.2a, respectively, in the 2014/15 influenza season. Furthermore, high amino acid substitution rates of the HA1 subunit were found in A(H3N2) group 3C.2a variants, indicating that increased antigenic drift of A(H3N2) group 3C.2a virus is associated with a vaccine mismatch to the 2015/16 vaccine reference strain Switzerland/9715293/2013 (group 3C.3a).ConclusionsA portion of the group 3C.2a isolates are not covered by the current A(H3N2) vaccine strain. These findings offer insights into the emergence of group 3C.2a variants with epidemic potential in the imminent influenza seasons

Trojan Horse nanotheranostics with dual transformability and multifunctionality for highly effective cancer treatment.

Nanotheranostics with integrated diagnostic and therapeutic functions show exciting potentials towards precision nanomedicine. However, targeted delivery of nanotheranostics is hindered by several biological barriers. Here, we report the development of a dual size/charge- transformable, Trojan-Horse nanoparticle (pPhD NP) for delivery of ultra-small, full active pharmaceutical ingredients (API) nanotheranostics with integrated dual-modal imaging and trimodal therapeutic functions. pPhD NPs exhibit ideal size and charge for drug transportation. In tumour microenvironment, pPhD NPs responsively transform to full API nanotheranostics with ultra-small size and higher surface charge, which dramatically facilitate the tumour penetration and cell internalisation. pPhD NPs enable visualisation of biodistribution by near-infrared fluorescence imaging, tumour accumulation and therapeutic effect by magnetic resonance imaging. Moreover, the synergistic photothermal-, photodynamic- and chemo-therapies achieve a 100% complete cure rate on both subcutaneous and orthotopic oral cancer models. This nanoplatform with powerful delivery efficiency and versatile theranostic functions shows enormous potentials to improve cancer treatment