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Acute lung injury (ALI) is characterized by a series of inflammatory reactions and serves as the main cause of mortality in intensive care unit patients. Although great progress has been made in understanding the pathophysiology of ALI, there are no effective treatments in clinic. Recently, we have synthesized a selenium-containing compound, which possesses obvious anti-inflammatory activity. The aim of the present study is to evaluate the protective effects of the selenium-containing compound 34# in LPS-induced ALI in mice as well as its underlying mechanism. Compound 34# was found to inhibit LPS-induced macrophage inflammatory cytokine release. These effects were observed to be produced via suppression of the MAPK/AP-1 pathway. Compound 34# was also noted to attenuate the LPS-induced lung inflammation in mice with ALI. The corresponding results suggested that compound 34# possesses remarkable protective effects on LPS-induced ALI. Furthermore, the MAPK/AP-1 pathway may prove to be the underlying mechanism. Accordingly, compound 34# may serve as a potential candidate for the prevention of ALI.

Background Human-machine dialog generation is an essential topic of research in the field of natural language processing. Generating high-quality, diverse, fluent, and emotional conversation is a challenging task. Based on continuing advancements in artificial intelligence and deep learning, new methods have come to the forefront in recent times. In particular, the end-to-end neural network model provides an extensible conversation generation framework that has the potential to enable machines to understand semantics and automatically generate responses. However, neural network models come with their own set of questions and challenges. The basic conversational model framework tends to produce universal, meaningless, and relatively "safe" answers. Methods Based on generative adversarial networks (GANs), a new emotional dialog generation framework called EMC-GAN is proposed in this study to address the task of emotional dialog generation. The proposed model comprises a generative and three discriminative models. The generator is based on the basic sequence-to-sequence (Seq2Seq) dialog generation model, and the aggregate discriminative model for the overall framework consists of a basic discriminative model, an emotion discriminative model, and a fluency discriminative model. The basic discriminative model distinguishes generated fake sentences from real sentences in the training corpus. The emotion discriminative model evaluates whether the emotion conveyed via the generated dialog agrees with a pre-specified emotion, and directs the generative model to generate dialogs that correspond to the category of the pre-specified emotion. Finally, the fluency discriminative model assigns a score to the fluency of the generated dialog and guides the generator to produce more fluent sentences. Results Based on the experimental results, this study confirms the superiority of the proposed model over similar existing models with respect to emotional accuracy, fluency, and consistency. Conclusions The proposed EMC-GAN model is capable of generating consistent, smooth, and fluent dialog that conveys pre-specified emotions, and exhibits better performance with respect to emotional accuracy, consistency, and fluency compared to its competitors.

The printable HTM-free (HTM = hole transport materials) mesoporous carbon-based perovskite solar cells (C-PSCs) are one of the most promising technologies because of their simple production process, lost-cost and excellent stability. In this study, a novel and secure method is used to modify the original graphite, and the obtained chlorinated graphites (C–Clx, x = 0.2, 0.4, and 0.6) are applied to counter electrodes (CEs) in the printing of C-PSCs based on the TiO2/ZrO2/(5-AVA)x(MA)1−xPbI3/C–Clx structure. The experimental results show that C–Clx (x = 0.2, 0.4, and 0.6) can effectively improve the efficiency of C-PSCs. The mesoporous PSCs based on C–Cl0.4 CE have achieved an average efficiency of 11.81%, which is about 1.46-times of the pristine device with carbon CE, and the short-circuit current density (Jsc), fill factor (FF) and open-circuit voltage (Voc) increased by 15.8%, 11.7% and 11.2%, respectively. Simultaneously, a 30 day aging test showed that PSCs based on (5-AVA)x(MA)1−xPbI3 and C–Clx CEs showed excellent stability under ambient air conditions.

Tomatoes contain active phytochemicals, including phytoene, phytofluene, beta-caroten, flavonoids, carotenoids, and lycopene but theirs effects on Weight, Body Mass Index, Blood Pressure and Inflammatory Factors is controversial. The aim of this systematic review and meta-analyze is reported the effects of Tomato consumption on Weight, Body Mass Index, Blood Pressure and Inflammatory Factors such as CRP and IL-6 in Randomized Controlled Trials. Literature search was conducted by a systematic search in PubMed/MEDLINE, Scopus, and Web of sciences (WOB) up to August 2019. There was no language or release time data restrictions. Two researcher conducted data extraction, independently. Dersimonian-laird random effect models were conducted to combining result of included studies. PRISMA guidelines (Preferred Items for Reporting of Systematic Reviews and Meta-Analyses) used for this meta-analysis. Nine studies met inclusion criteria for this meta-analysis. Combined results show not significant change in weight of participants (WMD: −0.03 kg, 95%CI: −0.59, 0.53, Pheterogeneity = 0.98) and BMI by tomato intervention (WMD: 0.07 kg/m2, 95%CI: −0.14, 0.29, Pheterogeneity = 0.99). Furthermore, blood pressure change was not significant too but pooled results show an significant reduction in IL-6 levels following intervention by tomato compared to control group (WMD: −0.12 pg/ml, 95% CI: −0.23, −0.02, Pheterogeneity = 0.001). Although, the pooled results from effect of tomato on CRP were not significant (WMD: 0.98 mg/l, 95%CI: −0.23, 219, Pheterogeneity = 0.09) but time-response analysis showed significant time dependent relation between tomato intervention and CRP levels. The results demonstrated a significant reduction in IL-6 by tomato consumption. Tomato reduce CRP levels in short period too. Keywords: Tomato, Weight, Blood pressure, IL-6, CRP

In order to explore the application of terahertz (THz) vortex beams in 3D dielectric-coated target detection and imaging, scattering characteristics of a THz Bessel vortex beam by 3D dielectric-coated conducting targets are investigated in terms of the combination of planewave angular spectrum expansion and a physics optics method in this paper. The accuracy of the proposed method is verified by comparing with the results of FEKO software. The scattering characteristics of a THz Bessel vortex beam by several typical 3D dielectric-coated targets are analyzed in detail. The effects of the beam parameters (topological charge, half-cone angle, incident angle and frequency) are discussed. The results show that with an increase of topological charge, the magnitude of the radar cross section (RCS) decreases, and the maximum value moves away from the incident direction gradually; the distribution of the RCS does not keep symmetry as the incident angle increases, and the corresponding orbital angular momentum state distribution of the far-scattered field is distorted remarkably.

As one of the effective methods to remove heavy metals, graphene (G) composite adsorption technology is irreplaceable in water pollution control and water purification, but its adsorption mechanism is not clear. Materials Studio (MS) software was used to simulate the molecular dynamics of the interaction between graphene oxide/graphene (GO/G) composite and alkali metal ion (M

BACKGROUND: Roxithromycin (RXM), a macrolide antibiotic, exhibits anti-asthmatic effects, but its specific mechanism of action remains elusive. We evaluated the effects of RXM on airway inflammation, the expression of calprotectin, and the activity of the receptor of advanced glycation end products (RAGE) to determine whether RXM alleviates inflammation by regulating RAGE activation, and thereby calprotectin expression, in neutrophilic asthma. METHODS: Male Brown Norway rats were sensitized with ovalbumin (OVA) and Freund’s complete adjuvant (FCA) mixture, followed by OVA challenge to induce neutrophilic asthma. RXM (30 mg/kg) or FPS-ZM1 (RAGE inhibitor, 1.5 mg/kg) was administered 30 min prior to each challenge. The infiltration of airway inflammatory cells and cytokines, as well as the expression of calprotectin and RAGE, was assessed. RESULTS: The expression of airway inflammatory cells and cytokines was found to be significantly elevated in OVA + FCA-induced rats. Increased expression of both calprotectin and RAGE was also detected in OVA + FCA-induced asthma [bronchoalveolar lavage fluid (BALF) calprotectin: 15.07±1.79 vs. 3.86±0.69 ng/mL; serum calprotectin: 20.47±1.64 vs. 9.29±1.31 ng/mL; lung tissue homogenates calprotectin: 28.82±1.01 vs. 12.02±1.38 ng/mg; BALF RAGE: 762.93±36.47 vs. 294.25±45.92 ng/mL; serum RAGE: 906.43±58.95 vs. 505.60±30.16 ng/mL; lung tissue homogenates RAGE: 1,585.24±177.59 vs. 461.53±63.40 ng/mg; all P

Acute lung injury (ALI) characterized by a series of inflammatory reactions and served as the main cause of mortality in intensive care unit patients. Although great progress have been made in understanding the pathophysiology of ALI, there are no effective treatment in clinic. Recently, we have synthesized a selenium-containing compounds, which possess obvious anti-inflammatory activity. The aim of the present study is to evaluate the protective effects of the selenium-containing compound 34# in LPS-induced ALI in mice as well as its underlying mechanism. Compound 34# was found to inhibit LPS-induced macrophage inflammatory cytokine release. These effects were observed to be produced via suppression of the MAPKs/AP-1 pathway. Compound 34# was also noted to attenuate the LPS-induced lung inflammation in mice with ALI. The corresponding results suggested that compound 34# possesses remarkable protective effects on LPS-induced ALI. Furthermore, the MAPKs/AP-1 pathway may prove to be its the underlying mechanism. Accordingly, compound 34# may serve as a potential candidate for the prevention of ALI.

Objective. Coronavirus disease 2019 (COVID-19), caused by the novel coronavirus SARS-CoV-2, was first identified in December 2019 in Wuhan, China, and has since spread globally, resulting in an ongoing pandemic. However, the study of asymptomatic patients is still rare, and the understanding of its potential transmission risk is still insufficient. In this study, epidemiological investigations were conducted in the Zhejiang province to understand the epidemiology and clinical characteristics of asymptomatic patients with COVID-19. Methods. This retrospective study was carried out on 22 asymptomatic patients and 234 symptomatic patients with COVID-19 who were hospitalized in Zhejiang Duodi Hospital from January 21 to March 16, 2020. The characteristics of epidemiology, demography, clinical manifestations, and laboratory data of mild patients were compared and analyzed. Results. The median age was 28 years in asymptomatic patients and 48 years in symptomatic patients. The proportion who were female was 77.3% in asymptomatic patients and 36.3% in symptomatic patients (p<0.001). The proportion of patients with coexisting diseases was 4.5% in asymptomatic patients and 38.0% in symptomatic patients (p=0.002). The proportion of patients with increased CRP was 13.6% in the asymptomatic group and 61.1% in the symptomatic group (p<0.001). The proportion of patients received antiviral therapy was 45.5% in the asymptomatic group and 97.9% in the symptomatic group (p<0.001). The proportion of patients received oxygen therapy was 22.7% in the asymptomatic group and 99.1% in symptomatic patients (p<0.001). By March 16, 2020, all patients were discharged from the hospital, and no symptoms had appeared in the asymptomatic patients during hospitalization. The median course of infection to discharge was 21.5 days in asymptomatic patients and 22 days in symptomatic patients. Conclusions. Asymptomatic patients are also infectious; relying only on clinical symptoms, blood cell tests, and radiology examination will lead to misdiagnosis of most patients, leading to the spread of the virus. Investigation of medical history is the best strategy for screening asymptomatic patients, especially young people, women, and people without coexisting disease, who are more likely to be asymptomatic when infected. Although the prognosis is good, isolation is critical for asymptomatic patients, and it is important not to end isolation early before a nucleic acid test turns negative.

Transparent conductive electrodes fabricated with random networks of silver nanowires (AgNWs) have been widely studied in recent years. However, the weak adhesion and chemical stability limit their further applications in perovskite solar cells. In this study, AgNWs@poly(3,4-ethylenedioxythiophene) (PEDOT)was first used as the transparent counter electrode to enhance the adhesion among AgNWs and avoid the contacts between the AgNWs and the perovskite crystals in the bifacial irradiated triple-mesoporous perovskite solar cells (PSCs). A series of XRD tests were took out and certified that the high stability of the composed films containing AgNWs@PEDOT and the perovskite crystals. A 40 days’ photocurrent-voltage test of the triple-mesoporous PSCs also proved the excellent stability and the champion power conversion efficiencies of 3.8% and 2.5% achieved from the front and the rear illumination respectively.

In recent years, artificial intelligence has made a significant breakthrough and progress in the field of humanmachine conversation. However, how to generate high-quality, emotional and subhuman conversation still a troublesome work. The key factor of man-machine dialogue is whether the chatbot can give a good response in content and emotional level. How to ensure that the robot understands the user’s emotions, and consider the user’s emotions then give a satisfactory response. In this paper, we add the emotional tags to the post and response from the dataset respectively. The emotional tags, as the emotional tags of post and response, represent the emotions expressed by this sentence. The purpose of our emotional tags is to make the chatbot understood the emotion of the input sequence more directly so that it has a recognition of the emotional dimension. In this paper, we apply the mechanism of GAN network on our conversation model. For the generator: We make full use of Encoder-Decoder structure form a seq2seq model, which is used to generate a sentence’s response. For the discriminator: distinguish between the human-generated dialogues and the machine-generated ones.The outputs from the discriminator are used as rewards for the generative model, pushing the system to generate dialogues that mostly resemble human dialogues. We cast our task as an RL(Reinforcement Learning) problem, using a policy gradient method to reward more subhuman conversational sequences, and in addition we have added an emotion tags to represent the response we want to get, which we will use as a rewarding part of it, so that the emotions of real responses can be closer to the emotions we specify. Our experiment shows that through the introduction of emotional intelligence, our model can generate responses appropriate not only in content but also in emotion, which can be used to control and adjust users emotion. Compared with our previous work, we get a better performance on the same data set, and we get less ’’safe’’ response than before, but there will be a certain degree of existence.

As one of the public service units, TV stations tried to make market-oriented reforms by developing brand TV programs to compete with others. The principal focus of our study was to find out the symbolic brand effect of self-congruity and brand personality on audience attitudes, brand loyalty, audience satisfaction, and program preference for TV programs in China. Results indicated that (a) the congruity of audience's self-concept and typical audience image had a positive influence on audience watching mentality; (b) a positive brand personality could result in favorable program evaluation; (c) preference for the host played a moderating role on brand personality effects.

Liquid-phase microextraction (LPME) coupled with gas chromatography-flame ionization detection is applied to the analysis of phenolic compounds (phenol, o-cresol, m-cresol, 2,4-dimethylphenol, 2,3- dimethylphenol, and 3,4-dimethylphenol) in water samples. Experimental parameters affecting the extraction efficiency (including extraction solvent and drop volume, stirring rate, extraction time, temperature, salt concentration, and pH) are investigated and optimized. The developed protocol yields a good linear calibration curve from 5 or 20 to 10000 microg/L for the target analytes. The limits of detection are in the range of 0.94 to 1.97 microg/L, and the relative standard deviation is below 9.37%. The established method is applied to determine the phenolic pollutants in real wastewater samples from a coking plant. The recoveries of the phenolic compounds studied are from 92% to 102%, suggesting the feasibility of the LPME method for the determination of the phenolic compounds in wastewater.

The determination of four volatile halocarbons (CHCl3, CCl4, C2HCl3 and C2Cl4) in water by headspace liquid-phase microextraction (HS-LPME) with gas chromatography using a micro electron capture detector (GC-μECD) is described. The effects of the type and volume of the extraction solvent, headspace volume, stirring rate, extraction temperature and time and ionic strength on the extraction performance are investigated and optimized. The developed protocol yields a linear calibration curve in the concentration range from 0.05 to 50 µg L−1 for the target analytes; the detection limits ranged from 0.003 to 0.146 µg L−1 and the relative standard deviation (R.S.D.) values below 8.45%. The results demonstrate that HS-LPME followed with GC-μECD is a simple and reliable technique for the determination of volatile halocarbons in water samples.

A novel method for the determination of phenolic compounds (phenol, o-cresol, m-cresol, 2,4-dimethylphenol, 2,3-dimethylphenol and 3,4-dimethylphenol) in water samples was developed by combined continuous-flow liquid-phase microextraction (CFME) with gas chromatography-flame ionization detection (GC-FID). The effects of extraction solvent type and volume, flow rate of sample solution, extraction time, salt concentration and pH on the extraction performance were investigated and optimized. The calibration curves and analyses of real samples were obtained under the following conditions: 3.0 µL n-Amyl acetate, 1.5 mL min−1 flowing rate, 15 min extracting time and pH 1.5. The developed protocol was found to yield a linear calibration curve in the concentration ranged from 10 to 10000 µg L−1 for the target analytes, and the limits of detection ranged from 2.32 to 2.95 µg L−1. Good repeatabilities of extraction were obtained, with relative standard deviation (R.S.D.) values below 6.89%. The results demonstrated that the CFME followed with GC-FID is a simple and reliable technique for the determination of phenolic compounds in water samples.

This work reports the effectiveness of a simple and rapid method for wettability improvement on aluminum surfaces – boiling water treatment. The wetting behavior and drainage performance on a series of aluminum surface samples after different lengths of boiling water treatment (3 minutes, 5 minutes, and 10 minutes) are examined. The data of contact angles for different sizes of droplets placed on these samples are reported with detailed surface roughness information from a 3D optical surface profiler. The contact angles are observed to decrease from the original value of 94.5° to approximately 10° after 10 minutes of treatment, and the reduction is correlated with the treatment length. Scanning electron microscopy (SEM) images and 3D optical profiler images suggest that the boiling water treatment facilitates oxidation of the sample surface and greatly alters the micro-structures on the surface, which are believed to be the two main reasons to change the droplet wetting behavior. The contact angle measurements are conducted again two days after the treatment and the hydrophilicity is retained.Copyright © 2013 by ASME

The creation of hydrophobic and superhydrophobic surfaces has attracted tremendous attention in the past decade. Such surfaces provide unique and highly useful properties that are suitable for a wide range of applications. In this paper, we report our findings on the dimensional effects of SU-8 and polydimethylsiloxane (PDMS) pillar arrays on surface hydrophobicity. Pillar arrays with various dimensions are designed and fabricated on SU-8 and PDMS surfaces using optical lithography and soft lithography. The water droplet contact angles on these features are obtained with a goniometer to demonstrate how the related dimensions, including the diameter of each pillar and the distance between two pillars, affect the surface hydrophobicity. Theoretical analyses are also carried out to estimate the contact angles of water droplets based on Cassie and Wenzel models. The experimental and analytical results are presented and compared in this paper. The results demonstrate that the dimensional change of a pillar array has a direct impact on its surface hydrophobicity. The highest contact angle can only be achieved using optimum designs. Furthermore, there are clear differences in our measurement results between the SU-8 and PDMS pillar arrays. We believe that these differences come from the inherent hydrophobicity difference of the two polymers. Without further coatings or treatment steps, SU-8 pillar arrays can achieve contact angles up to 140° while the PDMS structures can achieve higher contact angles up to 170°. Last, the PDMS pillar arrays demonstrate a transition phase from the Cassie state to the Wenzel state during the wetting experiments.Copyright © 2013 by ASME

This paper illustrates the capacity of a pheromone based self-routing control model for intelligent products to autonomously decide efficient processing operations and routing paths in dynamic manufacturing systems undergoing perturbations. This method is inspired by the behavior of foraging ants that leave a pheromone trail on their way to the food. Following ants use the pheromone trail with the highest concentration of pheromone to find the shortest path to the food. Self-routing of intelligent products in dynamic manufacturing systems imitates this behavior in a way that whenever a product leaves a manufacturing resource, i.e. machine, the product leaves information about the performance at the respective resource. The following products use the data from the past to render the routing decision. The discrete event simulations are analyzed by comparing statistics on throughput time data resulting from the system’s behavior in dynamic order arrival and machine breakdown situations.