FOXA2 attenuates lipopolysaccharide­induced pneumonia by inhibiting the inflammatory response, oxidative stress and apoptosis through blocking of p38/STAT3 signaling.

Pneumonia is a severe inflammatory disease of the lung. Forkhead box protein A2 (FOXA2) has been demonstrated to serve an important regulatory role in various pulmonary diseases; however, the role of FOXA2 in pneumonia remains to be elucidated. The present study aimed to explore the functional effects and regulatory mechanism of FOXA2 in pneumonia. An in vitro pneumonia model was induced using lipopolysaccharide (LPS) in WI-38 cells. The mRNA and protein expression levels of FOXA2 were determined by reverse transcription-quantitative PCR and western blotting, respectively. Cell viability was assessed using a Cell Counting Kit-8 assay. Inflammatory cytokines were evaluated using ELISA kits and oxidative stress markers were assessed using a malondialdehyde assay kit, superoxide dismutase assay kit and CATalase assay kit. Cell apoptosis was evaluated using flow cytometry and the caspase3 activity was determined. Western blotting was performed to examine the protein expression levels of endoplasmic reticulum stress (ERS)-associated factors. For a rescue assay, a p38 MAPK activator, U46619, was used to investigate the regulatory mechanism of FOXA2 involving p38/STAT3 signaling. FOXA2 was downregulated in LPS-induced WI-38 cells. FOXA2 overexpression alleviated LPS-induced inflammation, oxidative stress, apoptosis and ERS in WI-38 cells. Furthermore, the inhibitory effects of FOXA2 on inflammation, oxidative stress and apoptosis, as well as ERS in LPS-induced WI-38 cells were partly weakened by additional treatment with U46619. In conclusion, FOXA2 served a protective role against LPS-induced pneumonia by regulating p38/STAT3 signaling, providing a novel idea for the development of targeted therapeutic strategies for pneumonia.

The Research of Maneuverability Modeling and Environmental Monitoring Based on a Robotic Dolphin.

In this study, the C-turning, pitching, and flapping propulsion of a robotic dolphin during locomotion were explored. Considering the swimming action required of a three-dimensional (3D) robotic dolphin in the ocean, we propose a maneuverability model that can be applied to the flapping motion to provide precise and stable movements and function as the driving role in locomotion. Additionally, an added tail joint allows for the turning movement with efficient parameters obtained by a fluid-structure coupling method. To obtain a mathematical model, several disturbance signals were considered, including systematic uncertainties of the parameters, the perpetually changing environment, the interference from obstacles with effective fuzzy rules, and a sliding mode of control. Furthermore, a combined strategy of environment recognition was used for the positional control of the robotic dolphin, incorporating sonar, path planning with an artificial potential field, and trajectory tracking. The simulation results show satisfactory performance of the 3D robotic dolphin with respect to flexible movement and trajectory tracking under the observed interference factors.

Rapid identification of adulterated cow milk by non-linear pattern recognition methods based on near infrared spectroscopy.

More than 800 representative milk samples, which consisted of 287 raw cow milk samples from different pastures surrounding Shanghai of China and 526 adulteration milk samples containing different pseudo proteins and thickeners, were collected and designed to demonstrate a method for rapidly discriminating adulterated milks based on near infrared (NIR) spectra. The NIR classification models were built by two non-linear supervised pattern recognition methods of improved support vector machine (I-SVM) and improved and simplified K nearest neighbours (IS-KNN). Uniform design theory was applied to optimize the parameters of SVM and thus the computation amount was reduced 90%. Both two methods exhibit good adaptability in discriminating adulterated milks from raw cow milks. Further investigation showed that the correction ratio for discriminating milk samples increased with the increasing of adulteration solutions' level in the adulterated milk. The concentration of adulterants is an important factor of influencing milk discrimination results of the NIR pattern recognition models. The results demonstrated the usefulness of NIR spectra combined with non-linear pattern recognition methods as an objective and rapid method for the authentication of complicated raw cow milks.