PM2.5 Monitoring: Use Information Abundance Measurement and Wide and Deep Learning.

This article devises a photograph-based monitoring model to estimate the real-time PM2.5 concentrations, overcoming currently popular electrochemical sensor-based PM2.5 monitoring methods' shortcomings such as low-density spatial distribution and time delay. Combining the proposed monitoring model, the photographs taken by various camera devices (e.g., surveillance camera, automobile data recorder, and mobile phone) can widely monitor PM2.5 concentration in megacities. This is beneficial to offering helpful decision-making information for atmospheric forecast and control, thus reducing the epidemic of COVID-19. To specify, the proposed model fuses Information Abundance measurement and Wide and Deep learning, dubbed as IAWD, for PM2.5 monitoring. First, our model extracts two categories of features in a newly proposed DS transform space to measure the information abundance (IA) of a given photograph since the growth of PM2.5 concentration decreases its IA. Second, to simultaneously possess the advantages of memorization and generalization, a new wide and deep neural network is devised to learn a nonlinear mapping between the above-mentioned extracted features and the groundtruth PM2.5 concentration. Experiments on two recently established datasets totally including more than 100 000 photographs demonstrate the effectiveness of our extracted features and the superiority of our proposed IAWD model as compared to state-of-the-art relevant computing techniques.

Blind Quality Metric of DIBR-Synthesized Images in the Discrete Wavelet Transform Domain.

Free viewpoint video (FVV) has received considerable attention owing to its widespread applications in several areas such as immersive entertainment, remote surveillance and distanced education. Since FVV images are synthesized via a depth image-based rendering (DIBR) procedure in the "blind" environment (without reference images), a real-time and reliable blind quality assessment metric is urgently required. However, the existing image quality assessment metrics are insensitive to the geometric distortions engendered by DIBR. In this research, a novel blind method of DIBR-synthesized images is proposed based on measuring geometric distortion, global sharpness and image complexity. First, a DIBR-synthesized image is decomposed into wavelet subbands by using discrete wavelet transform. Then, the Canny operator is employed to detect the edges of the binarized low-frequency subband and high-frequency subbands. The edge similarities between the binarized low-frequency subband and high-frequency subbands are further computed to quantify geometric distortions in DIBR-synthesized images. Second, the log-energies of wavelet subbands are calculated to evaluate global sharpness in DIBR-synthesized images. Third, a hybrid filter combining the autoregressive and bilateral filters is adopted to compute image complexity. Finally, the overall quality score is derived to normalize geometric distortion and global sharpness by the image complexity. Experiments show that our proposed quality method is superior to the competing reference-free state-of-the-art DIBR-synthesized image quality models.

Reference-Free Quality Assessment of Sonar Images via Contour Degradation Measurement.

Sonar imagery plays a significant role in oceanic applications since there is little natural light underwater, and light is irrelevant to sonar imaging. Sonar images are very likely to be affected by various distortions during the process of transmission via the underwater acoustic channel for further analysis. At the receiving end, the reference image is unavailable due to the complex and changing underwater environment and our unfamiliarity with it. To the best of our knowledge, one of the important usages of sonar images is target recognition on the basis of contour information. The contour degradation degree for a sonar image is relevant to the distortions contained in it. To this end, we developed a new no-reference contour degradation measurement for perceiving the quality of sonar images. The sparsities of a series of transform coefficient matrices, which are descriptive of contour information, are first extracted as features from the frequency and spatial domains. The contour degradation degree for a sonar image is then measured by calculating the ratios of extracted features before and after filtering this sonar image. Finally, a bootstrap aggregating (bagging)-based support vector regression module is learned to capture the relationship between the contour degradation degree and the sonar image quality. The results of experiments validate that the proposed metric is competitive with the state-of-the-art reference-based quality metrics and outperforms the latest reference-free competitors.

HPLC-MS/MS-based analysis of everolimus in rabbit aqueous humor: pharmacokinetics of in situ gel eye drops of suspension.

Aim: Everolimus is an inhibitor of mTOR which is derived from rapamycin. Its high corneal permeability and bioavailability makes it an effective treatment for ocular disorders. To study the pharmacokinetics of in situ gel eye drops of everolimus suspension after ocular administration in rabbit aqueous humor, we developed a rapid and reliable HPLC-MS/MS method. Methodology/results: Ascomycin was selected as the internal standard. HPLC-MS/MS method was used to investigate the concentration-time relationship of everolimus in rabbit aqueous humor through protein precipitation technique. The validated results showed good linearity, accuracy and precision. Conclusion: The method developed in this study is rapid, sensitive and reliable for the determination of the concentration of everolimus in rabbit aqueous humor.

Cofactor engineering through heterologous expression of an NADH oxidase and its impact on metabolic flux redistribution in Klebsiella pneumoniae.

BACKGROUND: Acetoin is an important bio-based platform chemical. However, it is usually existed as a minor byproduct of 2,3-butanediol fermentation in bacteria. RESULTS: The present study reports introducing an exogenous NAD+ regeneration sysytem into a 2,3-butanediol producing strain Klebsiella pneumoniae to increse the accumulation of acetoin. Batch fermentation suggested that heterologous expression of the NADH oxidase in K. pneumoniae resulted in large decreases in the intracellular NADH concentration (1.4 fold) and NADH/NAD+ ratio (2.0 fold). Metabolic flux analysis revealed that fluxes to acetoin and acetic acid were enhanced, whereas, production of lactic acid and ethanol were decreased, with the accumualation of 2,3-butanediol nearly unaltered. By fed-batch culture of the recombinant, the highest reported acetoin production level (25.9 g/L) by Klebsiella species was obtained. CONCLUSIONS: The present study indicates that microbial production of acetoin could be improved by decreasing the intracellular NADH/NAD+ ratio in K. pneumoniae. It demonstrated that the cofactor engineering method, which is by manipulating the level of intracellular cofactors to redirect cellular metabolism, could be employed to achieve a high efficiency of producing the NAD+-dependent microbial metabolite.