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Features as well as chance review of work-related exposure to ultrafine debris produced by cooking food from the Chinese language bistro.
OBJECTIVE To determine whether sleep state maturity can be estimated accurately using conventional electroencephalography (cEEG) or amplitude-integrated electroencephalography (aEEG) features concerning sleep in neurologically unimpaired preterm infants. METHODS A total of 51 preterm infants were monitored with cEEG-polygraphy and simultaneous aEEG. Sleep state maturity of EEG corresponded to specific postmenstrual age (PMA). PMA on cEEG was blindly estimated according to cEEG patterns (indicated as background continuity, frequencies, and voltages) as well as developmental markers in specific states. PMA on aEEG was blindly estimated based on the cycling score (cycling representing sleep state transitions) according to a pre-established scoring system. RESULTS A total of 51 EEGs recorded between 32 and 37 weeks PMA were analysed. A significant relationship between estimated PMA (ePMA) and actual chronological PMA (cPMA) was shown by linear regression both on cEEG (r = 0.93, β = 0.98, 95% confidence interval (CI) 0.87-1.09, p less then 0.001) and aEEG (r = 0.85, β = 0.83, 95% CI 0.69-0.98, p less then 0.001). The estimation gap (defined as ePMA minus cPMA) was between -2 and +2 weeks both on cEEG and aEEG. The percentage of estimation gap between -1 and +1 weeks was 96% for cEEG, which was higher than the estimate of 88% for aEEG. CONCLUSION Estimated maturity of sleep state was well correlated with cPMA both on cEEG and aEEG. PMA corresponding to state maturity could be estimated within two weeks of actual cPMA using either of these two tools. However, cEEG had higher accuracy compared with aEEG in the evaluation of sleep state maturity. Antibiotic-resistant bacteria are a threat to human and animal health. In recent years, the presence of extended-spectrum-β-lactamase (ESBL)-producing Escherichia (E.) coli in chicken manure, which is used as organic fertilizer, is a concern. The aim of the present study was to determine the effects of the carbon/nitrogen (C/N) ratio and moisture content (MC) on the survival of ESBL-producing E. coli during laboratory-scale composting of chicken manure. Nine different compost mixtures were enriched with an ESBL-producing E. selleckchem coli strain to an initial concentration of 7 log10 CFU/g, and the number of E. coli, temperature, and chemical conditions during composting were determined. The fastest decrease in E. coli occurred for all mixtures with a C/N ratio of 101. Additionally, dry mixtures with an MC of 20% and a C/N ratio of either 101 or 401 exhibited faster reductions in E. coli than the moist mixtures did, despite having lower maximum temperatures within the bioreactors. The decimal reduction times ranged from 0.27 days in a mixture with a C/N ratio of 101 and 40% MC to 4.82 days in a mixture with a C/N ratio of 401 and 40% MC. Both the C/N ratio and MC had a significant effect on the number of ESBL-producing E. coli and on temperature development; the C/N ratio additionally affected the pH value and content of ammoniacal nitrogen during chicken manure composting. The results of this study demonstrate a considerable range of mechanisms involved in the inactivation of E. coli during chicken manure composting. Cork is light, porous, carbon-rich, and renewable, which leads to competitive advantages in the preparation of biochar, as compared to other biomass material. In this work, we propose to convert cork powder into cork-based biochar as Cu (II) adsorbent via slow pyrolysis, thereby providing a reliable and simple method for recycling cork industrial waste. The physicochemical properties of cork-based biochar prepared under different pyrolysis temperatures (450, 550, 650, and 750 °C) and pyrolysis time (0.5, 1.0, 1.5, and 2.0 h) were characterized by elemental analysis, FT-IR, XRD, N2 adsorption and SEM. The adsorption capacity of cork-based biochar on heavy metal ions was further evaluated by Cu ion adsorption testing. Results showed that the cork-based biochar produced under conditions of higher pyrolysis temperature and time, has higher aromaticity and lower polarity, larger specific surface area, and enhanced Cu ion adsorption capacity. The maximum specific surface area of cork-based biochar prepared at 750 °C for 0.5 h was 392.5 m2/g, which surpasses most other biochars reported in previous studies, which are beneficial to the application of wastewater management. The SEM image demonstrated that the biochar retains the special hollow polyhedral cell structure of raw material cork. Furthermore, a large number of pores formed on the cell wall after high temperature pyrolysis, and the cells are connected with each other through these open pores. Finally, cork-based biochar exhibits superior Cu ion adsorption capacity (18.5 mg/g) with a shorter equilibrium time (4 h), which gives it a competitive advantage to similar adsorbents. Barrett cancer is a treatable disease when detected at an early stage. However, current screening protocols are often not effective at finding the disease early. Volumetric Laser Endomicroscopy (VLE) is a promising new imaging tool for finding dysplasia in Barrett's esophagus (BE) at an early stage, by acquiring cross-sectional images of the microscopic structure of BE up to 3-mm deep. However, interpretation of VLE scans is difficult for medical doctors due to both the size and subtlety of the gray-scale data. Therefore, algorithms that can accurately find cancerous regions are very valuable for the interpretation of VLE data. In this study, we propose a fully-automatic multi-step Computer-Aided Detection (CAD) algorithm that optimally leverages the effectiveness of deep learning strategies by encoding the principal dimension in VLE data. Additionally, we show that combining the encoded dimensions with conventional machine learning techniques further improves results while maintaining interpretability. Furthermore, we train and validate our algorithm on a new histopathologically validated set of in-vivo VLE snapshots. Additionally, an independent test set is used to assess the performance of the model. Finally, we compare the performance of our algorithm against previous state-of-the-art systems. With the encoded principal dimension, we obtain an Area Under the Curve (AUC) and F1 score of 0.93 and 87.4% on the test set respectively. We show this is a significant improvement compared to the state-of-the-art of 0.89 and 83.1%, respectively, thereby demonstrating the effectiveness of our approach.
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