Retrieval of frequency spectrum from time-resolved spectroscopic data: comparison of Fourier transform and linear prediction methods

Femtosecond time-resolved signals often display oscillations arising from the nuclear and electronic wave packet motions. Fourier power spectrum is generally used to retrieve the frequency spectrum. We have shown by numerical simulations and coherent phonon spectrum of single walled carbon nanotubes (SWCNT) that the Fourier power spectrum may not be appropriate to obtain the spectrum, when the peaks overlap with varying phases. Linear prediction singular value decomposition (LPSVD) can be a good alternative for this case. We present a robust way to perform LPSVD analysis and demonstrate the method for the chirality assignment of SWCNT through the time-domain coherent phonon spectroscopy.X1133sciescopu

Unravelling the UV/H2O2 process using bioelectrochemically synthesized H2O2 to reuse waste nutrient solution

Abstract In this study, waste nutrient solution (WNS) was used as a catholyte in a bioelectrochemical cell to directly produce hydrogen peroxide (H2O2), after which the H2O2- containing WNS was integrated with the downstream UV oxidation process to meet quality standards for reuse. The generated current in the bioelectrochemical cell was successfully utilized at the cathode to produce H2O2 in WNS using a two-electron oxygen reduction reaction with different reaction times. The cathodic reaction time with the highest H2O2 production (504 ± 5.2 mg l−1) was 48 h, followed by that obtained from 24 h (368 ± 4.1 mg l−1), 12 h (158.8 ± 2.4 mg l−1), and 6 h (121.1 ± 4.1 mg l−1) reaction times. During H2O2 generation, calcium, magnesium, and phosphate in the WNS were recovered in the form of precipitates under alkaline conditions. The H2O2-containing WNS was further treated with different UV doses. After UV/H2O2 treatment, excitation-emission matrix and molecular weight distribution analyses demonstrated that aromatic compounds were reduced. Moreover, the gene expressions of sul1 (up to 95.65%), tetG (up to 93.88%), and aadA (up to 95.32%) were clearly downregulated compared with those of a control sample. Finally, a high disinfection efficiency was achieved with higher UV doses, resulting in successful seed germination. Thus, our results indicate that the developed method can be a promising process for reusing WNS in hydroponic systems

Korean childhood asthma study (KAS): a prospective, observational cohort of Korean asthmatic children

Abstract Background Asthma is a syndrome composed of heterogeneous disease entities. Although it is agreed that proper asthma endo-typing and appropriate type-specific interventions are crucial in the management of asthma, little data are available regarding pediatric asthma. Methods We designed a cluster-based, prospective, observational cohort study of asthmatic children in Korea (Korean childhood Asthma Study [KAS]). A total of 1000 Korean asthmatic children, aged from 5 to 15 years, will be enrolled at the allergy clinics of the 19 regional tertiary hospitals from August 2016 to December 2018. Physicians will verify the relevant histories of asthma and comorbid diseases, as well as airway lability from the results of spirometry and bronchial provocation tests. Questionnaires regarding subjects’ baseline characteristics and their environment, self-rating of asthma control, and laboratory tests for allergy and airway inflammation will be collected at the time of enrollment. Follow-up data regarding asthma control, lung function, and environmental questionnaires will be collected at least every 6 months to assess outcome and exacerbation-related aggravating factors. In a subgroup of subjects, peak expiratory flow rate will be monitored by communication through a mobile application during the overall study period. Cluster analysis of the initial data will be used to classify Korean pediatric asthma patients into several clusters; the exacerbation and progression of asthma will be assessed and compared among these clusters. In a subgroup of patients, big data-based deep learning analysis will be applied to predict asthma exacerbation. Discussion Based on the assumption that asthma is heterogeneous and each subject exhibits a different subset of risk factors for asthma exacerbation, as well as a different disease progression, the KAS aims to identify several asthma clusters and their essential determinants, which are more suitable for Korean asthmatic children. Thereafter we may suggest cluster-specific strategies by focusing on subjects’ personalized aggravating factors during each exacerbation episode and by focusing on disease progression. The KAS will provide a good academic background with respect to each interventional strategy to achieve better asthma control and prognosis