A comparative metabolomics analysis of Açaí (Euterpe oleracea Mart.) fruit, food powder, and botanical dietary supplement extracts.

INTRODUCTION: Euterpe oleracea Mart. (açaí) is a botanical of interest to many who seek functional foods that provide antioxidant and anti-inflammatory properties. Cancer patients are increasingly taking botanical dietary supplements containing açaí to complement their conventional therapeutics, which may lead to serious adverse events. Before testing our açaí extracts in vitro for botanical-drug interactions, the goal is to chemically characterize our extracts for compounds whose biological activity in açaí is unknown. OBJECTIVE: The objective of this work was to develop a chemical fingerprinting method for untargeted characterization of açaí samples from a variety of sources, including food products and botanical dietary supplement capsules, made with multiple extraction solvents. METHODS: An optimized LC-MS method was generated for in-depth untargeted fingerprinting of chemical constituents in açaí extracts. Statistical analysis models were used to describe relationships between the açaí extracts based on molecular features found in both positive and negative mode ESI. RESULTS: In an attempt to elucidate the differences in metabolites among açaí extracts from different cultivars, we identified or tentatively identified 173 metabolites from the 16 extracts made from 6 different sources. Of these compounds, there are 138 reported in açaí for the first time. Statistical models showed similar yet distinct differences between the extracts tested based on the polarity of compounds present and the origin of the source material. CONCLUSION: A high-resolution mass spectrometry method was generated that allowed us to greatly characterize 16 complex extracts made from different sources of açaí with different extraction solvent polarities.

CW_ICA: an efficient dimensionality determination method for independent component analysis.

Independent component analysis (ICA) is a widely used blind source separation method for signal pre-processing. The determination of the number of independent components (ICs) is crucial for achieving optimal performance, as an incorrect choice can result in either under-decomposition or over-decomposition. In this study, we propose a robust method to automatically determine the optimal number of ICs, named the column-wise independent component analysis (CW_ICA). CW_ICA divides the mixed signals into two blocks and applies ICA separately to each block. A quantitative measure, derived from the rank-based correlation matrix computed from the ICs of the two blocks, is utilized to determine the optimal number of ICs. The proposed method is validated and compared with the existing determination methods using simulation and scalp EEG data. The results demonstrate that CW_ICA is a reliable and robust approach for determining the optimal number of ICs. It offers computational efficiency and can be seamlessly integrated with different ICA methods.

Regulation of friction pair to promote conversion of mechanical energy to chemical energy on Bi2WO6 and realization of enhanced tribocatalytic activity to degrade different pollutants.

Recently, friction-induced tribocatalysis has received tremendous attention through converting mechanical energy to chemical energy. However, its efficiency is much lower than those of photocatalysis and piezocatalysis, and its environmental application is limited in dye degradation. Herein, we developed a facile approach to improve the tribocatalytic activity of Bi2WO6 via adding trace polymer powders to form friction pairs with Bi2WO6. Among various polymers, PTFE was demonstrated to be the best counterpart of Bi2WO6. Subsequently, the PTFE dosage, stirring rate, magnetic bar size and number, and stirring mode were further optimized. The PTFE-promoted Bi2WO6 tribocatalysis was verified to possess excellent performance not only for removing different dyes, but also for degrading chlorophenols that are typical persistent organic pollutants. Multiple uses of the recycled catalysts indicated its good stability and prominent tribocatalytic durability. EPR measurements suggested the generation of hydroxyl radical and superoxide radical, which were determined to be continuously generated within 12 h at the rates of 0.88 µM h-1 and 85 µM h-1, respectively. Subsequently, a possible mechanism was proposed to explain the enhanced performance of the PTFE-promoted Bi2WO6 tribocatalysis. Finally, on basis of the detected intermediates, the degradation pathways of Rhodamine B and 2,4-Dichlorophenol during tribocatalysis were suggested.

Strain-Level Discrimination of Bacteria by Liquid Chromatography and Paper Spray Ion Mobility Mass Spectrometry.

Determining bacterial identity at the strain level is critical for public health to enable proper medical treatments and reduce antibiotic resistance. Herein, we used liquid chromatography, ion mobility, and tandem MS (LC-IM-MS/MS) to distinguish Escherichia coli (E. coli) strains. Numerical multivariate statistics (principal component analysis, followed by linear discriminant analysis) showed the capability of this method to perform strain-level discrimination with prediction rates of 96.1% and 100% utilizing the negative and positive ion information, respectively. The tandem MS and LC separation proved effective in discriminating diagnostic lipid isomers in the negative mode, while IM separation was more effective in resolving lipid conformational biomarkers in the positive ion mode. Because of the clinical importance of early detection for rapid medical intervention, a faster technique, paper spray (PS)-IM-MS/MS, was used to discriminate the E. coli strains. The achieved prediction rates of the analysis of E. coli strains by PS-IM-MS/MS were 62.5% and 73.5% in the negative and positive ion modes, respectively. The strategy of numerical data fusion of negative and positive ion data increased the classification rates of PS-IM-MS/MS to 80.5%. Lipid isomers and conformers were detected, which served as strain-indicating biomarkers. The two complementary multidimensional techniques revealed biochemical differences between the E. coli strains confirming the results obtained from comparative genomic analysis. Moreover, the results suggest that PS-IM-MS/MS is a rapid, highly selective, and sensitive method for discriminating bacterial strains in environmental and food samples.

Enhanced photocatalytic activity of S-doped graphitic carbon nitride hollow microspheres: Synergistic effect, high-concentration antibiotic elimination and antibacterial behavior.

For the past few years, graphitic carbon nitride (g-C3N4) has been widely used to eliminate environmental pollutants, but limited active site on surface and low separation/migration ability suppress its practical uses. Herein, we adopted a supramolecular self-assembly route followed with S doping to synthesize S-doped g-C3N4 with a hollow microsphere composition (SCNHM), where the shell was demonstrated to compose of ultrathin nanosheets. The unique structural characteristics endow the SCNHM with high specific surface area (∼81 m2 g-1) to provide abundant reaction sites and enhanced light-harvesting due to the light-scattering effect of hollow structure. Moreover, the S dopant meliorated the electronic structure to narrow the bandgap and promoted the charge separation/transfer capability. With this synergistic effect, the SCNHM presented greatly improved photocatalytic activity for degrading tetracycline hydrochloride (TC) compared to the CN, SCN and CNHM samples. This photocatalyst could eliminate high-concentration TC (50 mg L-1) in 18 min, and the 30 min removal efficiencies of 100 mg L-1 and 200 mg L-1 reached 92 % and 60 %, which is much better than the reported photocatalysts in literatures (usually ≤ 20 mg L-1). Additionally, the good photocatalytic durability was confirmed and the degradation pathway of TC was proposed. Furthermore, the SCNHM was proved to meanwhile possess superior performance for inactivating the typical Gram-positive bacterium of Staphylococcus aureus (S. aureus) and the typical Gram-negative bacterium of Escherichia coli (E. coli). Finally, based on determination of band alignment and detection of active species, a plausible photocatalytic mechanism was proposed.

Determination of the key role to affect the piezocatalytic activity of graphitic carbon nitride for tetracycline hydrochloride degradation in water.

Graphitic carbon nitride (g-C3N4) has been proved to possess intrinsic piezoelectricity and its two-dimensional (2D) nanosheets present piezocatalytic activity to produce hydrogen from water splitting and eliminate organic pollutants in wastewater. Specific surface area and piezoelectric polarization are of great significance to achieve high piezocatalytic activity, but it is difficult to simultaneously improve both of them. Herein, to reveal the dominant role in the piezocatalysis of g-C3N4, we investigated the effect of exfoliation level on the piezocatalytic activity for degrading tetracycline hydrochloride (TC). Characterization results indicated that the specific surface area of the bulk g-C3N4 was much lower than those of exfoliated g-C3N4 samples due to the decrease of size and thickness. However, piezoresponse force microscopy (PFM) and kelvin probe force microscopy (KPFM) examinations suggested the bulk g-C3N4 possessed the biggest piezoelectric polarization that gradually declined as increasing the exfoliation temperature. Through testing the piezocatalytic abatement of TC, the activity decline following the order of decrease in polarization was confirmed, which demonstrated the piezoelectric polarization was the dominant factor in the piezocatalysis of g-C3N4. This conclusion was also verified by the step-by-step performance decrease of the bulk g-C3N4 during the successive four piezocatalytic runs, where the ultrasound treatment promoted the delamination of g-C3N4. In addition, superoxide (·O2-) radical, hydroxyl (·OH) radical and polarized positive charge were determined to be main active species, and accordingly the bulk g-C3N4 had the highest ·OH and ·O2- concentrations, as well as the highest piezocurrent response. This work reveals the main role to affect the piezocatalytic performance of g-C3N4, and also provides a possible strategy to design piezocatalysts with optimized piezocatalytic activity.

Classification of EEG signals: An interpretable approach using functional data analysis.

Electroencephalography (EEG) is a noninvasive method to record electrical activity of the brain. The EEG data is continuous flow of voltages, in this paper, we consider them as functional data, and propose a three-stage algorithm based on functional data analysis, with the advantage of interpretability. Specifically, the time and frequency information are extracted by wavelet transform in the first stage. Then, functional testing is utilized to select EEG channels and frequencies that show significant differences for different human behaviors. In the third stage, we propose to use penalized multiple functional logistic regression to interpretably classify human behaviors. With simulation and a scalp EEG data as validation set, we show that the proposed three-stage algorithm provides an interpretable classification of the scalp EEG signals.

Influencing Factors and Countermeasures of the Health of Residents in the City Clusters Along the Middle Reaches of the Yangtze River.

This paper introduces several factors, namely, environmental pollution, medical level and environmental governance, into the Grossman's production function for health. Then, an empirical analysis was conducted based on the 2004-2016 panel data of the city clusters along the middle reaches of the Yangtze River. Through the analysis, the author evaluated and compared how different factors affect the health of residents in the three city clusters: Changsha-Zhuzhou-Xiangtan (CZT) city cluster, Wuhan city cluster and circum-Poyang Lake (CPL) city cluster. The results show that: (1) In all three city clusters, economic growth can effectively improve the health of residents, and environmental pollution is also a key influencing factor of the health of residents. (2) Medical level has a close correlation with the health of residents. In the CZT city cluster, the medical level is positively correlated with the health of residents; in the CPL city cluster, the correlation is negative and takes the shape of an inverted U in the long run. (3) In all three city clusters, the environmental governance has an inverted U-shape correlation with the health of residents, indicating that environmental governance is not enough to promote the health of residents. Finally, several countermeasures were put forward to enhance the health of residents in the study area. The research findings shed new light on policymaking for the health of residents.