Comparison of Population-Weighted Exposure Estimates of Air Pollutants Based on Multiple Geostatistical Models in Beijing, China.

Various geostatistical models have been used in epidemiological research to evaluate ambient air pollutant exposures at a fine spatial scale. Few studies have investigated the performance of different exposure models on population-weighted exposure estimates and the resulting potential misclassification across various modeling approaches. This study developed spatial models for NO2 and PM2.5 and conducted exposure assessment in Beijing, China. It explored three spatial modeling approaches: variable dimension reduction, machine learning, and conventional linear regression. It compared their model performance by cross-validation (CV) and population-weighted exposure estimates. Specifically, partial least square (PLS) regression, random forests (RF), and supervised linear regression (SLR) models were developed based on an ordinary kriging (OK) framework for NO2 and PM2.5 in Beijing, China. The mean squared error-based R2 (R2mse) and root mean squared error (RMSE) in leave-one site-out cross-validation (LOOCV) were used to evaluate model performance. These models were used to predict the ambient exposure levels in the urban area and to estimate the misclassification of population-weighted exposure estimates in quartiles between them. The results showed that the PLS-OK models for NO2 and PM2.5, with the LOOCV R2mse of 0.82 and 0.81, respectively, outperformed the other models. The population-weighted exposure to NO2 estimated by the PLS-OK and RF-OK models exhibited the lowest misclassification in quartiles. For PM2.5, the estimates of potential misclassification were comparable across the three models. It indicated that the exposure misclassification made by choosing different modeling approaches should be carefully considered, and the resulting bias needs to be evaluated in epidemiological studies.

Catalytic oxidation of toluene by manganese oxides: Effect of K+ doping on oxygen vacancy.

Alkali metal potassium was beneficial to the electronic regulation and structural stability of transition metal oxides. Herein, K ions were introduced into manganese oxides by different methods to improve the degradation efficiency of toluene. The results of activity experiments indicated that KMnO4-HT (HT: Hydrothermal method) exhibited outstanding low-temperature catalytic activity, and 90% conversion of toluene can be achieved at 243°C, which was 41°C and 43°C lower than that of KNO3-HT and Mn-HT, respectively. The largest specific surface area was observed on KMnO4-HT, facilitating the adsorption of toluene. The formation of cryptomelane structure over KMnO4-HT could contribute to higher content of Mn3+ and lattice oxygen (Olatt), excellent low-temperature reducibility, and high oxygen mobility, which could increase the catalytic performance. Furthermore, two distinct degradation pathways were inferred. Pathway Ⅰ (KMnO4-HT): toluene → benzyl → benzoic acid → carbonate → CO2 and H2O; Pathway ⅠⅠ (Mn-HT): toluene → benzyl alcohol → benzoic acid → phenol → maleic anhydride → CO2 and H2O. Fewer intermediates were detected on KMnO4-HT, indicating its stronger oxidation capacity of toluene, which was originated from the doping of K+ and the interaction between KOMn. More intermediates were observed on Mn-HT, which can be attributed to the weaker oxidation ability of pure Mn. The results indicated that the doping of K+ can improve the catalytic oxidation capacity of toluene, resulting in promoted degradation of intermediates during the oxidation of toluene.

[A Comparison Study on Multiple Modeling Approaches for Air Pollutant Geographic Model Development in Shanghai].

Geostatistical models have been widely used in the exposure assessment of ambient air pollutants. However, few studies have focused on comparisons of modeling approaches and their prediction results. Here, we collected the NO2 and PM2.5 monitoring data from 55 sites in Shanghai from 2016 to 2019 and the geographic variables, such as road network, points of interest of emission locations, and satellite data were included. We used partial least squares regression (PLS), supervised linear regression (SLR), and random forest (RF) algorithms to develop spatial models and used ordinary kriging (OK) to develop a two-step model. We evaluated the models using a 5-fold cross validation method and selected the best model structure for each modeling approach between one-or two-step models that had been developed with or without OK. The results revealed that the best NO2 models were the RF-OK (Rmse2 was 0.70-0.82) and PLS-OK (Rmse2 was 0.78-0.84) models; the PLS model for PM2.5(Rmse2 was 0.62-0.71) outperformed the other PM2.5 models. We used the best models to predict annual exposures in Shanghai at a 1 km spatial scale and conducted the correlation analysis among the predictions of the best models. The results demonstrated that the NO2 predictions had higher correlation coefficients (r was 0.82-0.91) compared with those of the PM2.5 models (r was 0.66-0.96). Based on the exposure results predicted using the three models in 2019, we evaluated the cumulative population exposure concentrations for NO2 and PM2.5 in Shanghai.

Intermittent induction of LEA peptide by lactose enhances the expression of insecticidal proteins in Bacillus thuringiensis.

Cry toxins from Bacillus thuringiensis (Bt) have been extensively applied in agriculture to substitute the use of chemical insecticides. We have previously reported the use of a coexpression system in which late embryogenesis abundant (LEA) peptides under the control of the lac promoter increase the expression of insecticidal proteins in Bt. The use of lactose to induce the expression of LEA peptides may be a desirable alternative to isopropyl ß-D-thiogalactopyranoside, the most frequently used inducer for recombinant protein expression. In this study we investigated the use of lactose as an inducer for optimal protein expression. We observed enhanced insecticidal Cry protein expression by applying a simple technique based on intermittent induction, and then optimized concentration and the point of induction time from the 11th h to the 15th h. Our data suggest that intermittent induction of lactose might be a new technique for the enhancement of bacterial protein expression.

Design and Synthesis of Novel Peptides to Protect Ferulic Acid against Ultraviolet Radiation Based on Domain Site IIA of Bovine Serum Albumin.

Ferulic acid (FA) is known for its excellent antioxidant properties, which can provide many health benefits. One of its drawbacks is its instability under UVA light, which limits its potency. In this study, the new peptides LW2 (QNKRFYFRKNQ) and CW2 (a cyclic form of LW2) were designed based on bovine serum albumin site IIA conformation. A UVA irradiation experiment was performed to investigate the protective ability of these peptides towards FA against UVA damage. The percentages of FA remaining under UV irradiation due to the protection of CW2 and LW2 were 83% and 76%, respectively. The results showed the importance of the cationic residues and hydrophobic residues included in the peptide sequences. Moreover, the cyclic rigid structure showed greater protective ability as compared to its linear counterpart.

Two fibrinogen-related proteins (FREPs) in the razor clam (Sinonovacula constricta) with a broad recognition spectrum and bacteria agglutination activity.

Fibrinogen-related proteins (FREPs) that contain only the fibrinogen-related domain are likely involved in pathogen recognition. In this study, we identified two FREPs from the razor clam (Sinonovacula constricta), called ScFREP-1 and ScFREP-2, and investigated their roles in the immune response. Both ScFREP-1 and ScFREP-2 contained a fibrinogen-related domain at the C-terminal. ScFREP-1 and ScFREP-2 mRNAs were detected in all adult clam tissues tested, with the highest expression levels in the gill and mantle, respectively. Their expression levels were significantly upregulated after microbe infection. Recombinant ScFREPs could bind Gram-positive and Gram-negative bacteria as well as some pathogen-associated molecular patterns (PAMPs), and they could agglutinate those bacteria. These results showed that ScFREPs functioned as potential pattern recognition receptors to mediate immune response by recognizing PAMPs and agglutinating invasive microbes.

Integrated analysis of transcriptomic and metabolomic data to evaluate responses to hypersalinity stress in the gill of the razor clam (Sinonovacula constricta).

Salinity is an important ecological factor that affects physiological metabolism, survival, and distribution of marine organisms. Despite changes in the osmolarity and composition of the cytosol during salinity shifts, marine mollusks are able to maintain their metabolic function. The razor clam (Sinonovacula constricta) survives the wide range of salinity in the intertidal zone via changes in behavior and physiology. To explore the stress responses and mechanisms of salinity tolerance in razor clams, we collected transcriptomic and metabolomic data from a control group (salinity 20‰, S20) and a salinity-stress group (salinity 35‰, S35). The transcriptome data showed that genes related to the immune system, cytoskeleton remodeling, and signal transduction pathways dominated in the S35 group to counteract hypersalinity stress in the gill. The metabolomic analysis showed that 142 metabolites were significantly different between the S35 and S20 groups and that amino acid and carbohydrate metabolism were affected by hypersalinity stress. Levels of amino acids and energy substances, such as l-proline, isoleucine, and fructose, were higher in the gill of the S35 group. The combination of transcriptomic and metabolomic data indicated that metabolism of amino acids, carbohydrates, and lipids was enhanced in the gill during adaptation to high salinity. These results clarified the complex physiological processes involved in the response to hyperosmotic stress and maintenance of metabolism in the gill of razor clams. These findings provide a reference for further study of the biological responses of euryhaline shellfish to hyperosmotic stress and a molecular basis for the search for populations with high salinity tolerance.