Latent blood fingerprints (LBFPs) can provide critical information of foul play and help identify the suspects at violent crime scenes. The current methods for LBFP visualization are still not satisfactory because of the low sensitivity or complicated protocol. This study demonstrates a simple and effective LBFP visualization strategy by integrating a new amphiphilic fluorescent amino-functionalized conjugated polymer with the cotton-pad developing protocol. LBFPs on various substrates are visualized by simply covering them with the polymer solution-soaked cotton pads. The images display clear fingerprint patterns, ridge details, and sweat pores, even on very challenging substrates such as painted wood and multicolored can. The gray value analysis confirms semiquantitatively the enhancement of the contrast between ridges and furrows. Even LBFPs with various contaminations or aged for more than 600 days are effectively developed and visualized. The developed fingerprint images show superior stability over long storage time and against solvent washing. Moreover, the polymer causes no degradation of DNAs in the blood, suggesting the possibility of further DNA profiling and identification after development. The mechanistic investigation suggests that the formation of positive or inverted images can be attributed to the synergistic effects from the affinity between polymer and blood, and the affinity betwen polymer and substrate, as well as the slight quenching of polymer fluorescence by blood. Furthermore, the covalent bonding between the protonated primary amino group and proteins in blood endows the stability of the developed fingerprints. The result rationalizes the molecular design of the fluorescent polymer and sheds new light on the future strategies to effective LBFP visualization in practical applications.
Lipids establish the specialized thylakoid membrane of chloroplast in eukaryotic photosynthetic organisms, while the molecular basis of lipid transfer from other organelles to chloroplast remains further elucidation. Here we revealed the structural basis of Arabidopsis Sec14 homology proteins AtSFH5 and AtSFH7 in transferring phosphatidic acid (PA) from endoplasmic reticulum (ER) to chloroplast, and whose function in regulating the lipid composition of chloroplast and thylakoid development. AtSFH5 and AtSFH7 localize at both ER and chloroplast, whose deficiency resulted in an abnormal chloroplast structure and a decreased thickness of stacked thylakoid membranes. We demonstrated that AtSFH5, but not yeast and human Sec14 proteins, could specifically recognize and transfer PA in vitro. Crystal structures of the AtSFH5-Sec14 domain in complex with L-α-phosphatidic acid (L-α-PA) and 1,2-dipalmitoyl-sn-glycero-3-phosphate (DPPA) revealed that two PA ligands nestled in the central cavity with different configurations, elucidating the specific binding mode of PA to AtSFH5, different from the reported phosphatidylethanolamine (PE)/phosphatidylcholine (PC)/phosphatidylinositol (PI) binding modes. Quantitative lipidomic analysis of chloroplast lipids showed that PA and monogalactosyldiacylglycerol (MGDG), particularly the C18 fatty acids at sn -2 position in MGDG were significantly decreased, indicating a disrupted ER-to-plastid (chloroplast) lipid transfer, under deficiency of AtSFH5 and AtSFH7. Our studies identified the role and elucidated the structural basis of plant SFH proteins in transferring PA between organelles, and suggested a model for ER-chloroplast interorganelle phospholipid transport from inherent ER to chloroplast derived from endosymbiosis of a cyanobacteriumproviding a mechanism involved in the adaptive evolution of cellular plastids.
Understanding the spatiotemporal variations in the mass concentrations of particulate matter ≤2.5 µm (PM2.5) in size is important for controlling environmental pollution. Currently, ground measurement points of PM2.5 in China are relatively discrete, thereby limiting spatial coverage. Aerosol optical depth (AOD) data obtained from satellite remote sensing provide insights into spatiotemporal distributions for regional pollution sources. In this study, data from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD (1 km resolution) product from Moderate Resolution Imaging Spectroradiometer (MODIS) and hourly PM2.5 concentration ground measurements from 2015 to 2020 in Dalian, China were used. Although trends in PM2.5 and AOD were consistent over time, there were seasonal differences. Spatial distributions of AOD and PM2.5 were consistent (R2 = 0.922), with higher PM2.5 values in industrial areas. The method of cross-dividing the test set by year was adopted, with AOD and meteorological factors as the input variable and PM2.5 as the output variable. A backpropagation neural network (BPNN) model of joint cross-validation was established; the stability of the model was evaluated. The trend in the predicted values of BPNN was consistent with the monitored values; the estimation result of the BPNN with the introduction of meteorological factors is better; coefficient of determination (R2) and RMSE standard deviation (SD) between the predicted values and the monitored values in the test set were 0.663–0.752 and 0.01–0.05 μg/m3, respectively. The BPNN was simpler and the training time was shorter compared with those of a regression model and support vector regression (SVR). This study demonstrated that BPNN could be effectively applied to the MAIAC AOD data to estimate PM2.5 concentrations.
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