Stochastic determination of the spatial variation of potentially pathogenic bacteria communities in a large subtropical river.

Understanding the composition and assembly mechanism of waterborne pathogen is essential for preventing the pathogenic infection and protecting the human health. Here, based on 16S rRNA sequencing, we investigated the composition and spatial variation of potentially pathogenic bacteria from different sections of the Pearl River, the most important source of water for human in Southern China. The results showed that the potential pathogen communities consisted of 6 phyla and 64 genera, covering 11 categories of potential pathogens mainly involving animal parasites or symbionts (AniP), human pathogens all (HumPA), and intracellular parasites (IntCelP). Proteobacteria (75.87%) and Chlamydiae (20.56%) were dominant at the phylum level, and Acinetobacter (35.01%) and Roseomonas (8.24%) were dominant at the genus level. Multivariate analysis showed that the potential pathogenic bacterial community was significantly different among the four sections in the Pearl River. Both physicochemical factors (e.g., NO3-N, and suspended solids) and land use (e.g., urban land and forest) significantly shaped the pathogen community structure. However, spatial effects contributed more to the variation of pathogen community based on variation partitioning and path analysis. Null model based normalized stochasticity ratio analysis further indicated that the stochastic process rather than deterministic process dominated the assembly mechanisms by controlling the spatial patterns of potential pathogens. In conclusion, high-throughput sequencing shows great potential for monitoring the potential pathogens, and provided more comprehensive information on the potentially pathogenic community. Our study highlighted the importance of considering the influences of dispersal-related processes in future risk assessments for the prevention and control of pathogenic bacteria.

Integral characterization of normal and alopecic hair at different degeneration stages by in-situ visible and chemical imaging.

Direct exploration to differences between normal hair (NH) and alopecic hair (AH) at different degeneration stages is still lacking. To reveal compositional and structural variation of AH with reference to NH internally and externally, infrared spectroscopic imaging combined with scanning electron microscopy was applied to investigate integral changes of hair chemical profiles and surface texture structures, and infrared macro-fingerprinting analysis revealed detailed chemical compositions of NH and AH. Results showed that AH had excessive irregular laminated structures compared to NH, leading to a lower weight bearing capacity. Spatial distributions of lipids, phosphates, lipoproteins and phospholipids in hair transverse sections showed that their infrared absorptions were intensified and gradually centralized to medulla with average variable-areas increasing upto 2.3 folds (lipoproteins area changed from 13% in NH to 30% in AH)as the alopecia progressed. Extracted pixel spectra from the chemical images showed different fingerprint characteristics in 1075-1120 cm-1. Specifically, compared to NH, AH showed red shift of phosphate peaks, indicating the occurrence of phosphates transformation. In this study, in-situ visible and infrared chemical imaging directly revealed more irregular laminated scalps with decreasing weight bearing capacity and increasing distributive areas expanding to medulla of key components (phosphates, phospholipids, etc.) that were relevant to alopecia development from NH to AH, and offered a fast, eco-friendly and effective method for hair research.

Integrated recognition and quantitative detection of starch in surimi by infrared spectroscopy and spectroscopic imaging.

Surimi products have become increasingly-consumed food with prominent characteristics of high nutrition and convenience and its supply falls short of demand. However, due to exhausted fishery resource in recent years, surimi adulteration, such as addition of plant proteins, starch and other animal origin meat, is becoming serious, so recognition of these exogenous substances has become an urgent issue. In this study, Fourier transform infrared spectroscopy (FT-IR) combined with infrared spectroscopic imaging could distinguish heterogeneity in surimi qualitatively and quantitatively and obtain integral chemical images so that spatial distribution of each component in surimi could be visually displayed, thus a rapid recognition method and a prediction model were developed. The different starch contents in surimi had been primarily identified through intensity change of infrared absorption peaks at 1045cm-1 and 988cm-1, specifically with peak shifts to 1041cm-1 and to 992cm-1, respectively. In infrared imaging analysis, principal components (PCs) were separated and one key PC was confirmed as starch by characteristic peaks comparison at 1147cm-1, 1075cm-1, 997cm-1 and 930cm-1. Meanwhile, an established statistic model could predict starch content in surimi correctly with a reliable correlation coefficient (R=0.9856) and root mean square error of prediction (RMSEP=5.64). Therefore, FT-IR combined with infrared spectroscopic imaging could be applicable to integrally recognize and quantitatively detect starch in surimi.