From microbes to human beings, nontargeted metabolic profiling by liquid chromatography (LC)–mass spectrometry (MS) has been commonly used to investigate metabolic alterations. Still, a major challenge is the annotation of metabolites from thousands of detected features. The aim of our research was to go beyond coverage of metabolite annotation in common nontargeted metabolomics studies by an integrated multistep strategy applying data-dependent acquisition (DDA)-based ultrahigh-performance liquid chromatography (UHPLC)–high-resolution mass spectrometry (HRMS) analysis followed by comprehensive neutral loss matches for characteristic metabolite modifications and database searches in a successive manner. Using pooled human urine as a model sample for method establishment, we found 22% of the detected compounds having modifying structures. Major types of metabolite modifications in urine were glucuronidation (33%), sulfation (20%), and acetylation (6%). Among the 383 annotated metabolites, 100 were confirmed by standard compounds and 50 modified metabolites not present in common databases such as human metabolite database (HMDB) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were structurally elucidated. Practicability was tested by the investigation of urines from pregnant women diagnosed with gestational diabetes mellitus vs healthy controls. Overall, 83 differential metabolites were annotated and 67% of them were modified metabolites including five previously unreported compounds. To conclude, the systematic modifying group-assisted strategy can be taken as a useful tool to extend the number of annotated metabolites in biological and biomedical nontargeted studies.

To investigate the possibility of improving the quality of rice rich in resistant starch through operation of nonstarch polysaccharides, the high dietary fibre (7.24%) mutant cw and its wild-type R7954 were selected to study the physiochemical characteristics of starch before and after removal of nonstarch polysaccharides. Results showed that hydrolysed or partially hydrolysed nonstarch polysaccharides in cw decreased the resistant starch content significantly, from 15.23% to 10.8%. Nonstarch polysaccharides had significant influences on the gelatinisation temperature, RVA parameters of R7954, but no significant influences on that of cw. For cw, removal of cellulose increased swelling power and adhesiveness, decreased the hardness significantly, from 0.3 to 0.23 N, while the resistant starch content was still as high as 13.72% and showed no significant difference from the wild type. This suggests that the influences of nonstarch polysaccharides on starch properties depend both on the type of rice and the nonstarch polysaccharides. Operation on nonstarch polysaccharides for obtaining rice with lower glycemic index is feasible, but operation on nonstarch polysaccharides may also be an alternative way of improving the palatability for rice high in resistant starch.

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Atherosclerosis is characterized, as an inflammatory disorder in the circulatory system, with increasing tendency toward mortality and morbidity. Thus, developing novel therapeutic targeting inflammation is necessary. Here, we investigated the effects of interleukin‐36 receptor antagonist (IL‐36RN), a newly identified anti‐inflammatory factor, on atherosclerosis. The regulation of NLRP3 inflammasome by IL‐36RN was determined in vitro in macrophage cells after oxidized low‐density lipoprotein (ox‐LDL) stimulation. The IL‐1β and caspase‐1 p10 secretion were assessed by enzyme‐linked immunosorbent assay and western blot analysis. Finally, the IL‐36RN/NLRP3 inflammasome pathway was confirmed in apolipoprotein E‐deficient mice. IL‐36RN suppressed the expression of NLRP3, the secretion of IL‐1β, and caspase‐1 p10 in vitro, while IL‐36 pathway stimulation activated the NLRP3 inflammasome, which was inhibited by IL‐36RN. In the mouse model of atherosclerosis, IL‐36RN delivered by the lentivirus vector inhibited the development of atherosclerosis, and the atheroprotective effects of IL‐36RN were attenuated by IL‐36 pathway stimulation. Furthermore, the regulation of NLRP3 inflammasome by IL‐36RN was also confirmed in vivo. We demonstrated here that IL‐36RN exerted atheroprotective functions through IL‐36RN/NLRP3 inflammasome pathway.

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A novel fusion method is proposed for image sequence which based on the non-Gaussian statistical modeling of wavelet coefficients. Firstly, the source images are decomposed by dual tree complex wavelet transform (DT-CWT) respectively. Then, the wavelet coefficients are modeled using the generalized Gaussian distribution (GGD). Saliency measure, the weighted coefficient, is calculated by estimating distribution parameters. The pair of coefficients is fused through weighted average. Finally, the fused coefficients are reconstructed into a single fused image. The quality of the fused image is evaluated by three metric: entropy, mutual information and Q AB/F . The experimental results demonstrate that performance of the proposed method is prior to other two fusion approaches for infrared and visible dynamic image sequence..

Large-scale metabolite annotation is a bottleneck in untargeted metabolomics. Here, we present a structure-guided molecular network strategy (SGMNS) for deep annotation of untargeted ultra-performance liquid chromatography-high resolution mass spectrometry (MS) metabolomics data. Different from the current network-based metabolite annotation method, SGMNS is based on a global connectivity molecular network (GCMN), which was constructed by molecular fingerprint similarity of chemical structures in metabolome databases. Neighbor metabolites with similar structures in GCMN are expected to produce similar spectra. Network annotation propagation of SGMNS is performed using known metabolites as seeds. The experimental MS/MS spectra of seeds are assigned to corresponding neighbor metabolites in GCMN as their "pseudo" spectra; the propagation is done by searching predicted retention times, MS 1 , and "pseudo" spectra against metabolite features in untargeted metabolomics data. Then, the annotated metabolite features were used as new seeds for annotation propagation again. Performance evaluation of SGMNS showed its unique advantages for metabolome annotation. The developed method was applied to annotate six typical biological samples; a total of 701, 1557, 1147, 1095, 1237, and 2041 metabolites were annotated from the cell, feces, plasma (NIST SRM 1950), tissue, urine, and their pooled sample, respectively, and the annotation accuracy was >83% with RSD <2%. The results show that SGMNS fully exploits the chemical space of the existing metabolomes for metabolite deep annotation and overcomes the shortcoming of insufficient reference MS/MS spectra.