Urinary enterolignans and enterolignan-predicting microbial species are favourably associated with liver fat and other obesity markers.

Aims: Plant-derived lignans may protect against obesity, while their bioactivity needs gut microbial conversion to enterolignans. We used repeated measures to identify enterolignan-predicting microbial species and investigate whether enterolignans and enterolignan-predicting microbial species are associated with obesity. Methods: Urinary enterolignans, fecal microbiota, body weight, height, and circumferences of the waist (WC) and hips (HC) were repeatedly measured at the baseline and after 1 year in 305 community-dwelling adults in Huoshan, China. Body composition and liver fat [indicated by the controlled attenuation parameter (CAP)] were measured after 1 year. Multivariate-adjusted linear models and linear mixed-effects models were used to analyze single and repeated measurements, respectively. Results: Enterolactone and enterodiol levels were both inversely associated with the waist-to-hip ratio, body fat mass (BFM), visceral fat level (VFL), and liver fat accumulation (all P < 0.05). Enterolactone levels were also associated with lower WC (ß = -0.0035 and P = 0.013) and HC (ß = -0.0028 and P = 0.044). We identified multiple bacterial genera whose relative abundance was positively associated with the levels of enterolactone (26 genera) and enterodiol (22 genera, all P false discovery rate < 0.05), and constructed the enterolactone-predicting microbial score and enterodiol-predicting microbial score to reflect the overall enterolignan-producing potential of the host gut microbiota. Both these scores were associated with lower body weight and CAP (all P < 0.05). The enterolactone-predicting microbial score was also inversely associated with the BFM (ß = -0.1128 and P = 0.027) and VFL (ß = -0.1265 and P = 0.044). Conclusion: Our findings support that modulating the host gut microbiome could be a potential strategy to prevent obesity by enhancing the production of enterolignans.

Red meat intake, faecal microbiome, serum trimethylamine N-oxide and hepatic steatosis among Chinese adults.

BACKGROUND AND AIMS: Emerging evidence suggests a detrimental impact of high red meat intake on hepatic steatosis. We investigated the potential interplay between red meat intake and gut microbiome on circulating levels of trimethylamine N-oxide (TMAO) and hepatic steatosis risk. METHODS: This cross-sectional study was conducted in a representative sample of 754 community-dwelling adults in Huoshan, China. Diet was collected using 4 quarterly 3 consecutive 24-h dietary (12-day) recalls. We profiled faecal microbiome using 16S ribosomal RNA sequencing and quantified serum TMAO and its precursors using LC-tandem MS (n = 333). We detected hepatic steatosis by FibroScan. The adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were calculated using logistic regression. RESULTS: TMAO levels but not its precursors were positively associated with the likelihood of hepatic steatosis (aOR per 1-SD increment 1.86, 95% CI 1.04-3.32). We identified 14 bacterial genera whose abundance was associated with TMAO concentration (pFDR < .05) belonging to the phyla Firmicutes, Bacteroidetes, Actinobacteria and Proteobacteria families. Per 10 g/day increase in red meat intake was positively associated with TMAO levels among participants who had higher red meat intake (>70 g/day) and higher TMAO-predicting microbial scores (TMS, ß = .045, p = .034), but not among others (pinteraction = .030). TMS significantly modified the positive association between red meat and steatosis (pinteraction = .032), with a stronger association being observed among participants with higher TMS (aOR 1.30, 95% CI 1.07-1.57). CONCLUSIONS: The bacterial genera that predicted TMAO levels may jointly modify the association between red meat intake and TMAO levels and the subsequent risk of hepatic steatosis.

Synergistic effects of low-dose arsenic and N-methyl-N'-nitro-N-nitrosoguanidine co-exposure by altering gut microbiota and intestinal metabolic profile in rats.

Biological organisms are exposed to low-dose arsenic or N-nitro compounds (NOCs) alone or in combination worldwide, especially in areas with high cancer prevalence through drinking water or food exposure; however, information on their combined exposure effects is limited. Here, we conducted an in-depth study of the effects on the gut microbiota, metabolomics, and signaling pathways using rat models exposed to arsenic or N-methyl-N'-nitro-N-nitrosoguanidine (MNNG), one of the most active carcinogenic NOCs, separately or in combination with metabolomics and high-throughput sequencing. Compared to exposure alone, combined exposure to arsenic and MNNG exacerbated damage to gastric tissue morphology, interfered with intestinal microflora and substance metabolism, and exerted a stronger carcinogenic effect. This may be related to intestinal microbiota disorders, including Dyella, Oscillibacter, Myroides, and metabolic pathways such as glycine, serine, and threonine metabolism, arginine biosynthesis, central carbon metabolism in cancer, and purine and pyrimidine metabolism, thereby enhancing the cancer-causing effects of gonadotrophin-releasing hormone (GnRH), P53, and Wnt signaling pathways.

A Study of Personal Recognition Method Based on EMG Signal.

With the increasing development of internet, the security of personal information becomes more and more important. Thus, variety of personal recognition methods have been introduced to ensure persons' information security. Traditional recognition methods such as Personal Identification Number (PIN), or Identification tag (ID) are vulnerable to hackers. Then the biometric technology, which uses the unique physiological characteristics of human body to identify user information has been proposed. But the biometrics widely used at present such as human face, fingerprint, iris, and voice can also be forged and falsified. The biometric with living body features such as electromyography (EMG) signal is a good method to achieve aliveness detection and prevent the spoofing attacks. However, there are few studies on personal recognition based on EMG signal. According to the application context, personal recognition system may operate either in identification mode or verification mode. In the personal identification mode, the system recognizes an individual by searching the templates of all the users in the database for a match. While in the personal verification mode, the system validates a person's identity by comparing the captured features with her or his own template(s) stored in the system database. In this paper, both EMG-based personal identification method and EMG-based personal verification method are investigated. First, the Myo armband is placed on the right forearm (specifically, the height of the radiohumeral joint) of 21 subjects to collect the surface EMG signal under hand-open gesture. Then, two different methods are proposed for EMG-based personal identification, i.e., personal identification method based on Discrete Wavelet Transform (DWT) and ExtraTreesClassifier, and personal identification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Networks (CNN). Experiments with 21 subjects show that the identification accuracy of this two methods can achieve 99.206% and 99.203% respectively. Then based on the identification method using CWT and CNN, transfer learning algorithm is adopted to solve the model update problem when new data is added. Finally, an EMG-based personal verification method using CWT and siamese networks is proposed. Experiments show that the verification accuracy of this method can achieve 99.285%.

Dissecting a heterotic gene through GradedPool-Seq mapping informs a rice-improvement strategy.

Hybrid rice breeding for exploiting hybrid vigor, heterosis, has greatly increased grain yield. However, the heterosis-related genes associated with rice grain production remain largely unknown, partly because comprehensive mapping of heterosis-related traits is still labor-intensive and time-consuming. Here, we present a quantitative trait locus (QTL) mapping method, GradedPool-Seq, for rapidly mapping QTLs by whole-genome sequencing of graded-pool samples from F2 progeny via bulked-segregant analysis. We implement this method and map-based cloning to dissect the heterotic QTL GW3p6 from the female line. We then generate the near isogenic line NIL-FH676::GW3p6 by introgressing the GW3p6 allele from the female line Guangzhan63-4S into the male inbred line Fuhui676. The NIL-FH676::GW3p6 exhibits grain yield highly increased compared to Fuhui676. This study demonstrates that it may be possible to achieve a high level of grain production in inbred rice lines without the need to construct hybrids.