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Objective Salidroside is an important plant-derived aromatic compound with diverse biological properties. The main objective of this study was to synthesize salidroside from tyrosol using UDP-glucosyltransferase (UGT) with in situ regeneration of UDP-glucose (UDPG). Results The UDP-glucosyltransferase 85A1 (UGT85A1) from Arabidopsis thaliana,which showed high activity and regioselectivity towards tyrosol, was selected for the production of salidroside. Then, an in vitro cascade reaction for in situ regeneration of UDPG was constructed by coupling UGT85A1 to sucrose synthase from Glycine max (GmSuSy). The optimal UGT85A1-GmSuSy activity ratio of 1:2 was determined to balance the efficiency of salidroside production and UDP-glucose regeneration. Different cascade reaction conditions for salidroside production were also determined. Under the optimized condition, salidroside was produced at a titer of 6.0 g/L with a corresponding molar conversion of 99.6% and a specific productivity of 199.1 mg/L/h in a continuous feeding reactor. Conclusion This is the highest salidroside titer ever reported so far using biocatalytic approach.

Two-dimensional (2D) organic–inorganic perovskites have shown interesting optical properties due to the natural quantum-well structures. The repetition of soft organic and hard inorganic intercalations also renders 2D perovskites rich phonon dynamics. Here, we investigated the coherent acoustic phonons in (PEA)2PbI4 perovskite films by time-resolved Brillouin spectroscopy. The coherent acoustic phonons were launched indirectly in perovskite films by exciting Au nanoplates which were used as optoacoustic transducers. A longitudinal sound velocity ν = 1937 ± 31 m/s, and an elastic modulus E = 9.84 GPa along the cross-plane direction of perovskites were obtained from analysis of the Brillouin oscillation frequency. Following a bead-spring model, we calculated a spring constant k ≈ 1.709 N m−1 for PEA cations which is comparably small for perovskites. We also demonstrated that coherent acoustic phonons are sensitive to differentiate structural heterogeneity and monitor dynamic phase evolution in perovskite films. Domains of PbI2-rich and PbI2-poor phases were identified. Under light stimulus, PbI2-poor phases were gradually disappearing and PbI2-rich phases became crystallized. The observations of structural and elastic heterogeneity and dynamic phase evolution using coherent acoustic phonons provide a toolbox for submicroscale elastic characterization of perovskites.

BackgroundThe human gut microbiome is associated with numerous human diseases. The whole-genome shotgun metagenomics sequencing helps accumulate a massive amount of gut microbiome data. However, few curated integrated platforms are available to explore the vast dataset. Advances in data generation pose new challenges to researchers attempting to analyze, visualize, and reuse published data.ResultGutMeta (human GUT whole-genome shotgun METAgenomics data analysis platform) is a one-stop online human gut metagenomic research platform that integrates a curated database, analyses, and visualizations.First, we built the Human Gut Metagenomics Database (HGMD), which contained taxonomy profiling and metadata of the metagenomics. HGMD collected the published human gut microbiome samples with whole metagenome shotgun (WMGS) sequencing data and consistently performed taxonomy classification using MetaPhlan3 for each sample. The various related metadata information was curated, and phenotypes were according to the MeSH ID. At this moment, HGMD contains 20,898 samples from 91 projects related to 65 diseases. Embedded tools could help users to explore the samples by keywords. Second, GutMeta provides researchers with user-friendly metagenomics analysis modules, including community diversity calculation, differential testing, dimension reduction, disease classifier construction, etc. Then, GutMeta provides corresponding interactive visualizations which can download as Scalable Vector Graphics (SVG), providing high-quality images. Further, GutMeta supplies two additional visualizations for the multi-level taxonomy overview for advanced investigations. GutMeta also supports online editing, including attribute adjustment, recoloring, reordering, and drag-and-drop. Third, GutMeta supports users in building their metagenomics analysis workspaces, including standard profiles uploading and built-in HGMD data import for online customized analyses and visualization.ConclusionGutMeta offers a solution to improve reproducibility in metagenomic research, with the standardized procedure from input data to downstream analysis and visualization. GutMeta is a free access analysis platform that integrates human gut WMGS sequencing data, nine online bioinformatics analysis and data visualization modules/pipelines, and a customized workspace. GutMeta is avaiable at https://GutMeta.deepomics.org.

Because the tooth surface micromorphology affects the performance of the gear and the finite element analysis and the grinding process of tooth surface through finite element analysis and simulation is complicated and time-consuming, the paper proposed a three-dimensional (3D) ultrasonic assistant grinding (RUAG) geometric simulation method to discover the micromorphology formation mechanism. Based on the motion path of a single grain, the 3D models of gear blank and the grinder was built. It helps to simulate the RUAG process. The morphologies of the simulated tooth surface were compared under conventional grinding (CG) and RUAG, which was consistent with the practice. It is proved that the geometric simulation method intuitively shows the micro dents on the tooth surface under different grinding parameters. The simulation method is helpful to analyze the surface morphology under different RUAG machining parameters and saves the cost and time of the actual production and processing.

BackgroundFecal microbiota transplantation (FMT) may treat microbiome-associated diseases effectively. However, the mechanism and pattern of the FMT process require expositions. Previous studies indicated the necessity to track the FMT process at the microbial strain level. At this moment, shotgun metagenomic sequencing enables us to study strain variations during the FMT.ResultWe implemented a software package PStrain-tracer to study microbial strain variations during FMT from the shotgun metagenomic sequencing data. The package visualizes the strain alteration and traces the microbial engraftment during the FMT process. We applied the package to two typical FMT datasets: one ulcerative colitis (UC) dataset and one Clostridium difficile infection (CDI) dataset. We observed that when the engrafted species has more than one strain in the source sample, 99.3% of the engrafted species will engraft only a subset of strains. We further confirmed that the all-or-nothing manner unsuited the engraftment of species with multiple strains by heterozygous single-nucleotide polymorphisms (SNPs) count, revealing that strains prefer to engraft independently. Furthermore, we discovered a primary determinant of strain engrafted success is their proportion in species, as the engrafted strains from the donor and the pre-FMT recipient with proportions 33.10 % (p-value = 6e − 06) and 37.08 % (p-value = 9e − 05) significantly higher than ungrafted strains on average, respectively. All the datasets indicated that the diversity of strains bursts after FMT and decreases to one after eight weeks for twelve species. Previous studies neglected strains with their corresponding species showing insignificant differences between different samples. With the package, from the UC dataset, we successfully determined the strain variations of the species Roseburia intestinalis, a beneficial species reducing intestinal inflammation, colonized in the cured UC patient being engrafted from the donor, even if the patient hosted the same species yet before treatment. We found seven strains in donors from the CDI dataset and one strain in pre-FMT recipients from eight species that associated CDI FMT failure.ConclusionPStrain-tracer is the first framework that tracks strain alterations in metagenomic sequencing data of FMT. PStrain-tracer implemented several methods specialized for FMT experiment samples, such as visualization of strains abundance alteration in the FMT experiment and determinant strains detection in FMT failure. We applied PStrain-tracer on two published datasets, uncovered novel strains related to FMT failure, and demonstrated the necessity of analyzing the whole-genome shotgun metagenomic data of FMT at the strain level. We also developed an online visualizer of PStrain-tracer for the users to adjust their visualized results online. The package is available at https://github.com/deepomicslab/PStrain-tracer.

Motivation The microbial community plays an essential role in human diseases and physiological activities. The functions of microbes can differ due to strain-level differences in the genome sequences. Shotgun metagenomic sequencing allows us to profile the strains in microbial communities practically. However, current methods are underdeveloped due to the highly similar sequences among strains. We observe that strains genotypes at the same single nucleotide variant (SNV) locus can be speculated by the genotype frequencies. Also, the variants in different loci covered by the same reads can provide evidence that they reside on the same strain. Results These insights inspire us to design PStrain, an optimization method that utilizes genotype frequencies and the reads which cover multiple SNV loci to profile strains iteratively based on SNVs in a set of MetaPhlAn2 marker genes. Compared to the state-of-art methods, PStrain, on average, improved the performance of inferring strains abundances and genotypes by 87.75% and 59.45%, respectively. We have applied the PStrain package to the dataset with two cohorts of colorectal cancer (CRC) and found that the sequences of Bacteroides coprocola strains are significantly different between CRC and control samples, which is the first time to report the potential role of B.coprocola in the gut microbiota of CRC. Availabilityand implementation https://github.com/wshuai294/PStrain. Supplementary information Supplementary data are available at Bioinformatics online.

The high concentrations of heavy metals in municipal industrial sewer networks will seriously impact the microorganisms of the activated sludge in the wastewater treatment plant (WWTP), thus deteriorating the effluent quality and destroying the stability of sewage treatment. Therefore, timely prediction and early warning of heavy metal concentrations in industrial sewer networks is crucial. However, due to the complex sources of heavy metals in industrial sewer networks, traditional physical modeling and linear methods cannot establish an accurate prediction model. Herein, we developed a Gated Recurrent Unit (GRU) neural network model based on a deep learning algorithm for predicting the concentrations of heavy metals in industrial sewer networks. To train the GRU model, we used low-cost and easy-to-obtain urban multi-source data, including socio-environmental indicator data, air environmental indicator data, water quantity indicator data, and easily measurable water quality indicator data. The model was applied to predict the concentrations of heavy metals (Cu, Zn, Ni, and Cr) in the sewer networks of an industrial area in southern China. The results are compared with the commonly used Artificial Neural Network (ANN) model. In this study, it was shown that the GRU had better prediction performance for Cu, Zn, Ni, and Cr concentrations, with the average R

Enzymatic oxidative dehydrogenation of biomass-derived l -lactate in the presence of O2 is regarded as a green alternative for pyruvate production, but the process also results in H2O2 accumulation. To remove the effect of H2O2 on pyruvate production, various potential catalases from different species were screened for their efficiency in H2O2 degradation. Then, an in vitro cascade biocatalysis was designed using lactate oxidase from Aerococcus viridans (AvLOX) and catalase from Ureibacillus thermosphaericus (UtCAT). The in situ removal of H2O2 by UtCAT significantly improved the yield and productivity of pyruvate. To simplify the technological processes and reduce production costs, a whole cell biocatalyst without the addition of catalase for the production of pyruvate was established by coexpressing AvLOX and UtCAT in Escherichia coli. By applying suitable coexpression strategies and changing the ribosome binding site (RBS) strengths, the optimal ratio of AvLOX:UtCAT(12.6:4127.3) in E. coli-AvLOX-(rbs3)UtCAT was finally achieved. Under the optimized transformation conditions, pyruvate was produced at a titer of 59.9 g/L and a yield of 90.8% in a substrate fed-batch process, promising an alternative route for the green production of pyruvate.

Goodness and beauty have always been important topics of debate in the field of philosophy and aesthetics. The present study used behavior and event-related potentials (ERPs) to investigate whether moral beauty judgments and moral goodness judgments involve different cognitive processes or the same cognitive process under different language labels for the same human act. Behavioral results showed that individuals gave significantly higher scores for a beautiful face than an ugly face when making moral beauty judgments, but there were no significant differences between the two conditions when making moral goodness judgments. The ERP experiment displayed larger P2 amplitudes and the late positive potential (LPP) amplitude was elicited when displaying beautiful faces but not ugly faces during moral beauty judgments. However, during moral goodness judgments, the P2 and LPP showed no significant differences under the two conditions. In general, we conclude that moral beauty judgments and moral goodness judgments involve different cognitive processes, although they objectively refer to the same human act. One of the most important differences between moral beauty judgments and moral goodness judgments was that the former process involved an image, whereas the latter did not. The present conclusion provides important insights into the research in aesthetic perception and moral sense.

Background Horizontal Gene Transfer (HGT) refers to the transfer of genetic materials between organisms through mechanisms other than parent-offspring inheritance. HGTs may affect human health through a large number of microorganisms, especially the gut microbiomes which the human body harbors. The transferred segments may lead to complicated local genome structural variations. Details of the local genome structure can elucidate the effects of the HGTs. Results In this work, we propose a graph-based method to reconstruct the local strains from the gut metagenomics data at the HGT sites. The method is implemented in a package named LEMON. The simulated results indicate that the method can identify transferred segments accurately on reference sequences of the microbiome. Simulation results illustrate that LEMON could recover local strains with complicated structure variation. Furthermore, the gene fusion points detected in real data near HGT breakpoints validate the accuracy of LEMON. Some strains reconstructed by LEMON have a replication time profile with lower standard error, which demonstrates HGT events recovered by LEMON is reliable. Conclusions Through LEMON we could reconstruct the sequence structure of bacteria, which harbors HGT events. This helps us to study gene flow among different microbial species.

SARS-CoV-2 is a single-stranded RNA betacoronavirus with a high mutation rate. The rapidly emerged SARS-CoV-2 variants could increase the transmissibility, aggravate the severity, and even fade the vaccine protection. Although the coinfections of SARS-CoV-2 with other respiratory pathogens have been reported, whether multiple SARS-CoV-2 variants coinfection exists remains controversial. This study collected 12,986 and 4,113 SARS-CoV-2 genomes from the GISAID database on May 11, 2020 (GISAID20May11) and April 1, 2021 (GISAID21Apr1), respectively. With the single-nucleotide variants (SNV) and network clique analysis, we constructed the single-nucleotide polymorphism (SNP) coexistence networks and noted the SNP number of the maximal clique as the coinfection index. The coinfection indices of GISAID20May11 and GISAID21Apr1 datasets were 16 and 34, respectively. Simulating the transmission routes and the mutation accumulations, we discovered the linear relationship between the coinfection index and the coinfected variant number. Based on the linear relationship, we deduced that the COVID-19 cases in the GISAID20May11 and GISAID21Apr1 datasets were coinfected with 2.20 and 3.42 SARS-CoV-2 variants on average. Additionally, we performed Nanopore sequencing on 42 COVID-19 patients to explore the virus mutational characteristics. We found the heterozygous SNPs in 41 COVID-19 cases, which support the coinfection of SARS-CoV-2 variants and challenge the accuracy of phylogenetic analysis. In conclusion, our findings reported the coinfection of SARS-CoV-2 variants in COVID-19 patients, demonstrated the increased coinfected variants number in the epidemic, and provided clues for the prolonged viral shedding and severe symptoms in some cases.

The content of fat, oil and grease (FOG) in the sewer network sediments is the key indicator for diagnosing sewer blockage and overflow. However, the traditional FOG detection is time-consuming and costly, and the establishment of mathematical models based on statistical methods to predict the content of FOG fail to provide satisfactory accuracy. Herein, a deep learning algorithm used a data-driven FOG content prediction model is proposed to achieve a more accurate prediction of FOG content. Meanwhile, global sensitivity analysis (GSA) is exploited to evaluate the contribution of input indicators to the output indicator (FOG) in the model, so that some input indicators that have less impact on the prediction performance can be screened out, the best combination of input indicators can be determined, and the operation cost of the model can be reduced. To evaluate the effectiveness of the proposed model, a case study was conducted in a city in southern China. The experimental results indicate that the prediction model obtains good FOG estimations and performs well from a single site to multiple sites with a mean R2 of 0.922, showing a good generalization performance. Through GSA, the key input indicators in the model were identified as pH, water temperature (T), relative humidity (RH), sewage flow (Flow), drinking water supply (DWS), velocity (V) and conductivity (σ), and the input indicators such as air pressure (AP), population (Pop.), and liquid level (LV) can be reduced without affecting the prediction accuracy of the model.

Background Myasthenia gravis (MG) is an acquired immune-mediated disorder of the neuromuscular junction that causes fluctuating skeletal muscle weakness and fatigue. Pediatric MG and adult MG have many different characteristics, and current MG diagnostic methods for children are not quite fit. Previous studies indicate that alterations in the gut microbiota may be associated with adult MG. However, it has not been determined whether the gut microbiota are altered in pediatric MG patients. Methods Our study recruited 53 pediatric MG patients and 46 age- and gender-matched healthy controls (HC). We sequenced the fecal samples of recruited individuals using whole-genome shotgun sequencing and analyzed the data with in-house bioinformatics pipeline. Results We built an MG disease classifier based on the abundance of five species, Fusobacterium mortiferum, Prevotella stercorea, Prevotella copri, Megamonas funiformis, and Megamonas hypermegale. The classifier obtained 94% area under the curve (AUC) in cross-validation and 84% AUC in the independent validation cohort. Gut microbiome analysis revealed the presence of human adenovirus F/D in 10 MG patients. Significantly different pathways and gene families between MG patients and HC belonged to P. copri, Clostridium bartlettii, and Bacteroides massiliensis. Based on functional annotation, we found that the gut microbiome affects the production of short-chain fatty acids (SCFAs), and we confirmed the decrease in SCFA levels in pediatric MG patients via serum tests. Conclusions The study indicated that altered fecal microbiota might play vital roles in pediatric MG’s pathogenesis by reducing SCFAs. The microbial markers might serve as novel diagnostic methods for pediatric MG.

This paper introduces our solution for the Track2 in AI City Challenge 2021 (AICITY21). The Track2 is a vehicle re-identification (ReID) task with both the real-world data and synthetic data. We mainly focus on four points, i.e. training data, unsupervised domain-adaptive (UDA) training, post-processing, model ensembling in this challenge. (1) Both cropping training data and using synthetic data can help the model learn more discriminative features. (2) Since there is a new scenario in the test set that dose not appear in the training set, UDA methods perform well in the challenge. (3) Post-processing techniques including re-ranking, image-to-track retrieval, inter-camera fusion, etc, significantly improve final performance. (4) We ensemble CNN-based models and transformer-based models which provide different representation diversity. With aforementioned techniques, our method finally achieves 0.7445 mAP score, yielding the first place in the competition. Codes are available at https://github.com/michuanhaohao/AICITY2021_Track2_DMT., Comment: CVPR 2021 AI CITY CHALLENGE City-Scale Multi-Camera Vehicle Re-Identification Top 1. arXiv admin note: text overlap with arXiv:2004.10547

Ergosterol, a terpenoid compound produced by fungi, is an economically important metabolite serving as the direct precursor of steroid drugs. Herein, ergsosterol biosynthetic pathway modification combined with storage capacity enhancement was proposed to synergistically improve the production of ergosterol in Saccharomyces cerevisiae. S. cerevisiae strain S1 accumulated the highest amount of ergosterol [7.8 mg/g dry cell weight (DCW)] among the wild-type yeast strains tested and was first selected as the host for subsequent metabolic engineering studies. Then, the push and pull of ergosterol biosynthesis were engineered to increase the metabolic flux, overexpression of the sterol acyltransferase gene ARE2 increased ergosterol content to 10 mg/g DCW and additional overexpression of a global regulatory factor allele (UPC2-1) increased the ergosterol content to 16.7 mg/g DCW. Furthermore, considering the hydrophobicity sterol esters and accumulation in lipid droplets, the fatty acid biosynthetic pathway was enhanced to expand the storage pool for ergosterol. Overexpression of ACC1 coding for the acetyl-CoA carboxylase increased ergosterol content from 16.7 to 20.7 mg/g DCW. To address growth inhibition resulted from premature accumulation of ergosterol, auto-inducible promoters were employed to dynamically control the expression of ARE2, UPC2-1, and ACC1. Consequently, better cell growth led to an increase of ergosterol content to 40.6 mg/g DCW, which is 4.2-fold higher than that of the starting strain. Finally, a two-stage feeding strategy was employed for high-density cell fermentation, with an ergosterol yield of 2986.7 mg/L and content of 29.5 mg/g DCW. This study provided an effective approach for the production of ergosterol and other related terpenoid molecules.

Purpose Workplace thriving is a psychological state that promotes employee health and development. In addition to presenting a useful instrument that captures the nature of a thriving work life in China, the purpose of this paper is to investigate important factors that influence one’s thriving status within this national context. Design/methodology/approach Using diverse approaches across different samples, Study 1 contextualized the content of a workplace thriving scale (WTS) to fit the Chinese context. Study 2 tested the effects of learning goal orientation, exploration at work and role ambiguity on workplace thriving, employing a mediation model. As a supplement, Study 3 examined the test–retest reliability of workplace thriving. Findings Study 1 confirmed that in the Chinese setting, workplace thriving is a higher order construct represented by both a sense of learning and a sense of vitality. Study 2 found that learning goal orientation and exploration at work fostered thriving, while role ambiguity reduced thriving. Also, exploration mediated the relationship between learning goal orientation and thriving. Study 3 verified that the WTS was reliable over time in the Chinese setting, further increasing the reliability of results from Studies 1 and 2. Originality/value By rigorously and formally contextualizing the concept/construct of workplace thriving in China, this paper is informative for future research on thriving at work in Eastern cultures.