Construction of a 12-Gene Prognostic Risk Model and Tumor Immune Microenvironment Analysis Based on the Clear Cell Renal Cell Carcinoma Model.

OBJECTIVES: Accurate survival predictions and early interventional therapy are crucial for people with clear cell renal cell carcinoma (ccRCC). METHODS: In this retrospective study, we identified differentially expressed immune-related (DE-IRGs) and oncogenic (DE-OGs) genes from The Cancer Genome Atlas (TCGA) dataset to construct a prognostic risk model using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analysis. We compared the immunogenomic characterization between the high- and low-risk patients in the TCGA and the PUCH cohort, including the immune cell infiltration level, immune score, immune checkpoint, and T-effector cell- and interferon (IFN)-γ-related gene expression. RESULTS: A prognostic risk model was constructed based on 9 DE-IRGs and 3 DE-OGs and validated in the training and testing TCGA datasets. The high-risk group exhibited significantly poor overall survival compared with the low-risk group in the training (P < 0.0001), testing (P = 0.016), and total (P < 0.0001) datasets. The prognostic risk model provided accurate predictive value for ccRCC prognosis in all datasets. Decision curve analysis revealed that the nomogram showed the best net benefit for the 1-, 3-, and 5-year risk predictions. Immunogenomic analyses of the TCGA and PUCH cohorts showed higher immune cell infiltration levels, immune scores, immune checkpoint, and T-effector cell- and IFN-γ-related cytotoxic gene expression in the high-risk group than in the low-risk group. CONCLUSION: The 12-gene prognostic risk model can reliably predict overall survival outcomes and is strongly associated with the tumor immune microenvironment of ccRCC.

MYC and p53 alterations cooperate through VEGF signaling to repress cytotoxic T cell and immunotherapy responses in prostate cancer.

Patients with castration-resistant prostate cancer (CRPC) are generally unresponsive to tumor targeted and immunotherapies. Whether genetic alterations acquired during the evolution of CRPC impact immune and immunotherapy responses is largely unknown. Using our innovative electroporation-based mouse models, we generated distinct genetic subtypes of CRPC found in patients and uncovered unique immune microenvironments. Specifically, mouse and human prostate tumors with MYC amplification and p53 disruption had weak cytotoxic lymphocyte infiltration and an overall dismal prognosis. MYC and p53 cooperated to induce tumor intrinsic secretion of VEGF, which by signaling through VEGFR2 expressed on CD8+ T cells, could directly inhibit T cell activity. Targeting VEGF-VEGFR2 signaling in vivo led to CD8+ T cell-mediated tumor and metastasis growth suppression and significantly increased overall survival in MYC and p53 altered CPRC. VEGFR2 blockade also led to induction of PD-L1, and in combination with PD-L1 immune checkpoint blockade produced anti-tumor efficacy in multiple preclinical CRPC mouse models. Thus, our results identify a genetic mechanism of immune suppression through VEGF signaling in prostate cancer that can be targeted to reactivate immune and immunotherapy responses in an aggressive subtype of CRPC. Significance: Though immune checkpoint blockade (ICB) therapies can achieve curative responses in many treatment-refractory cancers, they have limited efficacy in CRPC. Here we identify a genetic mechanism by which VEGF contributes to T cell suppression, and demonstrate that VEGFR2 blockade can potentiate the effects of PD-L1 ICB to immunologically treat CRPC.

Racetrack FAIMS for High-Resolution and High-Sensitivity Characterization of Peptide Conformers.

A racetrack field asymmetric waveform ion mobility spectrometry (r-FAIMS) device, which consists of both cylindrical FAIMS (c-FAIMS) and planar FAIMS (p-FAIMS) sections with a 1 mm gap width, was developed and applied for high-resolution and high-sensitivity exploration of conformational diversity for peptides. The optimal operating conditions of r-FAIMS were systemically studied, and the performance of the fully optimized r-FAIMS was compared to a previously developed p-FAIMS in detail by using pure nitrogen as the FAIMS carrier gas. Relying on the ion focusing effect in the c-FAIMS section, the intensity of the FAIMS spectrum for doubly charged bradykinin ions acquired by using r-FAIMS is ∼8.5-fold higher than that acquired by using p-FAIMS under the same resolving power/resolution condition, implying about an order of magnitude better sensitivity of r-FAIMS. In addition, the peak separation resolution of r-FAIMS was ∼1.70-fold higher than p-FAIMS under a similar sensitivity condition for doubly charged bradykinin ions. Due to a reduced gap width of the newly designed r-FAIMS (1 mm) as compared to the previously developed p-FAIMS (1.88 mm), r-FAIMS can operate at a much higher separation field with a similar FAIMS dispersion voltage (DV) to gain significantly higher resolving power. For triply charged syntide 2 ions, the resolving power of r-FAIMS can easily exceed 120 at -3.5 kV DV by using pure nitrogen as the FAIMS carrier gas as compared to 44.2 resolving power obtained by using p-FAIMS at -4.0 kV DV. All of the experimental results have confirmed that r-FAIMS can perform structural characterization of biomolecules with both high resolution and high sensitivity.

2.09†µm high-gain, high-energy sub-nanosecond laser amplification system and its application to a millijoule-level mid-infrared ZnGeP2 optical parametric generator.

In this Letter, we report for the first time to our knowledge a 2 mJ-level 2.09 µm Ho:YAG regenerative amplifier (RA) seeded by the first-stage Ho-doped fiber (HDF) preamplifier of a gain-switched laser diode (GSLD). After the single-pass power amplifier (SPPA), the output of a 2.09 µm pulse laser with 1 kHz, 570 ps, and >10 mJ was achieved. The overall gain of the whole amplifier system was greater than 90 dB, providing a novel, stable, and reliable sub-nanosecond (sub-ns) pump source operating at a pulse repetition frequency (PRF) of 1 kHz for an optical parametric generator (OPG) based on ZnGeP2 (ZGP). Specifically, for the ZGP OPG structure, a maximum pulse energy of 1.82 mJ at 3-5 µm had been achieved with an injected pump pulse energy of 5.47 mJ, corresponding to a slope efficiency of 39.5% and an optical-to-optical conversion efficiency (OOCE) of 33.27%.

Narrowband mid-infrared optical parametric generator based on a type-II BaGa4Se7 crystal.

In this Letter, we first reported on a mid-infrared double-pass optical parametric generator (OPG) based on a single type-II phase-matching BaGa4Se7 (BGSe) crystal, pumped at 2.1 µm. The OPG achieved a maximum pulse energy of 55 µJ for generating narrowband mid-infrared laser pulses. The signal and idler lights exhibited center wavelengths of 4.04 and 4.33 µm, respectively, with bandwidths of 18.6 nm (11.4 cm-1) and 20.4 nm (10.9 cm-1). To improve the output performance, we utilized a cascaded scheme of type-I ZnGeP2 (ZGP) and type-II BGSe crystals. The spectral bandwidths of the signal and idler lights, nearing 4 µm, were narrower than 170 nm (90 cm-1), representing a significant improvement over the ZGP OPG. The cascaded OPG achieved a remarkable total optical-to-optical conversion efficiency (OOCE) of 14.9% and a maximum pulse energy of 0.329 mJ.

An optimize adaptable method for determining the monosaccharide composition of pectic polysaccharides.

Pectic polysaccharides are considered the highly complex natural plant polysaccharides which plays a vital role in plant tissue structure and human health. Detailed characterization of the monosaccharide composition can provide insights into the pectic polysaccharide structure. Nevertheless, when analyzing the monosaccharides of pectic polysaccharide, it is crucial to address the issue of incomplete hydrolysis that can occur due to the formation of acid-induced precipitates. Based on above, the main purpose of this article is to provide an optimized method for monosaccharide analysis of pectic polysaccharides through acid hydrolysis optimization using high-performance anion exchange chromatography (HPAEC) The results indicate that reducing the sample concentration to 0.5 mg/mL effectively reduces the acid gelling phenomenon and promotes the complete hydrolysis of pectin polysaccharides. The optimized parameters for acid hydrolysis involve 110 °C for 6 h in 2 M TFA. Furthermore, the consistency of this method is assessed, along with its ability to analyze pectin polysaccharides from various fruits. This hydrolysis approach holds promise for enabling accurate quantification of monosaccharide composition in pectic polysaccharides.

Engineering Bacillus licheniformis as industrial chassis for efficient bioproduction from starch.

Starch is an attractive feedstock in biorefinery processes, while the low natural conversion rate of most microorganisms limits its applications. Herein, starch metabolic pathway was systematically investigated using Bacillus licheniformis DW2 as the host organism. Initially, the effects of overexpressing amylolytic enzymes on starch hydrolysis were evaluated. Subsequently, the transmembrane transport system and intracellular degradation module were modified to accelerate the uptake of hydrolysates and their further conversion to glucose-6-phosphate. The DW2-derived strains exhibited robust growth in starch medium, and productivity of bacitracin and subtilisin were improved by 38.5% and 32.6%, with an 32.3% and 22.9% increase of starch conversion rate, respectively. Lastly, the employment of engineering strategies enabled another B. licheniformis WX-02 to produce poly-γ-glutamic acid from starch with a 2.1-fold increase of starch conversion rate. This study not only provided excellent B. licheniformis chassis for sustainable bioproduction from starch, but shed light on researches of substrate utilization.

Development of a fully packaged passive thermal regulator.

Thermal regulators are devices that can adopt either the role of a thermal insulator or a thermal conductor, depending on the thermal input conditions, and play an increasingly important role in thermal management systems. In this study, we developed and tested a new passive thermal regulator design that operates around room temperature and achieves high switching ratios. Our regulator is structurally integer, scalable, orientation-independent, resistant to vibration, and can be easily integrated into existing thermal management solutions. The working principle of the passive regulator is simple yet effective, whereby an aluminum plug attached to a bimetallic strip enters and exists a wedge-shaped gap between two conductors. We demonstrate a switching ratio of ≈ 50 ( - 8 + 34 ) :1 for a fully packaged prototype (≈ 320 (± 200):1 for a non-packaged regulator) operated in the open laboratory environment. Through geometric optimization using numerical simulations, we show that a switching ratio of ≈ 100 ( - 15 + 18 ) :1 can be easily obtained, which can be further increased by increasing the cross-sectional area of the input conductor, hence increasing the ON-state heat transfer rate. The OFF-state thermal performance is much less sensitive to the size of the conductor, making the device highly scalable.

Metagenomic comparison of intestinal microbiota between normal and liver fibrotic rhesus macaques (Macaca mulatta).

Liver fibrosis is an important pathological process in chronic liver disease and cirrhosis. Recent studies have found a close association between intestinal microbiota and the development of liver fibrosis. To determine whether there are differences in the intestinal microbiota between rhesus macaques with liver fibrosis (MG) and normal rhesus macaques (MN), fecal samples were collected from 8 male MG and 12 male MN. The biological composition of the intestinal microbiota was then detected using 16S rRNA gene sequencing. The results revealed statistically significant differences in ASVs and Chao1 in the alpha-diversity and the beta-diversity of intestinal microbiota between MG and MN. Both groups shared Prevotella and Lactobacillus as common dominant microbiota. However, beneficial bacteria such as Lactobacillus were significantly less abundant in MG (P = 0.02). Predictive functional analysis using PICRUSt2 gene prediction revealed that MG exhibited a higher relative abundance of functions related to substance transport and metabolic pathways. This study may provide insight into further exploration of the mechanisms by which intestinal microbiota affect liver fibrosis and its potential future use in treating liver fibrosis.

Anti-thymocyte globulin combined with post-transplantation cyclophosphamide reduce graft-versus-host disease in hematopoietic stem cell transplantation for pediatric leukemia.

This retrospective analysis evaluated the use of anti-thymocyte globulin (ATG) with or without post-transplantation cyclophosphamide (PTCy) for graft-versus-host disease (GvHD) prophylaxis in children with acute leukemia undergoing hematopoietic stem cell transplantation (HSCT). The study included 57 children, with 35 in the ATG-PTCy group and 22 in the ATG group. While overall incidence of acute and chronic GvHD did not differ significantly between groups, the ATG-PTCy group had lower rates of grade II-IV acute GvHD (p = 0.013) and moderate-to-severe chronic GvHD (p = 0.001) compared to the ATG group. Importantly, ATG-PTCy significantly improved GvHD/relapse-free survival (GRFS) compared to ATG (65.71% vs. 36.63%; p = 0.003). There were no differences in engraftment, infection rates, immune reconstitution, overall survival, leukemia-free survival, relapse rate, or non-relapse mortality between the two groups. Combining ATG with PTCy may reduce moderate-to-severe GvHD and improve GRFS in children undergoing HSCT for acute leukemia.

Molecular epizootiology of porcine reproductive and respiratory syndrome virus in the Xinjiang Uygur Autonomous Region of China.

Rapid evolution of porcine reproductive and respiratory syndrome virus (PRRSV) is the bottleneck for effective prevention and control of PRRS. Thus, understanding the prevalence and genetic background of PRRSV strains in swine-producing regions is important for disease prevention and control. However, there is only limited information about the epizootiological situation of PRRS in the Xinjiang Uygur Autonomous Region, China. In this study, blood or lung tissue samples were collected from 1,411 PRRS-suspected weaned pigs from 9 pig farms in Changji, Shihezi, and Wujiaqu cities between 2020 and 2022. The samples were first tested by RT-quantitative PCR, yielding a PRRSV-2 positive rate of 53.6%. Subsequently, 36 PRRSV strains were isolated through initial adaptation in bone marrow-derived macrophages followed by propagation in grivet monkey Marc-145 cells. Furthermore, 28 PRRSV-positive samples and 20 cell-adapted viruses were selected for high-throughput sequencing (HTS) to obtain the entire PRRSV genome sequences. Phylogenetic analysis based on the nucleotide sequences of the ORF5 gene of the PRRSV strains identified in this study grouped into sub-lineages 1.8 and 8.7 the former being the dominant strain currently circulating in Xinjiang. However, the NSP2 proteins of the Xinjiang PRRSV strains shared the same deletion patterns as sub-lineage 1.8 prototype strain NADC30 with the exception of 4 strains carrying 2-3 additional amino acid deletions. Further analysis confirmed that recombination events had occurred in 27 of 37 PRRSVs obtained here with the parental strains belonging to sub-lineages 1.8 and 8.7, lineages 3 and 5, with the recombination events having occurred most frequently in the 5' and 3' termini of ORF1a and 5' terminus of ORF1b.

Enhancing Reversibility and Stability of Mg Metal Anodes: High-Exposure (002) Facets and Nanosheet Arrays for Superior Mg Plating/Stripping.

Magnesium metal batteries (MMBs), recognized as promising contenders for post-lithium battery technologies, face challenges such as uneven magnesium (Mg) plating and stripping behaviors, leading to uncontrollable dendrite growth and irreversible structural damage. Herein, we have developed a Mg foil featuring prominently exposed (002) facets and an architecture of nanosheet arrays (termed (002)-Mg), created through a one-step acid etching method. Specifically, the prominent exposure of Mg (002) facets, known for their inherently low surface and adsorption energies with Mg atoms, not only facilitates smooth nucleation and dense deposition but also significantly mitigates side reactions on the Mg anode. Moreover, the nanosheet arrays on the surface evenly distribute the electric field and Mg ion flux, enhancing Mg ion transfer kinetics. As a result, the fabricated (002)-Mg electrodes exhibit unprecedented long-cycle performance, lasting over 6000 h (>8 months) at a current density of 3 mA cm-2 for a capacity of 3 mAh cm-2. Furthermore, the corresponding pouch cells equipped with various electrolytes and cathodes demonstrate remarkable capacity and cycling stability, highlighting the superior electrochemical compatibility of the (002)-Mg electrode. This study provides new insights into the advancement of durable MMBs by modifying the crystal structure and morphology of Mg.

MMSSC-Net: multi-stage sequence cognitive networks for drug molecule recognition.

In the growing body of scientific literature, the structure and information of drugs are usually represented in two-dimensional vector graphics. Drug compound structures in vector graphics form are difficult to recognize and utilize by computers. Although the current OCSR paradigm has shown good performance, most existing work treats it as a single isolated whole. This paper proposes a multi-stage cognitive neural network model that predicts molecular vector graphics more finely. Based on cognitive methods, we construct a model for fine-grained perceptual representation of molecular images from bottom to top, and in stages, the primary representation of atoms and bonds is potential discrete label sequence (atom type, bond type, functional group, etc.). The second stage represents the molecular graph according to the label sequence, and the final stage evolves in an extensible manner from the molecular graph to a machine-readable sequence. Experimental results show that MMSSC-Net outperforms current advanced methods on multiple public datasets. It achieved an accuracy rate of 75-94% on cognitive recognition at different resolutions. MMSSC-Net uses a sequence cognitive method to make it more reliable in interpretability and transferability, and provides new ideas for drug information discovery and exploring the unknown chemical space.

Qualitative and Quantitative Analysis of Three-Dimensional Fluorescence Spectra by Improved Parallel Factor Analysis with Internal Standard Sample Embedding.

The strategy of parallel factor analysis, combined with the internal standard method, has been increasingly applied to the qualitative and quantitative analysis of three-dimensional fluorescence spectra of unknown mixed fluorophores. Nevertheless, the disparity in the number of fluorophores included in the internal standard sample set and the number included in test samples may impact the qualitative and quantitative outcomes of parallel factor analysis. In this work, we systematically established the framework of the parallel factor analysis with internal standard sample embedding (ISSE-PARAFAC) strategy. We applied this framework to six datasets representing two scenarios and a real dataset and conducted a detailed discussion on the effects of the disparity between the number of fluorophores in the internal standard sample set and the number in the test set on both qualitative and quantitative results. Additionally, we introduced an enhancement to PARAFAC by aggregating fluorophores with similar emission wavelengths, corresponding to the peaks of emission loadings (spectra) obtained from PARAFAC, as a single fluorophore. This aggregation aimed to mitigate the strong correlation between similar fluorophores. The results imply that the presence of irrelevant fluorophores in the internal standard sample set, whether increased or decreased, does not significantly affect the qualitative and quantitative analysis of target fluorophores in the test set. Moreover, we demonstrated that the improved parallel factor analysis with internal standard sample embedding not only fully decomposes the uncorrelated mixed fluorophores for qualitative analysis but also allows the established linear concentration model for fluorescent components to predict the corresponding fluorophore concentration of test samples, enabling quantitative analysis at the ppm level (mg/L).

A Fully Automated Online Enrichment and Separation System for Highly Reproducible and In-Depth Analysis of Intact Glycopeptide.

A fully automated online enrichment and separation system for intact glycopeptides, named AutoGP, was developed in this study by integrating three different columns in a nano-LC system. Specifically, the peptide mixture from the enzymatic digestion of a complex biological sample was first loaded on a hydrophilic interaction chromatography (HILIC) column. The nonglycopeptides in the sample were washed off the column, and the glycopeptides retained by the HILIC column were eluted to a C18 trap column to achieve an automated glycopeptide enrichment. The enriched glycopeptides were further eluted to a C18 column for separation, and the separated glycopeptides were eventually analyzed by using an orbitrap mass spectrometer (MS). The optimal operating conditions for AutoGP were systemically studied, and the performance of the fully optimized AutoGP was compared with a conventional manual system used for glycopeptide analysis. The experimental evaluation shows that the total number of glycopeptides identified is at least 1.5-fold higher, and the median coefficient of variation for the analyses is at least 50% lower by using AutoGP, as compared to the results acquired by using the manual system. In addition, AutoGP can perform effective analysis even with a 1-µg sample amount, while a 10-µg sample at least will be needed by the manual system, implying an order of magnitude better sensitivity of AutoGP. All the experimental results have consistently proven that AutoGP can be used for much better characterization of intact glycopeptides.

Simulation study of a new racetrack FAIMS analyzer to achieve both high-resolution and high-sensitivity.

A new racetrack field-asymmetric waveform ion mobility spectrometry (r-FAIMS) analyzer was developed in this study by combining the existing planar FAIMS (p-FAIMS) and cylindrical FAIMS (c-FAIMS). The ion inlet and outlet regions of r-FAIMS were consisted of a half of c-FAIMS, respectively, and these c-FAIMS were further connected by two p-FAIMS to form a racetrack shaped FAIMS. With such FAIMS working electrode configuration, the ions entering the r-FAIMS can be focused and separated in the first c-FAIMS section, be further separated in the p-FAIMS section with high-resolution, be focused and separated again in the final c-FAIMS section and eventually enter the mass spectrometer or other analyzers for analysis. Detailed simulation by using SIMION software with the default FAIMS user program showed that the ion focusing effect in the first c-FAIMS section ensures the ions entering the following p-FAIMS section as a compact ion packet. This effectively decreases the ion loss caused by Coulomb repulsion and thermal diffusion in p-FAIMS section as compared to the ions being introduced into the p-FAIMS gap randomly in the conventional design. As a result, the ion transmission efficiency of r-FAIMS is at least 3.3-fold higher than the single p-FAIMS under the operating conditions used in this study. The ion trajectory simulation results also showed that the resolving power of r-FAIMS is about the sum of the resolving powers for its c-FAIMS and p-FAIMS sections. The resolving power of r-FAIMS is at least 3.6-fold higher than the single c-FAIMS under the operation conditions used in this study. Therefore, the r-FAIMS can realize both high-resolution and high-sensitive ion mobility separation.

Using blood routine indicators to establish a machine learning model for predicting liver fibrosis in patients with Schistosoma japonicum.

This study intends to use the basic information and blood routine of schistosomiasis patients to establish a machine learning model for predicting liver fibrosis. We collected medical records of Schistosoma japonicum patients admitted to a hospital in China from June 2019 to June 2022. The method was to screen out the key variables and six different machine learning algorithms were used to establish prediction models. Finally, the optimal model was compared based on AUC, specificity, sensitivity and other indicators for further modeling. The interpretation of the model was shown by using the SHAP package. A total of 1049 patients' medical records were collected, and 10 key variables were screened for modeling using lasso method, including red cell distribution width-standard deviation (RDW-SD), Mean corpuscular hemoglobin concentration (MCHC), Mean corpuscular volume (MCV), hematocrit (HCT), Red blood cells, Eosinophils, Monocytes, Lymphocytes, Neutrophils, Age. Among the 6 different machine learning algorithms, LightGBM performed the best, and its AUCs in the training set and validation set were 1 and 0.818, respectively. This study established a machine learning model for predicting liver fibrosis in patients with Schistosoma japonicum. The model could help improve the early diagnosis and provide early intervention for schistosomiasis patients with liver fibrosis.

Calcitonin gene­related peptide alleviates hyperoxia­induced human alveolar cell injury via the CGRPR/TRPV1/Ca2+ axis.

Although exogenous calcitonin gene­related peptide (CGRP) protects against hyperoxia­induced lung injury (HILI), the underlying mechanisms remain unclear. The present study attempted to elucidate the molecular mechanism by which CGRP protects against hyperoxia­induced alveolar cell injury. Human alveolar A549 cells were treated with 95% hyperoxia to establish a hyperoxic cell injury model. ELISA was performed to detect the CGRP secretion. Immunofluorescence, quantitative (q)PCR, and western blotting were used to detect the expression and localization of CGRP receptor (CGRPR) and transient receptor potential vanilloid 1 (TRPV1). Cell counting kit­8 and flow cytometry were used to examine the proliferation and apoptosis of treated cells. Digital calcium imaging and patch clamp were used to analyze the changes in intracellular Ca2+ signaling and membrane currents induced by CGRP in A549 cells. The mRNA and protein expression levels of Cyclin D1, proliferating cell nuclear antigen (PCNA), Bcl­2 and Bax were detected by qPCR and western blotting. The expression levels of CGRPR and TRPV1 in A549 cells were significantly downregulated by hyperoxic treatment, but there was no significant difference in CGRP release between cells cultured under normal air and hyperoxic conditions. CGRP promoted cell proliferation and inhibited apoptosis in hyperoxia, but selective inhibitors of CGRPR and TRPV1 channels could effectively attenuate these effects; TRPV1 knockdown also attenuated this effect. CGRP induced Ca2+ entry via the TRPV1 channels and enhanced the membrane non­selective currents through TRPV1 channels. The CGRP­induced increase in intracellular Ca2+ was reduced by inhibiting the phospholipase C (PLC)/protein kinase C (PKC) pathway. Moreover, PLC and PKC inhibitors attenuated the effects of CGRP in promoting cell proliferation and inhibiting apoptosis. In conclusion, exogenous CGRP acted by inversely regulating the function of TRPV1 channels in alveolar cells. Importantly, CGRP protected alveolar cells from hyperoxia­induced injury via the CGRPR/TRPV1/Ca2+ axis, which may be a potential target for the prevention and treatment of the HILI.

Electron ionization mass spectrometry feature peak relationships combined with deep classification model to assist similarity algorithm for fast and accurate identification of compounds.

RATIONALE: Gas chromatography-mass spectrometry (GC-MS) combines chromatography and MS, providing full play to the advantages of high separation efficiency of GC, strong qualitative ability of MS, and high sensitivity of detector. In GC-MS data processing, determining the experimental compounds is one of the most important analytical steps, which is usually realized by one-to-one similarity calculations between the experimental mass spectrum and the standard mass spectrum library. Although the accuracy of the algorithm has been improved in recent years, it is still difficult to distinguish structurally similar mass spectra, especially isomers. At the same time, the library capacity is very large and increasing every year, and the algorithm needs to perform large numbers of calculations with irrelevant compounds in the library to recognize unknown compounds, which leads to a significant reduction in efficiency. METHODS: This work proposed to exclude a large number of irrelevant mass spectra by presearching, perform preliminary similarity calculations using similarity algorithms, and finally improve the accuracy of similarity calculations using deep classification models. The replica library of NIST17 is used as the query data, and the master library is used as the reference database. RESULTS: Compared with the traditional recognition algorithm, the preprocessing algorithm has reduced the time by 4.2 h, and by adding the deep learning models 1 and 2 as the final determination, the recognition accuracy has been improved by 1.9% and 6.5%, respectively, based on the original algorithm. CONCLUSIONS: This method improves the recognition efficiency compared to conventional algorithms and at the same time has better recognition accuracy for structurally similar mass spectra and isomers.

Metagenome-assembled genomes from enrichment cultures grown on xenobiotic solvents.

Microbes play a significant role in the cleanup of xenobiotic contaminants. Based on metagenomes derived from long-term enrichment cultures grown on xenobiotic solvents, we report 166 metagenome-assembled genomes, of which 137 are predicted to be more than 90% complete. These genomes broaden the representation of xenobiotic degraders.