Multi-source domain adaptive network based on local kernelized higher-order moment matching for rotating machinery fault diagnosis.

Unsupervised domain adaptation has been extensively researched in rotating-machinery cross-domain fault diagnosis. A multi-source domain adaptive network based on local kernelized higher-order moment matching is constructed in this research for rotating-machinery fault diagnosis. Firstly, a multi-branch network is designed to map each source-target pair to a domain-specific shared space and to extract domain-invariant features using domain adversarial thought. Then, a local kernelized higher-order moment matching algorithm is proposed to perform fine-grained matching in shared category subspace. Finally, a feature fusion strategy based on the local domain distribution deviation is applied to synthesize the output features of multiple classifiers to obtain diagnostic results. The experimental validation of two-branch and three-branch networks on two public datasets is carried out and average diagnostic accuracies both exceed 99%. The results demonstrate the effectiveness and superiority of the approach.

Hsa_circ_0004214 involved in the epithelial-mesenchymal transition induced by beryllium sulfate through modulating JAK-STAT signaling pathway.

Background: Chronic beryllium disease is characterized by granulomas and pulmonary fibrosis. Recent studies have shown that microRNAs (miRNAs) and circular RNAs (circRNAs) play critical roles in the pathogenesis and development of many diseases. However, the role of miRNAs and circRNAs in pulmonary fibrosis induced by beryllium sulfate (BeSO4) has not been elucidated. Methods: Previous studies demonstrated hsa-miR-663b was down-regulated in the 150 µmol/L BeSO4-treated 16HBE cells, while hsa_circ_ 0004214 was up-regulated. Here we found epithelial-mesenchymal transition (EMT) involved in pulmonary fibrosis induced by BeSO4 (4, 8, and 12 mg/kg·BW) in SD rats. Results: Elevated expression of hsa-miR-663b blocked the EMT progression of 16HBE cells induced by 150 µmol/L BeSO4. Notably, the overexpression of hsa-miR-663b decreased the expression of leukemia inhibitory factor (LIF), which was predicted as a target gene of hsa-miR-663b by bioinformatics tools. Furthermore, elevated miR-663b inhibited the activation of the downstream Janus kinase-signal transducers and activators of transcription (JAK-STAT) signaling pathway induced by BeSO4 in 16HBE cells. Previous study suggested that hsa_circ_0004214 had binding sites for hsa-miR-663b. The results indicated hsa_circ_0004214 alleviated the BeSO4-induced EMT via JAK-STAT pathway in 16HBE cells. Conclusions: Collectively, the overexpression of hsa-miR-663b and knockdown of hsa_circ_0004214 attenuated the EMT induced by BeSO4 through the inhibition of JAK-STAT signaling pathway. The aberrant expressed hsa-miR-663b and hsa_circ_0004214 stimulated by BeSO4 may exert an important function in the toxic mechanism of beryllium exposure to 16HBE cells, providing the potential therapeutic targets in chronic beryllium disease.

Ensemble learning prediction framework for EGFR amplification status of glioma based on terahertz spectral features.

Epidermal growth factor receptor (EGFR) plays a pivotal role in the initiation and progression of gliomas. In particular, in glioblastoma, EGFR amplification emerges as a catalyst for invasion, proliferation, and resistance to radiotherapy and chemotherapy. Current approaches are not capable of providing rapid diagnostic results of molecular pathology. In this study, we propose a terahertz spectroscopic approach for predicting the EGFR amplification status of gliomas for the first time. A machine learning model was constructed using the terahertz response of the measured glioma tissues, including the absorption coefficient, refractive index, and dielectric loss tangent. The novelty of our model is the integration of three classical base classifiers, i.e., support vector machine, random forest, and extreme gradient boosting. The ensemble learning method combines the advantages of various base classifiers, this model has more generalization ability. The effectiveness of the proposed method was validated by applying an individual test set. The optimal performance of the integrated algorithm was verified with an area under the curve (AUC) maximum of 85.8 %. This signifies a significant stride toward more effective and rapid diagnostic tools for guiding postoperative therapy in gliomas.

DECIDE: A decoupled semantic and boundary learning network for precise osteosarcoma segmentation by integrating multi-modality MRI.

Automated Osteosarcoma Segmentation in Multi-modality MRI (AOSMM) holds clinical significance for effective tumor evaluation and treatment planning. However, the precision of AOSMM is challenged by the diverse characteristics of multi-modality MRI and the inherent heterogeneity and boundary ambiguity of osteosarcoma. While numerous methods have made significant strides in automated osteosarcoma segmentation, they primarily focused on the use of a single MRI modality and overlooked the potential benefits of integrating complementary information from other MRI modalities. Furthermore, they did not adequately model the long-range dependencies of complex tumor features, which may lead to insufficiently discriminative feature representations. To this end, we propose a decoupled semantic and boundary learning network (DECIDE) to achieve precise AOSMM with three functional modules. The Multi-modality Feature Fusion and Recalibration (MFR) module adaptively fuses and recalibrates multi-modality features by exploiting their channel-wise dependencies to compute low-rank attention weights for effectively aggregating useful information from different MRI modalities, which promotes complementary learning between multi-modality MRI and enables a more comprehensive tumor characterization. The Lesion Attention Enhancement (LAE) module employs spatial and channel attention mechanisms to capture global contextual dependencies over local features, significantly enhancing the discriminability and representational capacity of intricate tumor features. The Boundary Context Aggregation (BCA) module further enhances semantic representations by utilizing boundary information for effective context aggregation while also ensuring intra-class consistency in cases of boundary ambiguity. Substantial experiments demonstrate that DECIDE achieves exceptional performance in osteosarcoma segmentation, surpassing state-of-the-art methods in terms of accuracy and stability.

Genome comparisons reveal accessory genes crucial for the evolution of apple Glomerella leaf spot pathogenicity in Colletotrichum fungi.

Apple Glomerella leaf spot (GLS) is an emerging fungal disease caused by Colletotrichum fructicola and other Colletotrichum species. These species are polyphyletic and it is currently unknown how these pathogens convergently evolved to infect apple. We generated chromosome-level genome assemblies of a GLS-adapted isolate and a non-adapted isolate in C. fructicola using long-read sequencing. Additionally, we resequenced 17 C. fructicola and C. aenigma isolates varying in GLS pathogenicity using short-read sequencing. Genome comparisons revealed a conserved bipartite genome architecture involving minichromosomes (accessory chromosomes) shared by C. fructicola and other closely related species within the C. gloeosporioides species complex. Moreover, two repeat-rich genomic regions (1.61 Mb in total) were specifically conserved among GLS-pathogenic isolates in C. fructicola and C. aenigma. Single-gene deletion of 10 accessory genes within the GLS-specific regions of C. fructicola identified three that were essential for GLS pathogenicity. These genes encoded a putative non-ribosomal peptide synthetase, a flavin-binding monooxygenase and a small protein with unknown function. These results highlight the crucial role accessory genes play in the evolution of Colletotrichum pathogenicity and imply the significance of an unidentified secondary metabolite in GLS pathogenesis.

Portable tri-color ratiometric fluorescence paper sensor for intelligent visual detection of dual-antibiotics and aluminium ion.

The toxicological effect between co-existed antibiotics and metal ions was dangerous to the ecological environment and public health. However, the rapid quantification tools with convenience, accuracy and low cost for the detection of multiple targets were still challenging. Herein, a portable tri-color ratiometric fluorescence paper sensor was constructed by coupling of blue carbon dots and fluorescence imprinted polymer for down/up conversion simultaneous detection of tetracycline and sulfamethazine. Interestingly, the cascade detection of aluminum ion was also realized based on the individual detection system of tetracycline without the assistance of complex coupling reagents. The detection limits of smartphone method for the visual detection of tetracycline, sulfamethazine and aluminum ion were calculated as 0.014 µM, 0.004 µM and 0.019 µM, respectively. The portable fluorescence paper sensor was applied for the visual detection of tetracycline, sulfamethazine and aluminum ion in actual samples successfully with satisfactory recoveries. With the advantages of rapidness, low cost, and portability, the developed portable fluorescence paper sensor provided a new strategy for the visual real-time detection of multiple targets.

mTOR signaling promotes rapid m6A mRNA methylation to regulate NK-cell activation and effector functions.

Natural killer (NK) cells can be rapidly activated in response to cytokines during host defense against malignant cells or viral infection. However, it remains unclear what mechanisms precisely and rapidly regulate the expression of the numerous genes involved in activating NK cells. In this study, we discovered that NK-cell N6-methyladenosine (m6A) methylation levels were rapidly upregulated upon short-term NK-cell activation and were repressed in the tumor microenvironment. Deficiency of methyltransferase-like 3 (METTL3) or METTL14 moderately influenced NK-cell homeostasis, while double knockout of METTL3/14 significantly impacted NK-cell homeostasis, maturation, and antitumor immunity. This suggests a cooperative role of METTL3 and METTL14 in regulating NK-cell development and effector functions. Using methylated RNA immunoprecipitation sequencing (MeRIP-seq), we demonstrated that genes involved in NK-cell effector functions, such as Prf1 and Gzmb, were directly modified by m6A methylation. Furthermore, inhibiting mTOR complex 1 (mTORC1) activation prevented m6A methylation levels from increasing when NK cells were activated, and this could be restored by S-adenosylmethionine (SAM) supplementation. Collectively, we have unraveled crucial roles for rapid m6A mRNA methylation downstream of the mTORC1-SAM signal axis in regulating NK-cell activation and effector functions.

Comparative Proteomics Elucidates the Potential Mechanism of Sperm Capacitation of Chinese Mitten Crabs (Eriocheir sinensis).

Sperm capacitation is broadly defined as a suite of biochemical and biophysical changes resulting from the acquisition of fertilization ability. To gain insights into the regulation mechanism of crustacean sperm capacitation, 4D label-free quantitative proteomics was first applied to analyze the changes of sperm in Eriocheir sinensis under three sequential physiological conditions: seminal vesicles (X2), hatched with the seminal receptacle content (X3), and incubated with egg water (X5). In total, 1536 proteins were identified, among which 880 proteins were quantified, with 82 and 224 proteins significantly altered after incubation with the seminal receptacle contents and egg water. Most differentially expressed proteins were attributed to biological processes by Gene Ontology annotation analysis. As the fundamental bioenergetic metabolism of sperm, the oxidative phosphorylation, glycolysis, and the pentose phosphate pathway presented significant changes under the treatment of seminal receptacle contents, indicating intensive regulation for sperm in the seminal receptacle. Additionally, the seminal receptacle contents also significantly increased the oxidation level of sperm, whereas the enhancement of abundance in superoxide dismutase, peroxiredoxin 1, and glutathione S-transferase after incubation with egg water significantly improved the resistance against oxidation. These results provided a new perspective for reproduction studies in crustaceans.

PFD-Net: Pyramid Fourier Deformable Network for medical image segmentation.

Medical image segmentation is crucial for accurately locating lesion regions and assisting doctors in diagnosis. However, most existing methods fail to effectively utilize both local details and global semantic information in medical image segmentation, resulting in the inability to effectively capture fine-grained content such as small targets and irregular boundaries. To address this issue, we propose a novel Pyramid Fourier Deformable Network (PFD-Net) for medical image segmentation, which leverages the strengths of CNN and Transformer. The PFD-Net first utilizes PVTv2-based Transformer as the primary encoder to capture global information and further enhances both local and global feature representations with the Fast Fourier Convolution Residual (FFCR) module. Moreover, PFD-Net further proposes the Dilated Deformable Refinement (DDR) module to enhance the model's capacity to comprehend global semantic structures of shape-diverse targets and their irregular boundaries. Lastly, Cross-Level Fusion Block with deformable convolution (CLFB) is proposed to combine the decoded feature maps from the final Residual Decoder Block (DDR) with local features from the CNN auxiliary encoder branch, improving the network's ability to perceive targets resembling the surrounding structures. Extensive experiments were conducted on nine publicly medical image datasets for five types of segmentation tasks including polyp, abdominal, cardiac, gland cells and nuclei. The qualitative and quantitative results demonstrate that PFD-Net outperforms existing state-of-the-art methods in various evaluation metrics, and achieves the highest performance of mDice with the value of 0.826 on the most challenging dataset (ETIS), which is 1.8% improvement compared to the previous best-performing HSNet and 3.6% improvement compared to the next-best PVT-CASCADE. Codes are available at https://github.com/ChaorongYang/PFD-Net.

Social defeat stress induces liver injury by modulating endoplasmic reticulum stress in C57BL/6J mice.

Social defeat stress is associated with endoplasmic reticulum (ER) stress, inflammation and apoptosis. ER stress is thought to contribute to many lifestyle diseases such as liver injury, cardiovascular dysfunction and depression. We investigated the expression of the ER stress markers RNA-dependent protein kinase-like ER kinase (PERK), eukaryotic translation initiation factor 2α (eIF2α) and C/EBP homologous protein (CHOP), as well as inflammatory and apoptotic factors, to assess how social defeat stress induces liver injury. Furthermore, we evaluated the effects of the ER stress inhibitor phenylbutyric acid (PBA) and ER stress inducer thapsigargin (TG) on liver injury. Adult mice were divided into the control, social defeat, social defeat + PBA, TG, PBA and TG + PBA groups. The social defeat and social defeat + PBA groups were simultaneously exposed to social defeat stress for 10 days. The social defeat + PBA, TG, PBA and TG + PBA groups were treated with PBA or TG via intraperitoneal injections. PBA was injected 1 h before the TG injection into the TG + PBA group. Liver samples from six groups of mice were analyzed by histological analysis and western blotting. Social defeat stress promoted ER stress, increased the expression of inflammatory factors and induced apoptosis in the liver of socially defeated mice, which was reversed by PBA. Moreover, ER stress induces TG-induced liver injury by initiating ER stress. Social defeat stress initiates ER stress, promotes the expression of inflammatory and apoptotic factors, and induces liver injury. PBA suppresses liver injury caused by social defeat stress and TG treatment.

Bioaccumulation, transfer characteristics of metals in six vascular plants, and soil pollution assessment from Wachangping karst bauxite residue areas.

Bauxite residue (BR) is a large volume by-product generated during bauxite smelting process and metal pollution problem is becoming increasingly prominent in residue areas. Accumulation and transfer of metals in six vascular plants were analyzed and soil environment was evaluated. Results found levels of Al (2,110-26,280 mg kg-1), Fe (990 to 9,880 mg kg-1), Ca (8,020 to 49,250 mg kg-1), Mg (2,060 to 17,190 mg kg-1), K (16,840 to 39,670 mg kg-1), and Ti (80 to 1,240 mg kg-1) in plants. Metal concentrations in soils exceeded background levels. Bioconcentration factor (BCF) found that Al, Fe, and Ti in plants (roots, stems, and leaves) were relatively depleted (BCF <1). Transfer factor (TF) of Al, Fe, Ca, K, and Ti in plants was distinctly higher than 1 and mainly concentrated in stems and leaves. Pollution indices revealed that soil environment was at moderated to serious contaminated risk. Principal components analysis (PCA) showed that Artemisia caruifolia Buch. and Siegesbeckia orientalis L. plants had a good ability to absorb Al and Fe, which can be used as biological indicators and restoration materials.Novelty statementCurrently, soil environment was exposed to moderated to serious contaminated risk from Wachangping karst bauxite residue areas.Bioconcentration factor (BCF) analysis found that Al, Fe, and Ti in six vascular plants (roots, stems, and leaves) were relatively depleted (BCF <1).Transfer factor (TF) of Al, Fe, Ca, K, and Ti in vascular plants was distinctly higher than 1, which mainly concentrated in stems and leaves.PCA revealed that Artemisia caruifolia Buch. and Siegesbeckia orientalis L. plants had a good ability to absorb Al and Fe, which can be used as biological indicators and ecological restoration materials.

Catalytic properties characterization and degradation mode elucidation of a polyG-specific alginate lyase OUC-FaAly7.

Polymerized guluronates (polyG)-specific alginate lyase with lower polymerized mannuronates (polyM)-degrading activity, superior stability, and clear action mode is a powerful biotechnology tool for the preparation of AOSs rich in M blocks. In this study, we expressed and characterized a polyG-specific alginate lyase OUC-FaAly7 from Formosa agariphila KMM3901. OUC-FaAly7 belonging to polysaccharide lyase (PL) family 7 had highest activity (2743.7 ± 20.3 U/µmol) at 45 °C and pH 6.0. Surprisingly, its specific activity against polyG reached 8560.2 ± 76.7 U/µmol, whereas its polyM-degrading activity was nearly 0 within 10 min reaction. Suggesting that OUC-FaAly7 was a strict polyG-specific alginate lyase. Importantly, OUC-FaAly7 showed a wide range of temperature adaptations and remarkable temperature and pH stability. Its relative activity between 20 °C and 45 °C reached >90 % of the maximum activity. The minimum identifiable substrate of OUC-FaAly7 was guluronate tetrasaccharide (G4). Action process and mode showed that it was a novel alginate lyase digesting guluronate hexaose (G6), guluronate heptaose (G7), and polymerized guluronates, with the preferential generation of unsaturated guluronate pentasaccharide (UG5), although which could be further degraded into unsaturated guluronate disaccharide (UG3) and trisaccharide (UG2). This study contributes to illustrating the catalytic properties, substrate recognition, and action mode of novel polyG-specific alginate lyases.

Protein-protein interactions regulating α-synuclein pathology.

Parkinson's disease (PD) is a neurodegenerative disease characterized by the degeneration of dopaminergic neurons in the substantia nigra pars compacta (SNpc) and the formation of Lewy bodies (LBs). The main proteinaceous component of LBs is aggregated α-synuclein (α-syn). However, the mechanisms underlying α-syn aggregation are not yet fully understood. Converging lines of evidence indicate that, under certain pathological conditions, various proteins can interact with α-syn and regulate its aggregation. Understanding these protein-protein interactions is crucial for unraveling the molecular mechanisms contributing to PD pathogenesis. In this review we provide an overview of the current knowledge on protein-protein interactions that regulate α-syn aggregation. Additionally, we briefly summarize the methods used to investigate the influence of protein-protein interactions on α-syn aggregation and propagation.

Removal of potentially toxic elements from water with the moss-tufa micro-filtration system.

Mosses are an integral component in the tufa sedimentary landscape. In this study, we investigated the use of the porous moss-tufa structure as a filtration system for removing potentially toxic elements (PTEs) from water samples. Three species of mosses that commonly grow on tufa were selected, and the PTEs filtered by the moss-tufa system were identified by inductively coupled plasma mass spectrometry. The bioconcentration factor (BCF) of mosses was calculated to compare the enrichment effects of different mosses on PTEs. Likewise, the level of PTEs flowing through the moss-tufa system was measured, and the water quality removal rate (C) was calculated accordingly. The results revealed that the moss-tufa system was mainly composed of Fissidens grandifrons Brid., Hydrogonium dixonianum P. C. Chen, and Cratoneuron filicinum (Hedw.) Spruce var. filicinum. Among these, Fissidens grandifrons Brid. reported the highest retention capacity for PTEs. Collectively, the moss-tufa filtration system displayed a strong retention capacity and removal rate of Mn, Pb, and Ni from the water sample. The removal of PTEs by the moss-tufa system was mainly based on the enrichment of mosses and the adsorption-retention ability of tufa. In conclusion, the moss-tufa micro-filtration system displayed the effective removal of PTEs from water samples and could be applied to control the levels of toxic elements in karst water bodies.

Association between Shift Work and Health Outcomes in the General Population in China: A Cross-Sectional Study.

Shift work may adversely affect individuals' health, thus, the current study aimed to investigate the association between shift work and health outcomes in the general population. A total of 41,061 participants were included in this online cross-sectional survey, among which 9612 (23.4%) individuals engaged in shift work and 31,449 (76.6%) individuals engaged in non-shift work. Multiple logistic regression analyses were conducted to explore the association between shift work and health outcomes (psychiatric disorders, mental health symptoms, and physical disorders). In addition, associations between the duration (≤1 year, 1-3 years, 3-5 years, 5-10 years, ≥10 years) and frequency of shift work (<1 or ≥1 night/week) and health outcomes were also explored. The results showed that compared to non-shift workers, shift workers had a higher likelihood of any psychiatric disorders (odds ratios [OR] = 1.80, 95% CI = 1.56-2.09, p < 0.001), mental health symptoms (OR = 1.76, 95% CI = 1.68-1.85, p < 0.001), and physical disorders (OR = 1.48, 95% CI = 1.39-1.57, p < 0.001). In addition, inverted U-shaped associations were observed between the duration of shift work and health outcomes. These results indicated that shift work was closely related to potential links with poor health outcomes. The findings highlighted the importance of paying attention to the health conditions of shift workers and the necessity of implementing comprehensive protective measures for shift workers to reduce the impact of shift work.

Mitochondrial DNA Haplogroups and SNPs: Risk Factors in Multiple Cancers Based on a Cross-Tumor Analysis in Chinese Population.

BACKGROUND: Mitochondrial DNA's (mtDNA) haplogroups and SNPs were associated with the risk of different cancer. However, there is no evidence that the same haplogroup or mitochondrial SNP (mtSNP) exhibits the pleiotropic effect on multiple cancers. METHODS: We recruited 2,489 participants, including patients with colorectal, hepatocellular, lung, ovarian, bladder, breast, pancreatic, and renal cell carcinoma. In addition, 715 healthy individuals from Northern China served as controls. Next, cross-tumor analysis was performed to determine whether mtDNA variation is associated with multiple cancers. RESULTS: Our results revealed a significant decrease in the occurrence risk of multiple cancers among individuals belonging to haplogroup A [OR = 0.553, 95% confidence interval (CI) = 0.375-0.815, P = 0.003]. Furthermore, we identified 11 mtSNPs associated with multiple cancers and divided the population into high-risk and low-risk groups. Low-risk groups showed a significantly reduced risk of occurrence compared with high-risk groups (OR = 0.614, 95% CI = 0.507-0.744, P < 0.001). Furthermore, using interaction analysis, we identified a special group of individuals belonging to haplogroup A/M7 and the low-risk population, who exhibit a lower risk of multiple cancers compared with other populations (OR = 0.195, 95% CI = 0.106-0.359, P < 0.001). Finally, gene set enrichment analysis confirmed that haplogroup A/M7 patients had lower expression levels of cancer-related pathway genes compared with haplogroup D patients. CONCLUSIONS: We found that specific mtDNA haplogroups and mtSNPs may play a role in predicting multiple cancer predisposition in Chinese populations. IMPACT: This may provide a potential tool for early screening in clinical settings for individuals in the Chinese population.

OXTR polymorphisms associated with severity and treatment responses of schizophrenia.

The mechanisms generating specific symptoms of schizophrenia remain unclear and genetic research makes it possible to explore these issues at a fundamental level. Taking into account the associations between the oxytocin system and social functions, which are apparently impaired in schizophrenia patients, we hypothesized that the oxytocin receptor gene (OXTR) might be associated with schizophrenia symptoms in both severity and responses to antipsychotics and did this exploratory positional study. A total of 2363 patients with schizophrenia (1181 males and 1182 females) included in our study were randomly allocated to seven antipsychotic treatment groups and received antipsychotic monotherapy for 6 weeks. Their blood DNA was genotyped for OXTR polymorphisms. Their symptom severity was assessed by Positive and Negative Syndrome Scale (PANSS), and the scores were transformed into seven factors (positive, disorganized, negative symptoms apathy/avolition, negative symptoms deficit of expression, hostility, anxiety and depression). Percentage changes in PANSS scores from baseline to week 6 were calculated to quantify antipsychotic responses. We found that OXTR polymorphisms were nominally associated with the severity of overall symptoms (rs237899, ß = 1.669, p = 0.019), hostility symptoms (rs237899, ß = 0.427, p = 0.044) and anxiety symptoms (rs13316193, ß = -0.197, p = 0.038). As for treatment responses, OXTR polymorphisms were nominally associated with the improvement in negative symptoms apathy/avolition (rs2268490, ß = 2.235, p = 0.0499). No association between severity or response to treatment and OXTR polymorphisms was found with statistical correction for multiplicity. Overall, our results highlighted the possibility of nominally significant associations of the OXTR gene with the severity and improvement in schizophrenia symptoms. Given the exploratory nature of this study, these associations are indicative of the role of the OXTR gene in the pathology of schizophrenia and may contribute to further elucidate the mechanism of specific symptoms of schizophrenia and to exploit antipsychotics more effective to specific symptoms.

Preparation of rutin imprinted monolith (RIM) by using porogen containing ion liquid [BMIM]PF6 and its molecular recognition.

A [BMIM]PF6 ion liquid (IL)-assisted synthesis of a rutin imprinted monolith (RIM) was carried out in an in-situ polymerization method. Bi-functional monomers and a ternary porogen containing IL was used for the RIM preparation and a L9(33) orthogonal factor design performed. Scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FT-IR) and N2 adsorption method was for structural characterization of the RIMs. The monolith was directly used as stationary phase in liquid chromatography to test the retention selectivity, adsorption capacity and extraction application. The optimized porogen consists of 900 µL DMF, 144 µL ACN and 216 µL IL. The monolith RIM-13 obtained under the optimized conditions possessed improved adsorption performance, with a dynamic adsorption capacity of 6.695 mg/g, an imprinting efficiency of 4.841 and a selectivity α value of 4.821. Additionally, this monolith had also higher specific surface area, pore volume and permeability than that obtained without IL and the homogeneity of the imprint sites could be improved by using IL. When the RIM-13 was applied to the separation and purification of rutin from tartary buckwheat, a rutin product with a purity higher than 92 % can be obtained by one cycle. This molecular imprint solid-phase extraction (MI-SPE) is of potency to be applied to preparative-scale separation of other natural products.

Screening and identification of unknown chemical contaminants in food based on liquid chromatography-high-resolution mass spectrometry and machine learning.

Unknown or unexpected chemical contaminants and/or their transformation products in food that may be harmful to humans need to be discovered for comprehensive safety evaluation. Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is a powerful tool for detecting chemical contaminants in food samples. However, identifying all of peaks in LC-HRMS is not possible, but if class information is known in advance, further identification will become easier. In this work, a novel MS2 spectra classification-driven screening strategy was constructed based on LC-HRMS and machine learning. First, the classification model was developed based on machine learning algorithm using class information and experimental MS2 data of chemical contaminants and other non-contaminants. By using the developed artificial neural network classification model, in total 32 classes of pesticides, veterinary drugs and mycotoxins were classified with good prediction accuracy and low false-positive rate. Based on the classification model, a screening procedure was developed in which the classes of unknown features in LC-HRMS were first predicted through the classification model, and then their structures were identified under the guidance of class information. Finally, the developed strategy was tentatively applied to the analysis of pork and aquatic products, and 8 chemical contaminants and 11 transformation products belonging to 8 classes were found. This strategy enables screening of unknown chemical contaminants and transformation products in complex food matrices.