Dye-doped cellulose nanocrystals as novel dusting powders for visualizing latent fingerprints.

Powder-dusting method based on the visual contrast between the background surface and powder-covered ridges of a fingerprint is widely used to develop the invisible latent fingerprints (LFPs) left at crime scenes. Recently, the development of nano-sized powders with excellent optical performances has been extensively explored. In this work, we employed environmentally friendly and low-toxicity cellulose nanocrystals as the novel support. Using dye-doped cellulose nanocrystals as novel dusting powders, two dyes (phenylfluorone and curcumin) were adsorbed on the cellulose nanocrystals by a simple batch adsorption method. The dye-doped cellulose nanocrystals (namely, phenylfluorone-doped cellulose nanocrystals (PDCN) and curcumin-doped cellulose nanocrystals (CDCN)) containing 2% of the loaded mass of both the dyes with bright green fluorescence were developed to visualize LFPs on the surfaces of various substrates (such as glass slide, printing paper, orange plastic card, tile, stainless steel, compact disc, red plastic packing, copper foil and aluminum foil). Images of the LFPs can been obtained by both the dye-doped cellulose nanocrystals with sufficient affinity to the ridges of LFPs. High-quality ridge details with features at the second and third level can be detected by CDCN, whereas PDCN only display the secondary-level features of ridge details. Compared with PDCN, CDCN illustrate higher sensitivity, higher selectivity, and better contrast, especially for detecting fresh and non-fresh LFPs on porous and non-porous substrates, and has the potential for practical use in forensic science.

Green fabrication of modified lignin/zeolite/chitosan-based composite membranes for preservation of perishable foods.

Chitosan, as a kind of naturally occurring green and degradable material for the preservation of perishable foods, was investigated in this study with the objective of enhancing its preservation performances. Herein, lignin was modified using the solvent fractionation method (modified lignin, ML, including ML1-ML3), while natural clinoptilolite zeolite was modified using the alkali modification method (modified clinoptilolite zeolite, MCZ, including MCZ1-MCZ5). After optimizing the conditions, it was discovered that incorporating both ML3 and MCZ3 into pure chitosan-based membranes might be conducive to fabricate chitosan-based composite membranes for the preservation of perishable foods. As-prepared composite membranes possessed better visible light transmittance, antioxidant activity, and carbon dioxide/oxygen selectivity, resulting in improved preservation effects on the model perishable foods such as bananas, cherry tomatoes, and cheeses. These findings might indicate promising applications for chitosan-based composite membranes with modified lignin and zeolite in the field of eco-friendly degradable materials for the preservation of perishable foods.

Exposure to O3 and NO2 on the interfacial chemistry of the pulmonary surfactant and the mechanism of lung oxidative damage.

Exposure to ozone (O3) and nitrogen dioxide (NO2) are related to pulmonary dysfunctions and various lung diseases, but the underlying biochemical mechanisms remain uncertain. Herein, the effect of inhalable oxidizing gas pollutants on the pulmonary surfactant (PS, extracted from porcine lungs), a mixture of active lipids and proteins that plays an important role in maintaining normal respiratory mechanics, is investigated in terms of the interfacial chemistry using in-vitro experiments; and the oxidative stress induced by oxidizing gases in the simulated lung fluid (SLF) supplemented with the PS is explored. The results showed that O3 and NO2 individually increased the surface tension of the PS and reduced its foaming ability; this was accompanied by the surface pressure-area isotherms of the PS monolayers shifting toward lower molecular areas, with O3 exhibiting more severe effects than NO2. Moreover, both O3 and NO2 produced reactive oxygen species (ROS) resulting in lipid peroxidation and protein damage to the PS. The formation of superoxide radicals (O2•-) was correlated with the decomposition of O3 and the reactions of O3 and NO2 with antioxidants in the SLF. These radicals, in the presence of antioxidants, led to the formation of hydrogen peroxide and hydroxyl radicals (•OH). Additionally, the direct oxidation of unsaturated lipids by O3 and NO2 further caused an increase in the ROS content. This change in the ROS chemistry and increased •OH production tentatively explain how inhalable oxidizing gases lead to oxidative stress and adverse health effects. In summary, our results indicated that inhaled O3 and NO2 exposure can significantly alter the interfacial properties of the PS, oxidize its active ingredients, and induce ROS formation in the SLF. The results of this study provide a basis for the elucidation of the potential hazards of inhaled oxidizing gas pollutants in the human respiratory system.

Identifying Protein Phosphorylation Site-Disease Associations Based on Multi-Similarity Fusion and Negative Sample Selection by Convolutional Neural Network.

As one of the most important post-translational modifications (PTMs), protein phosphorylation plays a key role in a variety of biological processes. Many studies have shown that protein phosphorylation is associated with various human diseases. Therefore, identifying protein phosphorylation site-disease associations can help to elucidate the pathogenesis of disease and discover new drug targets. Networks of sequence similarity and Gaussian interaction profile kernel similarity were constructed for phosphorylation sites, as well as networks of disease semantic similarity, disease symptom similarity and Gaussian interaction profile kernel similarity were constructed for diseases. To effectively combine different phosphorylation sites and disease similarity information, random walk with restart algorithm was used to obtain the topology information of the network. Then, the diffusion component analysis method was utilized to obtain the comprehensive phosphorylation site similarity and disease similarity. Meanwhile, the reliable negative samples were screened based on the Euclidean distance method. Finally, a convolutional neural network (CNN) model was constructed to identify potential associations between phosphorylation sites and diseases. Based on tenfold cross-validation, the evaluation indicators were obtained including accuracy of 93.48%, specificity of 96.82%, sensitivity of 90.15%, precision of 96.62%, Matthew's correlation coefficient of 0.8719, area under the receiver operating characteristic curve of 0.9786 and area under the precision-recall curve of 0.9836. Additionally, most of the top 20 predicted disease-related phosphorylation sites (19/20 for Alzheimer's disease; 20/16 for neuroblastoma) were verified by literatures and databases. These results show that the proposed method has an outstanding prediction performance and a high practical value.

Research on surrogate model of dam numerical simulation with multiple outputs based on adaptive sampling.

Dam numerical simulation is an important method to research the dam structural behavior, but it often takes a lot of time for calculation when facing problems that require many simulations, such as structural parameter back analysis. The surrogate model is widely used as a technology to reduce computational cost. Although various methods have been widely investigated, there are still problems in designing the surrogate model's optimal Design of Experiments (DoE). In addition, most of the current DoE focuses on establishing a single-output problem. Designing a reasonable DoE for high-dimensional outputs is also a problem that needs to be solved. Based on the above issues, this research proposes a sequential surrogate model based on the radial basis function model (RBFM) with multi-outputs adaptive sampling. The benchmark function demonstrates the applicability of the proposed method to single-input & multi-outputs and multi-inputs & multi-outputs problems. Then, this method is applied to establishing a surrogate model for dam numerical simulation with multi-outputs. The result demonstrates that the proposed technique can be sampled adaptively and samples can be targeted based on the function form of the surrogate model, which significantly reduces the required sampling and calculation cost.

A Knowledge-Graph-Based Multimodal Deep Learning Framework for Identifying Drug-Drug Interactions.

The identification of drug-drug interactions (DDIs) plays a crucial role in various areas of drug development. In this study, a deep learning framework (KGCN_NFM) is presented to recognize DDIs using coupling knowledge graph convolutional networks (KGCNs) with neural factorization machines (NFMs). A KGCN is used to learn the embedding representation containing high-order structural information and semantic information in the knowledge graph (KG). The embedding and the Morgan molecular fingerprint of drugs are then used as input of NFMs to predict DDIs. The performance and effectiveness of the current method have been evaluated and confirmed based on the two real-world datasets with different sizes, and the results demonstrate that KGCN_NFM outperforms the state-of-the-art algorithms. Moreover, the identified interactions between topotecan and dantron by KGCN_NFM were validated through MTT assays, apoptosis experiments, cell cycle analysis, and molecular docking. Our study shows that the combination therapy of the two drugs exerts a synergistic anticancer effect, which provides an effective treatment strategy against lung carcinoma. These results reveal that KGCN_NFM is a valuable tool for integrating heterogeneous information to identify potential DDIs.

Lignin-based covalent organic polymers with improved crystallinity for non-targeted analysis of chemical hazards in food samples.

Lignin, the most abundant source of renewable aromatic compounds derived from natural lignocellulosic biomass, has great potential for various applications as green materials due to its abundant active groups. However, it is still challenging to quickly construct green polymers with a certain crystallinity by utilizing lignin as a building block. Herein, new green lignin-based covalent organic polymers (LIGOPD-COPs) were one-pot fabricated with water as the reaction solvent and natural lignin as the raw material. Furthermore, by using paraformaldehyde as a protector and modulator, the LIGOPD-COPs prepared under optimized conditions displayed better crystallinity than reported lignin-based polymers, demonstrating the feasibility of preparing lignin-based polymers with improved crystallinity. The improved crystallinity confers LIGOPD-COPs with enhanced application performance, which was demonstrated by their excellent performances in sample treatment of non-targeted food safety analysis. Under optimized conditions, phytochromes, the main interfering matrices, were almost completely removed from different phytochromes-rich vegetables by LIGOPD-COPs, accompanied by "full recovery" of 90 chemical hazards. Green, low-cost, and reusable properties, together with improved crystallinity, will accelerate the industrialization and marketization of lignin-based COPs, and promote their applications in many fields.

Identification of Potential Parkinson's Disease Drugs Based on Multi-Source Data Fusion and Convolutional Neural Network.

Parkinson's disease (PD) is a serious neurodegenerative disease. Most of the current treatment can only alleviate symptoms, but not stop the progress of the disease. Therefore, it is crucial to find medicines to completely cure PD. Finding new indications of existing drugs through drug repositioning can not only reduce risk and cost, but also improve research and development efficiently. A drug repurposing method was proposed to identify potential Parkinson's disease-related drugs based on multi-source data integration and convolutional neural network. Multi-source data were used to construct similarity networks, and topology information were utilized to characterize drugs and PD-associated proteins. Then, diffusion component analysis method was employed to reduce the feature dimension. Finally, a convolutional neural network model was constructed to identify potential associations between existing drugs and LProts (PD-associated proteins). Based on 10-fold cross-validation, the developed method achieved an accuracy of 91.57%, specificity of 87.24%, sensitivity of 95.27%, Matthews correlation coefficient of 0.8304, area under the receiver operating characteristic curve of 0.9731 and area under the precision-recall curve of 0.9727, respectively. Compared with the state-of-the-art approaches, the current method demonstrates superiority in some aspects, such as sensitivity, accuracy, robustness, etc. In addition, some of the predicted potential PD therapeutics through molecular docking further proved that they can exert their efficacy by acting on the known targets of PD, and may be potential PD therapeutic drugs for further experimental research. It is anticipated that the current method may be considered as a powerful tool for drug repurposing and pathological mechanism studies.

Identification of MiRNA-Disease Associations Based on Information of Multi-Module and Meta-Path.

Cumulative research reveals that microRNAs (miRNAs) are involved in many critical biological processes including cell proliferation, differentiation and apoptosis. It is of great significance to figure out the associations between miRNAs and human diseases that are the basis for finding biomarkers for diagnosis and targets for treatment. To overcome the time-consuming and labor-intensive problems faced by traditional experiments, a computational method was developed to identify potential associations between miRNAs and diseases based on the graph attention network (GAT) with different meta-path mode and support vector (SVM). Firstly, we constructed a multi-module heterogeneous network based on the meta-path and learned the latent features of different modules by GAT. Secondly, we found the average of the latent features with weight to obtain a final node representation. Finally, we characterized miRNA-disease-association pairs with the node representation and trained an SVM to recognize potential associations. Based on the five-fold cross-validation and benchmark datasets, the proposed method achieved an area under the precision-recall curve (AUPR) of 0.9379 and an area under the receiver-operating characteristic curve (AUC) of 0.9472. The results demonstrate that our method has an outstanding practical application performance and can provide a reference for the discovery of new biomarkers and therapeutic targets.

Highly Luminescent Nucleoside-Based N, P-Doped Carbon Dots for Sensitive Detection of Ions and Bioimaging.

The efficient detection of Fe3+ and MnO4 - in a water environment is very important and challenging due to their harmful effects on the health of humanity and environmental systems. Good biocompatibility, sensitivity, selectivity, and superior photophysical properties were important attributes of carbon dot-based CDs sensors for sensing applications. In this work, we synthesized N, P-co-doped carbon dots (N/P CDs) with guanosine 5'-monophosphate (GMP) as a green carbon source, with high fluorescence quantum yield in water (QY, 53.72%). First, the luminescent N/P CDs showed a three-state "on-off-on" fluorescence response upon the sequential addition of Fe3+ and F-, with a low detection limit of 12 nM for Fe3+ and 8.5 nM for F-, respectively. Second, the N/P CDs also exhibited desirable selectivity and sensitivity for toxic MnO4 - detection with the limit of detection of 18.2 nM, through a turn-off mechanism. Moreover, the luminescent N/P CDs successfully monitored the aforementioned ions in environmental water samples and in Escherichia coli.

Tandem Reactions for the Synthesis of High-Density Polycyclic Biofuels with a Double/Triple Hexane Ring.

Synthesizing high-density fuels from non-food biomass is of great interest in the field of biomass conversion because they can increase the loading capability and travel distance of vehicles and aircraft as compared to conventional low-density biofuels (<0.78 g/cm3). In this work, we reported a new and facile strategy for the synthesis of high-density biofuels with polycyclic structures from lignocellulose-derived 5,5-dimethyl-1,3-cyclohexanedione and different aldehyde derivatives in two steps under mild reaction conditions, including a developed tandem reaction and a hydrodeoxygenation reaction. Theoretical approaches were used to estimate the fuel properties, which indicate that the obtained biofuels have a high density of 0.78-0.88 g/cm3 and high net heat of combustion (NHOC) values of 44.0-46.0 MJ/kg. A representative biofuel 3c was measured to have a high NHOC of 43.4 MJ/kg, which matched well with the calculated NHOC value of 44.4 MJ/kg, indicating the high accuracy of the theoretical approaches. This work is expected to provide a green strategy for the synthesis of polycyclic high-density biofuels with platform chemicals.

Identification of Chemical-Disease Associations Through Integration of Molecular Fingerprint, Gene Ontology and Pathway Information.

The identification of chemical-disease association types is helpful not only to discovery lead compounds and study drug repositioning, but also to treat disease and decipher pathomechanism. It is very urgent to develop computational method for identifying potential chemical-disease association types, since wet methods are usually expensive, laborious and time-consuming. In this study, molecular fingerprint, gene ontology and pathway are utilized to characterize chemicals and diseases. A novel predictor is proposed to recognize potential chemical-disease associations at the first layer, and further distinguish whether their relationships belong to biomarker or therapeutic relations at the second layer. The prediction performance of current method is assessed using the benchmark dataset based on ten-fold cross-validation. The practical prediction accuracies of the first layer and the second layer are 78.47% and 72.07%, respectively. The recognition ability for lead compounds, new drug indications, potential and true chemical-disease association pairs has also been investigated and confirmed by constructing a variety of datasets and performing a series of experiments. It is anticipated that the current method can be considered as a powerful high-throughput virtual screening tool for drug researches and developments.

Dynamic Microwave-Assisted Micelle Extraction Coupled with Cloud Point Preconcentration for the Determination of Triazine Herbicides in Soil.

A green and simple method, dynamic microwave-assisted micelle extraction coupled with cloud point preconcentration, was developed for the determination of triazine herbicides in soil samples. The method has the advantages of those two extraction procedures, which could eliminate the interferences from complex soil samples greatly. Non-ionic surfactant Triton X-114 aqueous solution used as extraction solvent was continuously pumped into soil samples. The resulting extract was heated and centrifuged in the presence of NaCl. After centrifugation, the analytes were enriched into the surfactant-rich phase. No filtration or cleaning steps were required. Several key parameters were investigated. The Box-Behnken design was applied to optimize the experimental factors involved in the dynamic microwave-assisted micelle extraction. Good linearity was observed in the range of 1.00-250.00 µg kg-1. The limits of detection were ranged between 0.26 and 1.71 µg kg-1. The recoveries of analytes ranged from 80.3 to 98.3% with the relative standard deviations ranging from 1.1 to 6.6%.

Performance improvement for a 2D convolutional neural network by using SSC encoding on protein-protein interaction tasks.

BACKGROUND: The interactions of proteins are determined by their sequences and affect the regulation of the cell cycle, signal transduction and metabolism, which is of extraordinary significance to modern proteomics research. Despite advances in experimental technology, it is still expensive, laborious, and time-consuming to determine protein-protein interactions (PPIs), and there is a strong demand for effective bioinformatics approaches to identify potential PPIs. Considering the large amount of PPI data, a high-performance processor can be utilized to enhance the capability of the deep learning method and directly predict protein sequences. RESULTS: We propose the Sequence-Statistics-Content protein sequence encoding format (SSC) based on information extraction from the original sequence for further performance improvement of the convolutional neural network. The original protein sequences are encoded in the three-channel format by introducing statistical information (the second channel) and bigram encoding information (the third channel), which can increase the unique sequence features to enhance the performance of the deep learning model. On predicting protein-protein interaction tasks, the results using the 2D convolutional neural network (2D CNN) with the SSC encoding method are better than those of the 1D CNN with one hot encoding. The independent validation of new interactions from the HIPPIE database (version 2.1 published on July 18, 2017) and the validation of directly predicted results by applying a molecular docking tool indicate the effectiveness of the proposed protein encoding improvement in the CNN model. CONCLUSION: The proposed protein sequence encoding method is efficient at improving the capability of the CNN model on protein sequence-related tasks and may also be effective at enhancing the capability of other machine learning or deep learning methods. Prediction accuracy and molecular docking validation showed considerable improvement compared to the existing hot encoding method, indicating that the SSC encoding method may be useful for analyzing protein sequence-related tasks. The source code of the proposed methods is freely available for academic research at https://github.com/wangy496/SSC-format/ .

Novel Potential Small Molecule-MiRNA-Cancer Associations Prediction Model Based on Fingerprint, Sequence, and Clinical Symptoms.

As an important biomarker in organisms, miRNA is closely related to various small molecules and diseases. Research on small molecule-miRNA-cancer associations is helpful for the development of cancer treatment drugs and the discovery of pathogenesis. It is very urgent to develop theoretical methods for identifying potential small molecular-miRNA-cancer associations, because experimental approaches are usually time-consuming, laborious, and expensive. To overcome this problem, we developed a new computational method, in which features derived from structure, sequence, and symptoms were utilized to characterize small molecule, miRNA, and cancer, respectively. A feature vector was construct to characterize small molecule-miRNA-cancer association by concatenating these features, and a random forest algorithm was utilized to construct a model for recognizing potential association. Based on the 5-fold cross-validation and benchmark data set, the model achieved an accuracy of 93.20 ± 0.52%, a precision of 93.22 ± 0.51%, a recall of 93.20 ± 0.53%, and an F1-measure of 93.20 ± 0.52%. The areas under the receiver operating characteristic curve and precision recall curve were 0.9873 and 0.9870. The real prediction ability and application performance of the developed method have also been further evaluated and verified through an independent data set test and case study. Some potential small molecules and miRNAs related to cancer have been identified and are worthy of further experimental research. It is anticipated that our model could be regarded as a useful high-throughput virtual screening tool for drug research and development. All source codes can be downloaded from https://github.com/LeeKamlong/Multi-class-SMMCA.

Continuous-flow microwave-assisted extraction coupled with online single drop microextraction prior to GC-MS for determination of amide herbicides in rice samples.

A rapid method based on continuous-flow microwave-assisted extraction and online single drop microextraction was first developed and applied to the determination of amide herbicides in rice. The present method has the advantages of both continuous-flow microwave-assisted extraction and online single drop microextraction, which combines extraction, separation, preconcentration, and sample introduction in one step. By continuous-flow microwave-assisted extraction, analytes were first extracted from the rice samples using 15% methanol-water, and then concentrated into single drop. The microdrop was retracted into microsyringe and directly analyzed by gas chromatography with mass spectrometry without any filtration or clean-up process. The method greatly simplifies the sample treatment procedure, reduces consumption of toxic organic solvent, and extends the application of single drop microextraction to complex solid samples. Several parameters were optimized by Box-Behnken design. Under optimal experimental conditions, good linearity was observed in the range of 2.0-500.0 µg/kg. The limits of detection and quantification were in the range of 0.3-1.5 and 1.1-5.1 µg/kg, respectively. The intra- and inter-day precisions were between 1.9 and 4.8%. The present method was used to the analysis of real rice samples, and the recoveries of analytes were between 80.3 and 102.3% with the relative standard deviations ranging from 1.1 to 6.9%.

Seq-SymRF: a random forest model predicts potential miRNA-disease associations based on information of sequences and clinical symptoms.

Increasing evidence indicates that miRNAs play a vital role in biological processes and are closely related to various human diseases. Research on miRNA-disease associations is helpful not only for disease prevention, diagnosis and treatment, but also for new drug identification and lead compound discovery. A novel sequence- and symptom-based random forest algorithm model (Seq-SymRF) was developed to identify potential associations between miRNA and disease. Features derived from sequence information and clinical symptoms were utilized to characterize miRNA and disease, respectively. Moreover, the clustering method by calculating the Euclidean distance was adopted to construct reliable negative samples. Based on the fivefold cross-validation, Seq-SymRF achieved the accuracy of 98.00%, specificity of 99.43%, sensitivity of 96.58%, precision of 99.40% and Matthews correlation coefficient of 0.9604, respectively. The areas under the receiver operating characteristic curve and precision recall curve were 0.9967 and 0.9975, respectively. Additionally, case studies were implemented with leukemia, breast neoplasms and hsa-mir-21. Most of the top-25 predicted disease-related miRNAs (19/25 for leukemia; 20/25 for breast neoplasms) and 15 of top-25 predicted miRNA-related diseases were verified by literature and dbDEMC database. It is anticipated that Seq-SymRF could be regarded as a powerful high-throughput virtual screening tool for drug research and development. All source codes can be downloaded from https://github.com/LeeKamlong/Seq-SymRF .

CBL3 and CIPK18 are required for the function of NHX5 and NHX6 in mediating Li+ homeostasis in Arabidopsis.

Arabidopsis NHX5 and NHX6 are endosomal Na+,K+/H+ antiporters that function in mediating Na+, K+ and pH homeostasis. Here, we report that NHX5 and NHX6 mediate Li+ homeostasis in Arabidopsis. We found that the nhx5 nhx6 double mutant was defective in growth and had a high pale rate under Li+ stress; complementation with either NHX5 or NHX6 restored the growth of the double mutant under LiCl treatments. We further found that CBL3 and CIPK18 collaborate with NHX5 and NHX6 in controlling seedling growth. CBL3 and CIPK18 are involved in the NHX5- and NHX6-mediated response to Li+ stress but not to salt or low K+ stress. In addition, NHX5 and NHX6 coordinate NHX8, a plasma membrane antiporter, in mediating Li+ homeostasis. NHX8 may function differently from NHX5 and NHX6 in mediating Li+ homeostasis. NHX8 was not controlled by CBL3 and CIPK18. Overall, CBL3 and CIPK18 are required for the function of NHX5 and NHX6 in mediating Li+ homeostasis in Arabidopsis.

Microwave absorption medium-assisted extraction coupled with reversed-phase dispersive liquid-liquid microextraction of triazine herbicides in corn and soybean samples.

A new extraction method, microwave absorption medium-assisted extraction coupled with reversed-phase dispersive liquid-liquid microextraction, was developed for the determination of triazine herbicides in corn and soybean samples. Triazine herbicides were extracted with hexane and then directly enriched into the ionic liquid phase. The purification of sample and concentration of target analytes were performed simultaneously. The method combines the advantages of nonpolar solvent dynamic microwave extraction and reversed-phase dispersive liquid-liquid microextraction, which could greatly simplify the operation and reduce the whole pretreatment time. The Box-Behnken design was used to the optimization of experimental factors involved in the dynamic microwave-assisted extraction. In the present study, good linearity in the range of 5.00-500.00 µg/kg was obtained. The limits of detection and quantification varying from 1.3 to 4.2 and 4.1 to 13.9 µg/kg were achieved, respectively. The intra- and interday precisions were between 2.7 and 6.9%. The present method was applied to the analysis of corn and soybean samples, and the recoveries of analytes ranged from 80.7 to 106.9% with the relative standard deviations of 2.1-7.8%. The present method shows the potentials of practical applications in the treatment of the complex fatty solid samples.

Soil CO2 emissions from summer maize fields under deficit irrigation.

Irrigation practice is one of the main factors affecting soil carbon dioxide (CO2) emission from croplands and therefore on global warming. As a water-saving irrigation practice, the deficit irrigation has been widely used in summer maize fields and is expected to adapt to the shortage of water resources in Northwest China. In this study, we examined the impacts of deficit irrigation practices on soil CO2 emissions through a plot experiment with different irrigation regimes in a summer maize field in Northwest China. The irrigation regimes consisted of three irrigation treatments: deficit irrigation treatments (T1: reduce the irrigation amount by 20%, T2: reduce the irrigation amount by 40%) and full irrigation (T0) treatments. The results showed that the soil CO2 cumulative emissions with T1 and T2 were decreased by 9.8% (p < 0.05) and 14.3% (p < 0.05), respectively, compared with T0 treatment (1365.3 kg-C ha-1). However, there were no significant differences between T1 and T2 treatments (p > 0.05). Soil CO2 fluxes with different irrigation treatments showed significant correlations with soil moisture (p < 0.001) and soil temperature (p < 0.05). It was also observed that summer maize yields with T1 and T2 treatments were reduced by 4.9% (p > 0.05) and 30.9% (p < 0.05), compared with T0 (34.3 t ha-1), respectively. The findings demonstrate that the deficit irrigation treatment (T1) resulted in a considerable decrease in soil CO2 emissions without impacting the summer maize yields significantly. The results could be interpreted to develop better irrigation management practices aiming at reducing soil CO2 emissions, saving water, and ensuring crop yield in the summer maize fields in Northwest China.